Carbon artifact adjustments for the IMPROVE and CSN speciated particulate networks Mark Green, Judith Chow, John Watson Desert Research Institute Ann Dillner University of California at Davis Neil Frank and Joann Rice USEPA Office of Air Quality Planning and Standards National Air Quality Conference, San Diego, CA March 2011

Introduction Organic aerosol is a major contributor to PM 2.5 concentration, typically accounting for 25-50% of reconstructed fine mass Organic aerosol % of fine mass ( )

Seasonal OC Concentrations WinterSpring Summer Autumn

CSN network map (continental US) IMPROVE network map (Continental US)

Statement of the problem Analysis of filter samples for carbon analysis done by thermal optical reflectance (TOR) for both IMPROVE and CSN networks TOR heats filter in stages without oxygen and organic carbon volatilized and converted to CO2 Finally, 2% oxygen is added to combust elemental carbon Because high temperatures are needed for TOR analysis, Teflon filter cannot be used Quartz fiber filters are used to collect the aerosol for analysis Quartz fiber filters are known to react with organic gases causing sampling artifacts Positive artifact from adsorption of organic gases Negative artifact from volatilization of particles off filter (e.g. as temperature increases during the day or after sample is collected).

Organic Sampling Artifacts Positive sampling artifact: gas-phase adsorption onto quartz Negative sampling artifact: SVOC is volatilized “after” captured by filters Quartz- or other filter material Backup fiber Particle (P) Particle and gas are in a dynamic equilibrium! Gas Molecule CIG: Charcoal-impregnated glass-fiber filter

Treatment of artifact Typically, positive artifact thought to be greater than negative artifact SEARCH network uses denuders to remove organic gases upstream of filter- use a backup filter to capture gases volatilizing off front filter- negative artifact However denuder approach add complexity and expense and also alters gas-particle equilibrium IMPROVE network has used backup filters at a few sites to characterize positive artifact IMPROVE has subtracted monthly median backup filter OC concentrations at 6 sites to give a monthly “correction” to apply to all sites CSN network is currently collecting back filters at all sites without denuders but has not determined how to use them for artifact correction Desire a method consistent between networks and that can give continuity in time over >20 years of IMPROVE data

Average front filter (QF), backup filter (QBQ), and field blank OC concentrations (Top 1% QF excluded) IMPROVE QBQ= 21% of QF, bQF=11.8% of QF CSN QBQ= 15.8% of QF, bQF=6.7% of QF

Some possible methods for artifact adjustment 1)Ignore potential positive and negative artifacts 2)Subtract representative OC concentration on field blank 3)Subtract representative OC concentration on backup filter 4)Use denuder to prevent positive artifact and ignore any negative artifact 5)Use denuder and backup filter to characterize negative artifact and field blanks for positive artifact 6)Same as above, except without denuder 7)Add quartz filter behind Teflon and subtract (recommended by McDow & Huntzicker (1990) 1 Only 1-3, 6 can be done with existing sampling set-ups/existing data 1 Atmos. Environ., 24A,

Data analysis methods Use only sample site days with front filter (QF), blank filter (bQF), and backup (QBQ) available Look at relationships between QF and bQF and QBQ and if they vary by geographic location or season to see if regional or seasonal artifact adjustments are called for Consider IMPROVE and CSN data separately and then together (may expect differences because CSN mainly urban, IMPROVE mainly rural Used 1839 CSN samples Used 1387 IMPROVE samples Sep Feb 2010 Removed QBQ OC>1.2*QF OC

CSN Average bQF and QBQ OC by site show little geographic pattern (sites ordered from EPA Region 1 (left) to EPA Region 10 (right)

IMPROVE – bQF and especially QBQ show seasonal pattern (higher in summer) CSN shows subtle seasonal patterns in bQF and QBQ

Backup filter OC (QBQ) proportional to front filter OC (QF). Logarithmic or power law fits work about equally well. QBQ scales approximately with square root of QF. IMPROVE better fit than CSN. Estimating backup filter OC from front filter OC

Combined data set power law fit slightly better than logarithmic fit. Power law fit equations for CSN, IMPROVE, and combined similar- suggests little adverse impact from using combined equation for all data

Averaging the data clarifies the front/back filter OC relationship

How to proceed? Use of curve fitting to estimate QBQ from QF gives less error than using median QBQ – so no good reason to continue using median QBQ for IMPROVE adjustment or to apply to CSN data BUT- What does the QBQ OC really represent? Alternate explanation – back filter collects positive and negative artifact – fit linear curve to QBQ vs QF – slope represents negative artifact proportional to QF concnetration – intercept represents positive artifact – QF OC adjusted = QF + slope*QF - intercept – Blank filters represent a positive artifact independent of concentration – QF OC adjusted= QF+slope*QBQ-2*bQF (method gives small negative average adjustment for IMPROVE, small positive for CSN) – This is equivalent to SEARCH approach, except no denuder is present to remove organic vapors

Linear model for QBQ based on QF (highest 1% of data excluded from figure and fit) OCback =.08*OCfront+0.13 Intercept about equal to field blank- CSN linear model gives intercept of 0.23 compared to bQF of 0.16 Linear model less satisfactory than power or ln for CSN

Summary Method of artifact “correction” can affect OC concentrations up to 20% or so Don’t currently have enough information to determine most appropriate correction Backup filter OC concentration best represented by fit proportional to concentration BUT We don’t really know what backup filter represents Use of field blanks (may adjust seasonally, monthly, etc.) straightforward and consistent with artifact corrections for non- reactive compounds Want consistent methodology among CSN and IMPROVE networks for comparability and ability to calculate urban/rural differences, etc. Conference call later this month to try to discuss artifact correction approach for both networks