Carbonaceous Matter in Air Quality Model Applications Gopal Sistla, - - PowerPoint PPT Presentation

Carbonaceous Matter in Air Quality Model Applications

Gopal Sistla, Prakash Doraiswamy* , Kevin Civerolo, Christian Hogrefe and Winston Hao

Bureau of Air Quality Analysis and Research Division of Air Resources New York State Department of Environmental Conservation Albany, NY 12233

* On assignment from Atmospheric Sciences Research Center, University at Albany, Albany NY 12222

PM2.5 NAAQS

‡ National Ambient Air Quality Standards for PM2.5

promulgated in 1997

„ 24-hr: 65 µg/ m 3 „ Annual: 15 µg/ m 3 „ SIP due April 2008 for 10 county region of NY as part of

NYCMSA

‡ September 2006 Revisions

„ 24-hr: 35 µg/ m 3 „ Annual: 15 µg/ m 3 „ State recommendations by December 2007 and EPA

designation before December 2008

‡ Under the revised NAAQS, there is a potential for

some of the urban counties in New York to exceed the new 24-hr standard

What does PM2.5 Contain ?

‡ Over New York state, measured PM2.5 mass typically

consists of 60% or more secondary components, implying that in addition to control of primary emissions, there is a need to focus on important precursors (SO2 and NOx).

‡ Measurements from IS52 (Bronx, NY) and Pinnacle

State Park (PSP) indicate that sulfate and carbon [ elemental (EC) and organic (OC)] together constitute ~ 47% during winter and as much as 65% during summer.

EC OM NH4 NO3 SO4 Other Concentration, µg/m3

Monthly Average PM2.5 Composition at IS52 (Bronx, NY) monitor (2002- 2006)

IS52, Bronx, NY Monthly Average 2002-2006

25 20 15 10 5

OM = 1.4 * OC

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ‡

OM/ PM 2 .5

„

~ 25% in winter

„

~ 30% in summer

‡

EC/ PM 2 .5

„

~ 8% in winter

„

~ 5% in summer

‡

SO4

= / PM 2 .5 „

~ 22% in winter

„

~ 33% in summer

EC OM NH4 NO3 SO4 Other Concentration, µg/m3

Monthly Average PM2.5 Composition at PSP monitor (2002-2006)

Pinnacle State Park, NY Monthly Average 2002-2006

18 16 14 12 10 8 6 4 2

OM = 1.4 * OC

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

‡

OM/ PM 2 .5

„

~ 20% in winter

„

~ 26% in summer

‡

EC/ PM 2 .5

„

~ 3% in winter

„

~ 2% in summer

‡

SO4

= / PM 2 .5 „

~ 32% in winter

„

~ 42% in summer

Why Worry about Carbon ?

‡

Atmospheric chemistry of sulfate formation has been understood reasonably well arising from SO2 emissions

„

Largely from combustion of coal and oil in electric utilities

„

And from combustion of diesel, gasoline, and fuel oil by on- and non-road mobile sources, and stationary sources

‡

Control programs such as Clean Air Interstate Rule (CAIR), Regional Haze Rule, and Low Sulfur Diesel Rule are expected to decrease the contribution of sulfate and nitrate to PM2.5, thereby increasing the need for a better understanding of the relative role of carbon and its contribution to PM2.5 mass.

‡

Elemental (EC) and Organic Carbon (OC) are operationally defined

„

Differences between measurement and analytical methods

„

Wide range of conversion factors to convert OC to OM that vary by region and season

„

OC refers to a composite of species, a majority of which is not well characterized

„

Estimate of OC in blank filters

Air Quality Models

‡ Air quality models:

„ Provide temporal and spatial resolution of species

concentrations that is not typically available in measured data at all locations.

„ Help to visualize and understand the atmospheric processes

to the extent of the current scientific understanding and assumptions, and to evaluate control strategies.

‡ These models are driven by inputs derived from

„ Meteorology „ Emissions „ Chemical mechanisms

‡ We utilized the Carbon Bond IV (CB-IV) chemical

mechanism, which is a set of representative chemical equations attempting to simulate the complex reality in a modeling framework

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Overview of PM Emissions Modeling & CMAQ Outputs

PM2.5 & PM10 Emissions Speciate into SO4

=, NO3

• ,

EC, OC, and the rest into unspeciated PM

CMAQ

( Gas-phase Chem istry, SO4/ NO3/ NH 4 equilibrium , SOA yields based on precursor species reacted Advection, Diffusion, Deposition)

ASO4, ANO3, ANH4, AEC, AORGPA, AORGA, AORGB, A25, ASOIL, ASEAS, ACORS In both nucleation and accumulation modes Comes from source profiles

AORGPA – Primary Anthropogenic Organic AORGA – Secondary Anthropogenic Organic AORGB – Secondary Biogenic Organic

Apply temporal profile Apply gridding surrogates

How does the Model Compare with Observations

‡ Model results are from CMAQ-based air quality forecasting

simulations covering from June 2005 to Dec 2006

‡ Data from a single 12-km by 12-km grid cell that contained the

monitor was used

‡ All measured mass and species concentrations used in the

comparisons were obtained from the AQS database for all STN sites in NY

‡ Sites were grouped into three categories: NY City, Rural and

Western NY (see map on next slide)

‡ Analysis was for summer (June, Jul, Aug) and winter (Dec, Jan,

Feb) periods

‡ Also shown are the diurnal model predictions, which are

compared with continuous monitoring data for IS52

NY City Rural Western NY

CMAQ (y-axis) vs. STN (x-axis): PM2.5 Mass (µg/m3)

Summer (June-Aug)

NY City Rural Western NY

80.0 40.0 40.0 32.0 32.0 60.0 24.0 24.0 40.0 16.0 16.0 20.0 8.0 8.0 0.0 0.0 0.0 0.0 20.0 40.0 60.0 80.0 0.0 8.0 16.0 24.0 32.0 40.0 0.0 8.0 16.0 24.0 32.0 40.0

Winter (Dec, Jan-Feb)

350.0 25.0 40.0 280.0 20.0 32.0 210.0 15.0 24.0 140.0 10.0 16.0 70.0 5.0 8.0 0.0 0.0 0.0 0.0 70.0 140.0 210.0 280.0 350. 0.0 5.0 10.0 15.0 20.0 25.0 0.0 8.0 16.0 24.0 32.0 40.0

CMAQ (y-axis) vs. STN (x-axis): PM2.5 Sulfate (µg/m3)

Summer (June-Aug)

NY City Rural Western NY

20.0 20.0 20.0 16.0 16.0 16.0 12.0 12.0 12.0 8.0 8.0 8.0 4.0 4.0 4.0 0.0 0.0 0.0 0.0 4.0 8.0 12.0 16.0 20.0 0.0 4.0 8.0 12.0 16.0 20.0 0.0 4.0 8.0 12.0 16.0 20.0

Winter (Dec, Jan-Feb)

8.0 150.0 8.0 120.0 6.0 6.0 90.0 4.0 4.0 60.0 2.0 2.0 30.0 0.0 0.0 0.0 0.0 2.0 4.0 6.0 8.0 0.0 2.0 4.0 6.0 8.0 0.0 30.0 60.0 90.0 120.0 150.

CMAQ (y-axis) vs. STN (x-axis): PM2.5 Nitrate (µg/m3)

Summer (June-Aug)

NY City Rural Western NY

10.0 2.0

3.0

8.0

2.4 1.5

6.0

1.8 1.0

4.0

1.2 0.5

2.0

0.6 0.0 0.0

0.0 0.0 2.0 4.0 6.0 8.0 10.0 0.0 0.5 1.0 1.5 2.0

0.0 0.6 1.2 1.8 2.4 3.0

Winter (Dec, Jan-Feb)

4.0 15.0 15.0 12.0 12.0 3.0 9.0 9.0 2.0 6.0 6.0 1.0 3.0 3.0 0.0 0.0 0.0 0.0 3.0 6.0 9.0 12.0 15.0 0.0 1.0 2.0 3.0 4.0 0.0 3.0 6.0 9.0 12.0 15.0

CMAQ (y-axis) vs. STN (x-axis): PM2.5 Ammonium (µg/m3)

Summer (June-Aug)

NY City Rural Western NY

10.0 4.0 8.0 8.0 3.0 6.0 6.0 2.0 4.0 4.0 1.0 2.0 2.0 0.0 0.0 0.0 0.0 2.0 4.0 6.0 8.0 10.0 0.0 1.0 2.0 3.0 4.0 0.0 2.0 4.0 6.0 8.0

Winter (Dec, Jan-Feb)

4.0 60.0 8.0 3.0 45.0 6.0 2.0 30.0 4.0 1.0 2.0 15.0 0.0 0.0 0.0 0.0 15.0 30.0 45.0 60.0 0.0 1.0 2.0 3.0 4.0 0.0 2.0 4.0 6.0 8.0

CMAQ (y-axis) vs. STN (x-axis): PM2.5 Org. Matter (µg/m3)

Summer (June-Aug)

NY City Rural Western NY

16.0 12.0 15.0 12.0 12.0 9.0 9.0 8.0 6.0 6.0 4.0 3.0 3.0 0.0 0.0 0.0 0.0 4.0 8.0 12.0 16.0 0.0 3.0 6.0 9.0 12.0 0.0 3.0 6.0 9.0 12.0 15.0

Winter (Dec, Jan-Feb)

8.0 60.0 15.0 12.0 6.0 45.0 9.0 4.0 30.0 6.0 2.0 15.0 3.0 0.0 0.0 0.0 0.0 15.0 30.0 45.0 60.0 0.0 2.0 4.0 6.0 8.0 0.0 3.0 6.0 9.0 12.0 15.0

CMAQ (y-axis) vs. STN (x-axis): PM2.5 EC (µg/m3)

Summer (June-Aug)

NY City Rural Western NY

10.0 0.6 2.0 8.0 0.5 1.6 6.0 0.4 1.2 4.0 0.2 0.8 2.0 0.1 0.4 0.0 0.0 0.0 0.0 2.0 4.0 6.0 8.0 10.0 0.0 0.1 0.2 0.4 0.5 0.6 0.0 0.4 0.8 1.2 1.6 2.0

Winter (Dec, Jan-Feb)

25.0 0.6 2.0 20.0 0.5 1.6 15.0 0.4 1.2 10.0 0.2 0.8 5.0 0.1 0.4 0.0 0.0 0.0 0.0 5.0 10.0 15.0 20.0 25.0 0.0 0.1 0.2 0.4 0.5 0.6 0.0 0.4 0.8 1.2 1.6 2.0

Comparison based on STN data

‡ Primary PM emissions appear to be over-estimated for

the 3 urban monitors in New York City, as illustrated by significant over-predictions of EC.

‡ OM in the summer period was found to be under-

predicted much more so at rural and western NY monitors than those in New York City, which may be due to underestimation of secondary organic aerosols (SOA).

‡ Severe over-estimation of EC seen for the winter

period for urban monitors in New York City is from a combination of shallow planetary boundary layer height and high primary emissions.

Predicted and Observed

Summer Winter

Diurnal Profiles at IS52, Bronx, NY

2 4 6 8 10 12 14 16 18

Observed

10 20 30 40 50 2 4 6 8 10 12 14 16 18 20 22

Hour

Predicted Observed Predicted Observed Predicted

10 20 30 40 50 60 70 80

Predicted

2 4 6 8 10 12 14 16 18 20 22

Hour

5 10 15 20

Observed Observed Predicted Observed Predicted

Mass Sulfate Nitrate

Observed Predicted

6 12 4 16 3.5 14 5 10 3 12 4 8 2.5 10 2 8 3 6 1.5 6 2 4 1 4 1 2 0.5 2

(All concentrations are in

0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22

Hour Hour

µg/m3)

7

Note the use of different

4 3.5 1.6 1.4 3.5 3 6

scales between Observed

0 2 4 6 8 10 12 14 16 18 20 22

Hour

0 2 4 6 8 10 12 14 16 18 20 22

Hour

1.2 3 2.5 5

and Predicted

1 2.5 2 4 0.8 2 1.5 3

Concentrations

0.6 1.5 1 2 0.4 1 0.5 1 0.2 0.5

Predicted and Observed

Summer Winter

Observed

Diurnal Profiles at IS52, Bronx, NY

18 16

Mass

14

ved

12 10

ser

8

b O

6 4 2

• Org. Matter

EC

ed v ser b O

10 20 30 40 50

Predicted

5 10 15 20

Observed

10 20 30 40 50 60 70 80

Predicted

0 2 4 6 8 10 12 14 16 18 20 22 2 4 6 8 10 12 14 16 18 20 22

Hour Hour

12 3.5 25 6 3 5 10 20

Observed Predicted

15 2 0 2 4 6 8 10 12 14 16 18 20 22

Hour

2.5

Predicted Observed Observed Predicted Predicted Predicted

4 8 3 6 1.5 10 2 4 1 5 0.5 1 2 0 2 4 6 8 10 12 14 16 18 20 22

Hour

(All concentrations are in µg/m3)

9 1.6 9 2 8 8 1.4 0 2 4 6 8 10 12 14 16 18 20 22

Hour

0 2 4 6 8 10 12 14 16 18 20 22

Hour

Note the use of different

7 7 1.5 1.2 6 5 4 3 6 1

scales between Observed and Predicted Concentrations

5 4 3 1 0.5 0.8 0.6 0.4 2 2 1 0.2 1

Comparison based on Continuous data

‡ At IS52, even though the predicted hourly

concentrations are higher than measurements, there seems to be a better agreement in the diurnal patterns for the winter period for PM2.5 mass, sulfate and nitrate, but not for EC and OM

‡ Except for nitrate, all other species differ in their

measured and predicted summer diurnal profiles, with predicted concentrations often higher by a factor of two or more for some species

‡ The non-capture of the overall diurnal pattern in

summer for the species other than nitrate suggests the need for a closer examination of the speciation used in the emissions inventory and their temporal allocation

What Does Model-Predicted Organic Matter (OM) Show?

‡ Modeled OM is combination of contributions from

• primary and secondary anthropogenics, and
• secondary biogenics

‡ We calculated model-predicted hourly average

concentrations of OM at

• Urban (IS52)
• Suburban (Albany)
• Rural (Pinnacle State Park)
• Remote (Whiteface Mountain)

CMAQ-predicted Primary and Secondary Organic Concentrations

Modeled Primary and Secondary OM Conc. IS52 Bronx

2 4 6 8 10 12 14 16 Jun-05 Aug-05 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06 Dec-06 Month-Year Concentration, µg/m

3

SecAnth_OM (ug/m3) SecBiog_OM (ug/m3) PriAnth_OM (ug/m3)

Modeled Primary and Secondary OM Conc. Albany

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Jun-05 Aug-05 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06 Dec-06 Month-Year Concentration, µg/m3 SecAnth_OM (ug/m3) SecBiog_OM (ug/m3) PriAnth_OM (ug/m3)

Modeled Primary and Secondary OM Conc. Pinnacle State Park

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Jun-05 Aug-05 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06 Dec-06 Month-Year Concentration, µg/m 3 SecAnth_OM (ug/m3) SecBiog_OM (ug/m3) PriAnth_OM (ug/m3)

Modeled Primary and Secondary OM Conc. Whiteface Mountain

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Jun-05 Aug-05 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06 Dec-06 Month-Year Concentration, µg/m3 SecAnth_OM (ug/m3) SecBiog_OM (ug/m3) PriAnth_OM (ug/m3)

Modeled Ratio of SOA to OM

Site Sum m er/ Fall W inter IS 52 (urban) < 10% < 4% Albany (suburban) ~ 35 to 40% < 10% Pinnacle SP (rural) ~ 40 to 45% ~ 20% Whiteface Mountain (remote) ~ 50 to 60% ~ 20%

What does this mean?

‡ Model-based data suggests that the primary

anthropogenic organic matter (PAOM) often dominates compared to the other organic components at all these locations

‡ PAOM is substantial even at rural locations,

suggesting the need for examination of the emissions inventory and its processing

‡ Unlike the photochemical model, routine ambient

measurement techniques do not provide for a distinction between primary and secondary organic aerosols

‡ Techniques based on continuous EC and OC to

estimate SOA are under investigation

Conclusions

‡ The over-prediction of EC and primary organics

points to the need for detailed assessment of the emissions inventory and its processing

‡ The PM speciation in the model for carbon is limited

• nly to OC and EC components by the current

available source characterization data and needs further refinement/ improvement

‡ Need linkages between ambient measurements of

• rganics and its speciated compounds to model-

based estimates

Acknowledgements

‡ We would like to express our thanks to the

Bureau of Air Quality Surveillance staff for providing the measured PM2.5 mass and species concentrations.