PM 2.5 Carbon Measurements in EPA Region 10 Robert Kotchenruther, Ph.D. NW-AIRQUEST June, 2011.

Slides:



Advertisements
Similar presentations
Heather Simon, Adam Reff, Benjamin Wells, Neil Frank Office of Air Quality Planning and Standards, US EPA Ozone Trends Across the United States over a.
Advertisements

NASA AQAST 6th Biannual Meeting January 15-17, 2014 Heather Simon Changes in Spatial and Temporal Ozone Patterns Resulting from Emissions Reductions: Implications.
What are the important parameters that need to be defined for a carbonaceous aerosol analysis ? Hélène CACHIER Laboratoire des Sciences du Climat et de.
Source Apportionment of PM 2.5 in the Southeastern US Sangil Lee 1, Yongtao Hu 1, Michael Chang 2, Karsten Baumann 2, Armistead (Ted) Russell 1 1 School.
Chemical Composition of Organic Carbon Fractions Barbara Zielinska.
Carbon artifact adjustments for the IMPROVE and CSN speciated particulate networks Mark Green, Judith Chow, John Watson Desert Research Institute Ann Dillner.
EPA Precursor Gas Training Workshop PM 2.5 Chemical Speciation Network (CSN) Carbon Conversion Joann Rice.
STN/IMPROVE Comparison Study Preliminary Results Paul Solomon, ORD Tracy Klamser-Williams, ORIA Peter Egeghy, ORD Dennis Crumpler, OAQPS Joann Rice, OAQPS.
Overview of CSN Data Relevant to OC/EC Artifact Adjustments presented by James Flanagan RTI International Davis, CA January 22-23, 2008.
FIRE AND BIOFUEL CONTRIBUTIONS TO ANNUAL MEAN AEROSOL MASS CONCENTRATIONS IN THE UNITED STATES ROKJIN J. PARK, DANIEL J. JACOB, JENNIFER A. LOGAN AGU FALL.
Carbon Measurements and Adjustments Measurement of organics by IMPROVE & STN networks, Use of blank data to correct carbon concentration measurements,
U. Dusek 1, R. Holzinger 1, T. Röckmann 1 Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University, The Netherlands Combined measurements.
Use of National PM2.5 and Speciation Network Measurements for Model Evaluation For presentation at PM Model Performance Workshop February 10-11, 2004:
Nolwenn PERRON Göteborg, OC and EC separation for 14 C analyses N. Perron 1, L. Besnier 1, S. Szidat 2, A. S. H. Prévôt.
Source apportionment of Swiss carbonaceous aerosols using radiocarbon analyses of different fractions References: S. Szidat et al., 2007: Dominant impact.
Evaluation of Secondary Organic Aerosols in Atlanta
Air Quality Impacts from Prescribed Burning Karsten Baumann, PhD. Polly Gustafson.
Insights from Thermal Analysis of Individual Organic Compounds, Mixtures, Black Carbon Surrogates, Airborne PM and Extracts Lara Gundel, R.L. Dod, T.W.
Effects of Iron Oxides on the Determination of Organic and Elemental Carbon using Thermal Optical Techniques Kochy Fung AtmAA, Inc., Calabasas, CA, USA.
Uncertainties in Optical Charring Correction Schemes Jian Zhen Yu and Hong Yang Department of Chemistry Hong Kong University of Science & Technology EC/OC.
Fossil vs Contemporary Carbon at 12 Rural and Urban Sites in the United States Bret A. Schichtel (NPS) William C. Malm (NPS) Graham Bench (LLNL) Graham.
VII. How might current analysis methods be enhanced or combined to obtain more information about the nature of OC, EC, and other carbon fractions in filter.
1 Collocated STN-CSN and IMPROVE carbon measurements WHW, UCD 1/22/08.
Radiation’s Role in Anthropogenic Climate Change AOS 340.
Diurnal cycle of fossil and non-fossil total carbon using 14 C analyses during CalNex P. Zotter 1, A.S.H. Prévôt 1, Y. Zhang 2, S. Szidat 2, X. Zhang 3,
NATURAL AND TRANSBOUNDARY INFLUENCES ON PARTICULATE MATTER IN THE UNITED STATES: IMPLICATIONS FOR THE EPA REGIONAL HAZE RULE Rokjin J. Park ACCESS VII,
Comparison of the STN and IMPROVE Networks for Mass and Selected Chemical Components (Preliminary Results) Paul Solomon, ORD Tracy Klamser-Williams, ORIA.
State of the Science on Sources of Carbonaceous Aerosols and Their Contribution to Regional Haze John G. Watson Judith C. Chow Desert Research.
(Impacts are Felt on Scales from Local to Global) Aerosols Link Climate, Air Quality, and Health: Dirtier Air and a Dimmer Sun Emissions Impacts == 
MODELS3 – IMPROVE – PM/FRM: Comparison of Time-Averaged Concentrations R. B. Husar S. R. Falke 1 and B. S. Schichtel 2 Center for Air Pollution Impact.
25/05/20071 About comparability of measured and modeled metrics Jean-Philippe Putaud Fabrizia Cavalli DG JRC Institute for Environment and Sustainability.
Sensitivity of top-down correction of 2004 black carbon emissions inventory in the United States to rural-sites versus urban-sites observational networks.
Did the recession impact recent decreases in observed sulfate concentrations? Shao-Hang Chu, US EPA/OAQPS/AQAD October, 2011.
PM2.5 Model Performance Evaluation- Purpose and Goals PM Model Evaluation Workshop February 10, 2004 Chapel Hill, NC Brian Timin EPA/OAQPS.
Update on IMPROVE Light Extinction Equation and Natural Conditions Estimates Tom Moore, WRAP Technical Coordinator May 23, 2006.
CHEMICAL CHARACTERISTICS OF NORTH AMERICAN OUTFLOW: INSIGHTS FROM CHEBOGUE POINT, NOVA SCOTIA Allen Goldstein, Dylan Millet, James Allan, Eben Cross, Rupert.
The Use of Source Apportionment for Air Quality Management and Health Assessments Philip K. Hopke Clarkson University Center for Air Resources Engineering.
Online measurements of chemical composition and size distribution of submicron aerosol particles in east Baltic region Inga Rimšelytė Institute of Physics.
Public Meeting to Discuss “Weekend Effect” Research June 23, 1999.
Model Evaluation Comparing Model Output to Ambient Data Christian Seigneur AER San Ramon, California.
Regional Air Quality Modeling Results for Elemental and Organic Carbon John Vimont, National Park Service WRAP Fire, Carbon, and Dust Workshop Sacramento,
AoH/MF Meeting, San Diego, CA, Jan 25, 2006 WRAP 2002 Visibility Modeling: Summary of 2005 Modeling Results Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung.
Workshop on Air Quality Data Analysis and Interpretation Evaluation of Emission Inventory.
Office of Research and Development National Risk Management Research Laboratory, Air Pollution Prevention and Control Division Photo image area measures.
Correlations between DTT Activity and PM Constituents Wing Tuet April
Introduction Experimental Methods Conclusions Emissions of volatile organic compounds and particulate matter from small-scale peat fires I. George 1, R.
IMPROVE/STN Comparison & Implications for Visibility and PM2.5
Matteo Reggente Giulia Ruggeri Satoshi Takahama
Ann M. Dillner, Mark C. Green
Matteo Reggente Giulia Ruggeri Gözde Ergin Christophe Delval
Brian Timin- EPA/OAQPS
Matteo Reggente Giulia Ruggeri Adele Kuzmiakova Satoshi Takahama
Sunil Kumar TAC, COG July 9, 2007
Sources of the PM10 aerosol in Flanders, Belgium, and re-evaluation of the contribution from wood burning Willy Maenhaut1,2, Reinhilde Vermeylen2, Magda.
Introduction to Experiment 4: Clouds
COPERT 4 Training 6. Exhaust and non-exhaust PM
Aerosol chemistry studies at the SMEARIII station in Kumpula
Svetlana Tsyro, David Simpson, Leonor Tarrason
PMcoarse , Monitoring Budgets, and AQI
with EUSAAR NA2 Partners
A Review of Time Integrated PM2.5 Monitoring Data in the United States
About comparability of measured and modeled metrics
Rationalizing the differences between thermo-optical OC/EC methods.
TFMM PM Assessment Report
U.S. Perspective on Particulate Matter and Ozone
Title Why do we underestimate Elemental Carbon in PM?
Jean-Philippe Putaud, Fabrizia Cavalli
Updating a Fuel-based Inventory of Vehicle Emissions
EPA FY2008 Air Monitoring Budget Guidance
Svetlana Tsyro, David Simpson, Leonor Tarrason
Presentation transcript:

PM 2.5 Carbon Measurements in EPA Region 10 Robert Kotchenruther, Ph.D. NW-AIRQUEST June, 2011

Motivation for this presentation: EPA changed the urban (STN network) PM 2.5 carbon sampling and analysis protocols to bring them in line with those used by the IMPROVE monitoring network. After switching to the new sample and analysis protocols, we found a significant decrease in the observed % total carbon (TC=OC+EC) – 2010 Data for PM2.5 Samples > 25 ug/m3 about 10% drop in TC at each site Is this an issue with the measurement method or caused by an emissions change? This presentation will show why we think it is primarily a measurement issue, and give some recommendations on how to deal with it.

More motivation for this presentation: We also observed an increase in %EC at each site after switch 2006 – 2010 Data for PM2.5 Samples > 25 ug/m3 %EC increase at each site and a decrease in %OC at each site 2006 – 2010 Data for PM2.5 Samples > 25 ug/m3 %OC decrease at each site

To get to the bottom of this – need to review some background on how OC & EC are defined: OC and EC are ‘operationally’ defined based on a thermal and optical measurement protocol rather than objectively defined. OC = the portion of total carbon that is not light absorbing and can be thermally volatilized in a non-oxidizing atmosphere EC = the portion of total carbon that is light absorbing and can be thermally volatilized (combusted) in an oxidizing atmosphere Idealized thermogram Idealized thermogram features: Temperature is ramped in stages under inert (He) atmosphere. Volatilized carbon is measured in each stage and total of the stages is = OC Temperature is further ramped in stages under oxidizing (He + 2% O 2 ) atmosphere. Volatilized carbon is measured in each stage and total of stages is = EC That’s the ideal... BUT... in reality some thermally unstable OC can pyrolyse in the He-mode to form EC. (He)(He + O 2 )

Correcting for OC pyrolysis Thermogram – IMPROVE (TOR) method OP (OC pyrolysis) is measured in one of two ways, looking at light reflectance off the quartz filter or looking at light transmittance through the filter. Reflectance method is called TOR (Thermal optical reflectance). Transmittance method is called TOT (Thermal optical transmittance) OC pyrolysis is observed when reflectance or transmittance decreases in the non-oxidizing atmosphere from its starting point. Pyrolyzed OC (OP) is quantified when reflectance or transmittance reaches its initial level after O 2 is added. Final OC and EC determined as: OC = OC1+OC2+OC3+OC4+OP EC = EC1+EC2+EC3-OP

To understand what’s causing the shift in TC, EC, and OC in the change in STN measurement method, we also need to know what biases the measurements are subject to. Mass can be added to the quartz filter by gaseous VOC adsorbing onto the filter -> Positive bias (adds mass to OC & TC) -> influenced by monitor flow rate Mass can be removed from the filter by particle OC volatilization -> Negative bias (loses mass from OC & TC) -> influenced by flow rate, pressure drop across filter Mass can be shifted between OC and EC based on the OP method used-> OP (TOT method) > OP (TOR method) [usually] -> TOT shifts more mass to OC, from EC (but same TC) -> possibly due to adsorbed VOC and SVOC charring in filter interior. Also, OC and EC can be more uncertain under high filter loading -> TOT and TOR more uncertain for highly loaded filters -> Highly loaded filters may have a starting transmittance and reflectance near zero (if OP is difference of two small numbers, more uncertain)

When the STN method changed, what was different? New STN was set to mimic the IMPROVE method, so  OC temperatures were lowered in new STN  TOR replaced TOT as default OP method in new STN  EC temperatures are a little lowered in new STN  New STN has a smaller filter size, higher flow rates, higher face velocity How are these changes expected to effect biases? Method Comparison

OC and EC measurements... expected effect on biases due to STN shift ChangeHigher flow rate and face vel. for new STN Expected biases - Less VOC adsorption (smaller positive bias to OC, TC) -More OC volatilization (larger negative bias to OC, TC) Both effects expected to cause lower %TC and %OC

OC and EC measurements... expected effect on biases due to STN shift ChangeTOR now default OP method Expected biases - we know that TOT is usually > TOR for same temp - but also Chow et al. (2004) have found that TOT method is sensitive to temperature, with higher temps causing higher OP(TOT) - Hence, both lower OC temps and shift to TOR will result in a significant shift in OC/EC ratio in favor of EC (but same TC)

OC and EC measurements... expected effect on biases due to STN shift ChangeSmaller filter and higher flow rate Expected biases - New STN filters will have higher loadings -TOR and TOT reflectance and transmittance will start more saturated, therefore less sensitive to quantifying OP char. - TOT and TOR will have higher uncertainty Shift makes OC/EC uncertainty higher for high concentration samples

Inter-comparison studies: Others have studied this issue and developed correlations of OC and EC between methods (by analysis of co-located monitors) and corroborated our observed decrease in %TC & %OC and increase in %EC. Rattigan et al. 2011, Atmospheric Environment 45 (2011) Compared co-located data at an urban and rural location in New York state. EPA Speciation Network Newletter, ( Compared co-located data in Birmingham, Alabama. Schichtel et al., IMPROVE Carbon meeting. Compared co-located data between from all co- located sites. We are not the only ones to discover this:

So in summary: about 10% drop in TC at each site  The decrease in %TC and %OC after the change in STN carbon method is mainly caused by increased OC volatilization from the quartz filter with higher flow rates and face velocities.  The increase in %EC after the change in STN carbon method is mainly caused by changing OP methods to TOR (from TOT) and lower temperatures in the OC temperature protocol.

Recommendations: For PM2.5 attainment demonstrations, should we use older STN carbon method or new? Benefits of old STN carbon method: Less negative bias from OC volatilization In some cases, may be the majority of data you have (e.g., Fairbanks) Benefits of new STN carbon method: TOR may have less bias/uncertainty than TOT May be the majority of data you have All future measurements will use new carbon method as default The recommendation here is to collaborate with EPA to determine what is the best choice for each nonattainment area.

Recommendations: TOR vs. TOT OP measurement method Benefits of TOR: TOR is insensitive to changes in OC temp protocols TOR is possibly less susceptible to saturation at high loadings TOR has less bias from adsorbed VOC char TOR is also used in IMPROVE network, so comparable. Benefits of TOT: Several recent papers from Georgia Tech. (Cheng et al., 2011a; Cheng et al., 2011b) suggest TOT method gives a better quantification of OC from biomass burning Reasons for recommending TOR: May be less susceptible to bias and uncertainty than TOT Is EPA default and directly comparable to IMPROVE Not enough evidence (1 research group) behind TOT/biomass burning link

References: Cheng et al. 2011a, Atmospheric Environment 45 (2011) Cheng et al. 2011b, Atmospheric Environment 45 (2011) Chow et al. 2001, Aerosol Science and Technology 34: 23–34 (2001). Chow et al. 2004, Environ. Sci. Technol. 2004, 38, Chow et al. 2010, Atmos. Chem. Phys., 10, 5223–5239, Rattigan et al. 2011, Atmospheric Environment 45 (2011) Schauer et al. 2003, Environ. Sci. Technol. 2003, 37, Thank you!