Thermodynamic characterization of Mexico City Aerosol during MILAGRO 2006Thermodynamic characterization of Mexico City Aerosol during MILAGRO 2006 Christos Fountoukis1, Amy Sullivan2,7, Rodney Weber2, Timothy VanReken3,8, Marc Fischer4, Edith Matías5, Mireya Moya5,

Delphine Farmer6, Ronald Cohen6 and Athanasios Nenes1,2

1School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 2School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA.3National Center for Atmospheric Research, Boulder, CO 4Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA.

5Centro de Ciencias de la Atmosfera, Universidad Nacional Autonoma de Mexico, Mexico City, México 6Department of Chemistry, University of California Berkeley, Berkeley, CA.7Now at Department of Atmospheric Science, Colorado State University, CO 8Now at Laboratory for Atmospheric Research, Department of Civil & Environmental Engineering, Washington State University, Pullman, Washington.

AcknowledgmentsAcknowledgmentsNOAA contract NMRAC000-5-04017, EPA contract X83234201, NSF ATM-0513035, NCAR Advanced Study Program, and NSF ATM-0511829.

IntroductionIntroduction• At the heart of every air quality simulation is a module for computing the equilibrium composition of aerosol.

• Understanding the prediction uncertainty from assumptions on phase state and composition is required for effective PM simulations.

Observational DataObservational Data• We use fast measurements of aerosol and gas-phase

constituents sampled at the T1 site during the MILAGRO 2006 campaign.

• Particle Into Liquid Sampler (PILS) (Orsini et al., 2003), for PM2.5 ion concentrations.

• Quantum-cascade laser (QCL) (Fischer et al., 2007), for NH3(g)

• Thermal dissociation-laser induced fluorescence (TD-LIF, Farmer et al., 2006; Day et al., 2002), for volatile nitrate (i.e. HNO3(g) + NH4NO3).

• Ambient temperature (T), pressure and relative humidity (RH).

• Aerosol particles (PM2.5) were also collected with a cascade micro-orifice uniform deposit impactor (MOUDI), MSP Model 100 (Marple et al., 1991).

Preferred Aerosol Phase StatePreferred Aerosol Phase State

-2

-1

0

1

2

3

4

5

0 10 20 30 40 50 60 70 80 90 100

Stable

Metastable

(a)

Relative Humidity, %

Pre

dict

ed –

Obs

erve

d NH4(p)

-8

-6

-4

-2

0

2

4

6

8

0 10 20 30 40 50 60 70 80 90 100

Stable

Metastable

Relative Humidity, %

Pre

dict

ed –

Obs

erve

d NO3(p)

(b)

-2

-1

0

1

2

3

4

5

0 10 20 30 40 50 60 70 80 90 100

Stable

Metastable

(a)

Relative Humidity, %

Pre

dict

ed –

Obs

erve

d NH4(p)

-2

-1

0

1

2

3

4

5

0 10 20 30 40 50 60 70 80 90 100

Stable

Metastable

(a)

Relative Humidity, %

Pre

dict

ed –

Obs

erve

d NH4(p)

-8

-6

-4

-2

0

2

4

6

8

0 10 20 30 40 50 60 70 80 90 100

Stable

Metastable

Relative Humidity, %

Pre

dict

ed –

Obs

erve

d NO3(p)

(b)

-8

-6

-4

-2

0

2

4

6

8

0 10 20 30 40 50 60 70 80 90 100

Stable

Metastable

Relative Humidity, %

Pre

dict

ed –

Obs

erve

d NO3(p)

(b)

For Mexico City aerosol, assess the:

• Ability of the ISORROPIA-II thermodynamic model (Fountoukis and Nenes, 2007) to predict aerosol composition.

• Timescale for achieving equilibrium.

• Importance of explicitly including crustal species in the thermodynamics.

ObjectivesObjectives

• Preferred phase state, either “stable” (solids precipitate out of solution upon saturation), or, “metastable” (aerosol is an aqueous phase regardless of saturation state).

ISORROPIA-II vs. ObservationsISORROPIA-II vs. Observations

y = 0.466x + 0.534

R2 = 0.209

0

2

4

6

8

10

0 2 4 6 8 10

CF=0

CF=1

CF=2

CF=3

y = 0.973x - 0.020

R2 = 0.986

0

1

2

3

4

0 1 2 3 4

CF=0CF=1CF=2CF=3

y = 0.991x - 0.676

R2 = 0.992

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

CF=0CF=1CF=2CF=3

y = 1.007x + 0.816

R2 = 0.618

0

3

6

9

0 3 6 9

CF=0CF=1CF=2CF=3

y = 0.933x + 0.789

R2 = 0.742

0

5

10

15

20

25

0 5 10 15 20 25

CF=0CF=1CF=2CF=3

Pre

dic

ted

Observed

NH3(g)(a)

Observed

NH4(p)

Pre

dic

ted

(b)

Observed

HNO3(g)

Pre

dic

ted

(c)

Observed

NO3(p)

Pre

dic

ted

(d)

Observed

Cl(p)

Pre

dic

ted

(e)

y = 0.466x + 0.534

R2 = 0.209

0

2

4

6

8

10

0 2 4 6 8 10

CF=0

CF=1

CF=2

CF=3

y = 0.973x - 0.020

R2 = 0.986

0

1

2

3

4

0 1 2 3 4

CF=0CF=1CF=2CF=3

y = 0.991x - 0.676

R2 = 0.992

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

CF=0CF=1CF=2CF=3

y = 1.007x + 0.816

R2 = 0.618

0

3

6

9

0 3 6 9

CF=0CF=1CF=2CF=3

y = 0.933x + 0.789

R2 = 0.742

0

5

10

15

20

25

0 5 10 15 20 25

CF=0CF=1CF=2CF=3

Pre

dic

ted

Observed

NH3(g)(a)

Observed

NH4(p)

Pre

dic

ted

(b)

Observed

HNO3(g)

Pre

dic

ted

(c)

Observed

NO3(p)

Pre

dic

ted

(d)

Observed

Cl(p)

Pre

dic

ted

(e)

The data is classified into 4 “completeness factor” (CF) categories: CF=0 (51% of the data) corresponds to 5-min average measurements of all (gas + particulate phase) species. CF=1 (26% of the data) corresponds to 20-min average measurement of all (gas + particulate phase) species. CF=2 (13% of the data) corresponds to 5-min average measurements, with the PILS nitrate being larger than the TD-LIF HNO3(g) + NH4NO3. CF=3 (10% of the data) corresponds to 20-min average

measurements, with the PILS nitrate being larger than the TD-LIF HNO3(g) + NH4NO3

0

2

4

6

8

10

12

14

16

18

1:28AM

1:38AM

8:28AM

9:58AM

11:48AM

1:28PM

3:18PM

3:28PM

4:58PM

6:52PM

8:22PM

Time (03/27/2006)

Co

nc

en

tra

tio

n (

μg

m-3

)

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Fra

cti

on

al R

ela

tiv

e H

um

idit

y

NitrateAmmoniumRH

Diurnal profile of measured nitrate, ammonium and ambient RH for 27 March 2006.

Good agreement between modeling and predictions.Large excess of NH3(g) drives most Cl, NO3 into the aerosol

phase, so:

• Small errors in particulate nitrate are magnified in the gas phase.

• HNO3(g) exhibits large scatter (Mean Normalized Error ~ 80%), which is however less than the estimated uncertainty (~ 100%).

• Predicted concentrations of gas phase HCl are low (0-0.3 μg m-3).

• For RH (<50%) and [SO4]/[NO3] < 1, NME and NMB for NO3(p) were significantly larger when using the metastable solution. The opposite was seen when [SO4]/[NO3] > 1.

• This suggests that the “stable” state (solids precipitate out of solution upon saturation) is preferred when [SO4]/[NO3] < 1 and vice versa.

Averaging time Error metric NH3(g) NH4(p) HNO3(g) NO3(p) Cl(p)

5min (CF=0) NME (%) 7.16 52.30 71.72 33.87 17.56

NMB (%) -6.73 49.16 -45.49 21.49 -17.56

20min (CF=1) NME (%) 4.42 41.14 63.06 30.25 13.02 NMB (%) -3.26 30.38 -6.83 3.27 -13.02

35min (CF=0) NME (%) 6.68 49.48 64.15 30.54 19.58 NMB (%) -6.60 48.89 -51.17 24.36 -19.58

Equilibration TimescaleEquilibration Timescale

• Mean Normalized Error (MNE) and Mean Normalized Bias (MNB) do not depend on the CF factor, but only on the averaging timescale.

• The MNB becomes minimum at ~ 20 min, and suggests this is the equilibration timescale.

Property Treatment of crustals NH4(p) NO3(p) H2O(liq)

Mean Observed (μg m-3) 2.24 5.37 -

Insoluble 3.18 5.47 13.23 Equivalent Na 2.77 5.61 13.09 Mean Predicted (μg m-3)

ISORROPIA-II 2.55 5.86 11.67

Insoluble 46.76 (41.53) 31.03 (1.87) N/A Equivalent Na 34.3 (23.3) 28.7 (4.44) N/A NME (NMB), (%)

ISORROPIA-II 34.04 (13.6) 26.2 (9.2) N/A

Three treatments of crustals (Mg, Ca, K) are considered:

• Explicitly in the ISORROPIA-II thermodynamic calculations

• Treating crustal species as “equivalent sodium” (i.e., by adding [Na]=[K]+2[Ca]+2[Mg] to the input data)

• Treating crustals as insoluble.

Importance of explicit crustal treatment Importance of explicit crustal treatment

Mean prediction error and bias for all 3 crustal treatments.

• Treating crustals as insoluble gives the largest prediction errors and biases.

• The water uptake is not significantly affected by the crustal treatment assumption; full thermodynamics tend to give the lowest water uptake values.

• Equivalent sodium differs from the full thermodynamic treatment; the latter tends to give smaller mean errors. This has important implications for the treatment of dust in large-scale models.