Application of Combined Mathematical and Meteorological Receptor Models (UNMIX & Residence Time Analysis) to 1991-99 IMPROVE Aerosol Data from Brigantine NWR, NJ (R.Poirot & P. Wishinski, VT DEC)

(also see upcoming Comparative Positive Matrix Factorization Analysis by Jong Hoon Lee & Barbara Turpin at Rutgers University)

The general Approach involves applying Mathmatical Model to Speciated Aerosol Data, then applying Ensemble Trajectory Techniques to evaluate “sources”

(See: Sources of Fine Particle Concentration and Composition in Northern Vermont, posted at: http://capita.wustl.edu/neardat/reports/ TechnicalReports/ReceptorModels/ awmatoxRP.pdf)

“Traditional” UNMIX Approach for Apportioning Measured Fine Mass (employed at Underhill, VT)

• Include Measured Mass as Model Input and

• Specify It as the “Total” and “Normalization” Variable

• Compositions & Contributions expressed as Mass Fractions

“Non-Traditional” Approach for Apportioning Reconstructed Mass Species (employed at Brigantine, NJ)

• Exclude Measured Mass as Model Input and

• Construct Composite Variables for: Sulfates, Nitrates, Carbonaceous Matter (EC + 1.4 OC) and Crustal Material

• 4 Separate UNMIX Runs, 1 for each Major Mass Species

• Initial Results expressed as fractions of Major Species

Brigantine: 4 Separate UNMIX Runs, 1 Each to Explain Mass of: Sulfates (1.375xSO4), Carbonaceous Matter (EC+1.4xOC)

Nitrates (1.29 x NO3) & Crustal Material (4 x Si)

22 Sources, expressed as mass fractions of 4 major species

Half the Initially identified 22 sources were highly correlated (same source was identified as contributor to several major species)

Combining these “Redundant Sources” reduced # from 22 to 11

“Redundant Sources” had highly correlated daily mass contributions and also had similar compositions for common trace elements

Sum of daily source contributions accounts for 90% of measured mass and “perfectly” explains 100% of the reconstructed mass

Average Monthly Source Contributions at Brigantine NJ, 1991-99

Fine Mass Fractional Source Contributions at Brigantine NJ, 1991-99 on Worst 20 % (20 ug/m3) Average (11 ug/m3) and Best 20 % (5 ug/m3) PM-2.5 Days

Multi-year Changes in Source fine Mass Contributions (Note most of the improvement results from pre-1996 reductions in Summer Sulfate)

Thermally Stratified Carbon Fractions Show Different Distributions

Four of the sources show day of week differences:

Zn & Oil > on Wednesdays,

Woodsmoke & “Lite Carbon” are > on Saturdays.

Of these 4 Sources:

Zinc > Wed all year round,

Oil > Wed only in Winter,

Woodsmoke > Sat only in Winter

Lite Carb > Sat only in Summer

Wind Roses based on hourly surface met from Atlantic City, for every sample day, and then constrained to days when each source was ‘high’ (top 10% days).

Met data & “Rose Works” software kindly provided by Steve Mauch, at UAI Environmental.

Based on ATAD Trajectories kindly provided by Kristie Gebhart, NPS

Occasional Soil Dust Spikes hit Brigantine a few times every Summer (July) and Always correspond with (much) Higher soil spikes to the Southeast...

Time Series of Soil Dust Impacts at Brigantine

No relationship between Al:Ca Ratio and Fine Soil Mass at Brigantine, until Al/Ca > 3.8.

An Al/Ca Ratio of > 3.8 and a fine soil concentration of > 3 ug/m3 have been identified as Indicator of Sahara Dust.

For Example in Gebhart, Kreideneweis & Malm analysis of Big Bend, TX aerosol, currently in press in: The Science of the Total Environment. (draft, do not cite, etc.)

Sea Salt (normalized to Na) derived in 7 Separate UNMIX Runs, with 7 Different Subsets of Input Variables and Observations, with Nearly Identical Compositions (for common Input Variables) and

Nearly Identical Contributions (for common Input Observations)

(Chloride Not Included because its mass fraction’s Too Variable)

The 5 Extra “Sea Salt” Runs also Yielded 5 extra “Soil” sources.

These agree almost Perfectly with the original “Soil” source derived from “Crustal” (Si) Run,

Both in terms of their daily mass contributions and fractional mass compositions, for common observations and common input variables.

So: these Soil & Sea Salt sources may be “wrong”, but they are robustly identified in multiple runs with multiple combos of input variables and observations.

Sea Salt & Windblown Dust are both “contaminated” by NO3 & SO4 (How would we calculate Extinction Efficiencies for these Mixtures?)

…and who do we Blame for the associated Visibility effects?