Using Aethalometer Data to Examine Ambient Particulate Matter Sources: Fairbanks, AK Jay Turner Washington University in St. Louis June 15, 2010 Photo Credit: FNSB

Culpability - Challenges Exceedance days characteristics – Extreme stagnation – Virtually all species co-vary Chemical Transport Models – Struggle with extreme stagnation Receptor Models – Struggle when many source contributions covary Data driven approach – Ideally link back to emissions activities which may or may not be known

Focus of Today’s Talk Preliminary analysis of Aethalometer data – Interpret concentration patterns using known or perceived activity patterns – Contribute to weight-of-evidence for culpability determination Can Aethalometer data inform the apportionment of PM 2.5 mass? – Guide future work Monitoring objectives (what, when, how) Refined source emissions profiles and emissions inventories

Conceptual Model Many emission sources – Primary PM – Condensable PM at very low temperatures Likely dominant sources in winter – Space heating Fuel oil Wood burning Possibly others (e.g. waste oil) – Motor vehicle exhaust

Peger Road, Nov Feb 2010 Wintertime measurements – PM 2.5 Chemical Speciation Network (CSN) sampler 1-in-3 day, 24-hour average samples – PM 2.5 mass (gravimetric, Teflon filter) – PM 2.5 species (EC/OC, major ions, XRF elements) – Magee Scientific Aethalometer Two wavelengths (880 and 270 nm) Black carbon (BC) and UV-absorbing carbon (UVC) Continuous measurements, 5-minute time resolution other sites, including hourly criteria gases and PM 2.5 mass (BAM)

Continuously deposit aerosol onto a quartz fiber tape (spot deposit), typically 1-6 LPM Measure change in light transmission over a user-defined interval, typically 5 minutes –User assigns an effective mass absorption efficiency to obtain a mass concentration for the absorbing material Instruments with different wavelengths –1-channel (880 nm) –2-channel (370 and 880 nm) –7-channel (***) 880 nm is the standard channel for black carbon (BC) –Presumably little interference from other absorbing materials Magee Scientific Aethalometer

Black Carbon vs. Elemental Carbon Black carbon (BC) as a surrogate for elemental carbon (EC) EC ~10% of wintertime PM 2.5 mass BC-EC relationship ~1:1 (albeit noisy, especially at high concentrations)

Black Carbon vs. PM 2.5 Mass BC is a good surrogate for PM 2.5 mass (strongly co-vary) What is the utility of the BC data? What added value does it have?

Wavelength Dependence of Absorption Relationship between wavelength and Aethalometer absorption:  ’ depends on aerosol chemical and physical properties Fossil fuel combustion  atn ~ -1 (i.e.  ’ ~ 1) Biomass/biofuel burning, mineral dust,  atn ~ -2 (i.e.  ’ ~ 2) (Kirchstetter et al. 2004) wavelength, (nm) Bakersfield, CA 0000 PST Aethalometer absorption coefficient,  atn (m -1 )

Black Carbon Apportionment Two-source model - assume BC is a mixture of contributions from: – fossil fuel combustion (BC f ) with  ’=1.0 – biomass burning (BC b ) with  ’=1.8 For 2-channel Aethalometer with = 370 nm (UVC) and 880 nm (BC), and the above  ’ values: BC b = UVC – BC BC f = BC – BC b

Apportioned Daily-Average BC To some extent the signals behave differently What is our confidence in the apportionment?

Day of Week Fossil BC Daily concentration scaled using the centered 7-day average Fossil BC highest on weekdays and lowest on Sunday Compare to Fairbanks-wide traffic volume data…

Day of Week Fossil BC and Traffic Volume Green circles: relative change in Fairbanks-wide average daily traffic (ADT) – all vehicles Fossil BC tracks ADT quite well Should refine by estimating diesel vehicle ADT patterns

Day of Week Biomass BC Daily concentration scaled using the centered 7-day average Biomass BC higher on weekends compared to weekdays Need additional wood-burning activity data to evaluate

Broad within-hour distributions due to aggregating of clean days and dirty days Scale to treat each day equally… Diurnal Fossil BC (Absolute Concentration)

Hourly concentration scaled using the centered 23-hour average Fossil BC highest during daytime hours Compare to Fairbanks-wide traffic volume data… Diurnal Fossil BC (Scaled Concentration)

Green circles: relative change in Fairbanks-wide average hourly traffic– all vehicles Fossil BC tracks traffic volume quite well Should refine by estimating diesel vehicle patterns Diurnal Fossil BC and Traffic Volume

Broad within-hour distributions due to aggregating of clean days and dirty days Scale to treat each day equally… Diurnal Biomass BC (Absolute Concentration)

Hourly concentration scaled using the centered 23-hour average Biomass BC highest evenings and nighttime Compare to wood-burning activity data… Diurnal Biomass BC (Scaled Concentration)

Diurnal Biomass BC and Wood Burning Green circles: scaled wood energy use (two wood-only households) Biomass BC tracks wood energy use moderately well Should refine by increasing the wood energy use activity data set Replace with wood profile from homes with both wood stoves and oil heaters; it does look different.

Non-soil potassium often considered a tracer for wood smoke Stronger correlation between potassium and Biomass BC than Total BC Biomass BC and Potassium Ion Total BCBiomass BC

Evaluation of BC Apportionment Fossil BC patterns – Weekday/weekend consistent with traffic volume – Diurnal profile consistent with traffic volume – Need refined data for gasoline versus diesel activity Biomass BC patterns – Strongly correlated with potassium ion – Diurnal profile consistent with wood energy use – Weekday/weekend pattern seems plausible – Need more comprehensive space heating inventory diurnal, day of week various ambient temperature regimes

Apportioning PM 2.5 Mass Using BC Fossil (BC f ) and Biomass (BC b ) apportionments seem reasonable Multivariable regression of PM 2.5 mass on BC f and BC b – Motor vehicles… BC f – Space heating with wood… BC b – Space heating with fuel oil… (?) A fossil fuel but likely very low BC, emissions likely dominated by sulfur

[PM 2.5 Mass] = 5.4[BC f ] + 20[BC b ] Two-source model can explain observed gravimetric mass values Are the regression coefficients reasonable? Multivariable Regression of PM 2.5 Mass on BC Components

Apportioning PM 2.5 Mass Using BC Fossil PM 2.5 Mass = 5.4  Fossil BC – Thus, source PM 2.5 mass is ~18% “EC” – Plausible for motor vehicle fleet, depending on gas/diesel split Biomass PM 2.5 Mass = 20  Biomass BC – Thus, source PM 2.5 mass is ~5% “EC” – Reasonable for both wood burning but also fuel oil – Confounding by fuel oil used for space heating? Activity might track wood use to some extent Expect elevated sulfate compared to wood smoke…

Sulfur and BC Components Sulfur correlates much better with biomass BC than fossil BC Perhaps Biomass PM 2.5 represents “Space Heating” PM 2.5 Sulfur vs. Fossil BCSulfur vs. Biomass BC

“Space Heating” vs. “Motor Vehicles” “Space Heating” ~70% of PM 2.5 on exceedance days – weekday/weekend dependence…

Summary BC analysis shows promise as a PM 2.5 mass apportionment tool for Fairbanks – Especially in weight of evidence context – Covarying source contributions still cloud the interpretation – Focus on changes in response to environmental conditions (e.g. temperature) and emissions activities Refinements – Multivariate analysis include day of week (categorical variable), temperature, etc.

Future Work Aethalometer and speciation data available for a second site – Preliminary analysis shows similar trends Chemical analyses of PM 2.5 samples – Levoglucosan (wood smoke), nickel (heating oil) Need refined emission inventories – Motor vehicles, wood burning, fuel oil combustion – Activity patterns – Emission factors Monitoring objectives and plans for next winter