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This presentation contains preliminary results and data.

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Air Quality in the Northern Colorado Front Range (First) Results from FRAPPÉ and DISCOVER-AQ

Gabriele Pfister and Frank FlockeSojin Lee

Atmospheric Chemistry Observations and Modeling Lab (ACOM)National Center for Atmospheric Research (NCAR)

& the FRAPPÉ and DISCOVER-AQ Science Teams

NOAA Seminar, 29 March 2017

Air quality issues in the NFRMA

The Front Range is in non-Attainment for the 8-hour Ozone NAAQS

The Front Range is in non-Attainment for the 8-hour Ozone NAAQS

Air quality issues in the NFRMA

U.S. air overall has become cleaner

Due to emission controls and advanced technology, pollutant emissions in the U.S. have decreased.

Front Range: 22% decrease

2005-2007 2009-2011

Trends in Colorado Surface Ozone and NOx

Source: CDPHE 2015 Annual Report

NOx

Ozone

linear regression lines with confidence limits

Source: Colorado SIP, released December 2016; Correction Method (Reddy and Pfister, 2016) http://raqc.org/technical-support-documents-for-the-moderate-area-2008-8-hour-ozone-standard-state-implementation-plan/

75 ppb

Surface Ozone Trends 2006-2015

Ozone = NOx + VOCs +

Trends in 4th highest MDA8 corrected for weather (exceptional events excluded)

Rocky Flats NREL/Golden CAMP/Denver

Background Ozone Trends

+ Background/Inflow

Cooper et al., 2014

Ozone = NOx + VOCs +

What and where are the relevant sources?

How do these emissions get transported?

How do they get chemically processed?

How much pollution comes into Colorado?

Which are the best ways to improve air

quality?

The Questions

Air quality monitoring (and regulation)

(ground)Observations

Emissions

Model

Most operational measurements of air quality are made

at the surface and cover few pollutants.

The Challenge

• The atmosphere is three dimensional. Pollutants move and mix horizontallyAll processes are time-dependent and many are non-linear ⇒4D

• There is not just NOx and “VOC.” VOC are comprised of hundreds of different species with varying roles in ozone chemistry and are emitted from different sources.

• Reducing VOCs or NOx does not necessarily decrease ozone. Sometimes ozone might increase because ozone chemistry is dependent on the mix of ingredients.

Most operational measurements of air quality are made

at the surface and of only few pollutants. BUT:

In order to adequately simulate Front Range ozone and accurately predict outcomes of possible control strategies, the model must:

• Use accurate emissions (magnitude, speciation, location, diurnal and seasonal variability)

• Distribute emissions into an accurate boundary layer

• Predict wind direction and speed, horizontal dispersion and mixing and vertical mixing/dilution with accurately modeled background air

• Accurately compute the chemical reactions and physical transformations

• Predict future trends

(or anywhere for that matter)

Modeling AQ in the Front Range

Air quality monitoring (and regulation)

(ground)Observations

Emissions

Model

FRAPPÉ (Field Campaign) Study Approach

Observations

compare

Model

Emissions

evaluaterefine

Processes

evaluate

refine

Integration of different platforms and observation strategies

FRAPPÉ & DISCOVER-AQ15 July - 18 August 2014, Colorado Front Range

Funding SourcesFRAPPÉ: State of Colorado / CDPHE

National Science Foundation (NSF)

DISCOVER-AQ: NASA

Others: NOAA, GO3 Project, NPS, EPA

PIs: Gabriele Pfister and Frank FlockeNational Center for Atmospheric Research (NCAR)

PI: James CrawfordNASA Langley

Merging of two major field campaigns

Comprehensive look at air quality in the Northern Colorado Front Range

FRAPPÉ and DISCOVER-AQ

4 aircraft

6 mobile vans

6 enhanced ground sites

Tethered balloons and daily ozone sondes

Enhanced CDPHE network

The Colorado Front Range

• Diverse sources of air pollutants• Separated spatially/temporally in some cases, co-located in others • Different expectations of future growth• Emissions difficult to assess due to variability and high

number of individual sources

• Unique, mountain-driven local meteorology• Drives local mixing and transport• Recirculation of pollutants• A challenge for chemical-transport models

- Non-Attainment Area

● Oil/Gas Wells

NASA P-3 flight tracks: • focus on non-Attainment area• repetitive flight pattern to look at

diurnal variability• Vertical profiling over selected

ground sites 1,000agl-18,000asl

NCAR/NSF C130 flight tracks:• larger region with targeted flights • emission sources in the NFRMA and other areas

(urban, O&NG, agriculture, powerplants, …)• Mixing, outflow, upslope events,…

Methane emissions

Methane important greenhouse gas and released by both agriculture and O&G activities

What are the relative contributions of biogenic (landfills and cattle) and thermogenic (oil and gas) sources to methane in the Denver area?

Hydrogen stable isotopic measurements of source and flight samples indicate biogenic sources may be a significant contributor to methane despite the large number of oil and gas production sites – perhaps due to regulation of methane emissions from oil and gas wells in Colorado?

RF05July 31, 2014

85% biogenic49% biogenic

Amy Townsend-Small et al., U Cincinnati (GRL, 2016)

Methane emissions from O&G & agriculture

Methane: Picarro 2311 (RAF/ACOM)Ethane: CAMS (A. Fried, CU)

C-130 flights E of 105.2W and N of 39.8N


Franco et al., 2016 [~5%]SMOKE/CMAQ similar

XCEL Energy 2014 Jul/Aug Fraction in Distr. NG [7%]NEI (McKeen) similar

Methane: Picarro 2311 (RAF/ACOM)Ammonia:TILDAS(Aerodyne)


USDA inventory for CAFOs (JEQ, 2010)



Rocky Mountain NP

• Transport of Nitrogen from sources in the Front Range damages the fragile ecosystem.

• Nitrogen deposition in the Park is ~15 times greater than the natural background deposition rate.

• Ecosystem health first beganto decline at high elevationson the east side of the Park between 1950 and 1964. ~ 1,000,000 cattle

Methane: Picarro 2311 (RAF/ACOM)Ammonia:TILDAS(Aerodyne)


USDA inventory for CAFOs (JEQ, 2010)


Distribution of ammonia (gas phase, C130)

Distribution of ammonium ion (particle phase, C130)

Model setup

CMAQ version v5.2 beta (Deborah Luecken (U.S. EPA)); NCAR ECMWF analysis fields; WRF V3.8.1 met; CB06r3 chem.

Category EPA sectors Source

Mobile Onroad mobile, nonroad mobile, Rail emissions

d01: EPA 2011 mobile emission based on EPA2011v6.3 platform; d02: 2017 Alpine geophysics inventory (Dennis McNally, AG)

Oil and Gas nonpoint oil and gas, point oil and gas emissions

2014 CDPHE emission inventory with spatial, temporal, and chemical speciation profiles from EPA 2011v6.3 platform (Dale Wells, CDPHE)

Other point EGU, point NONIPM EGU: 2014 activity data, point NONIPM: 2017 projection EPA data + spatial, temporal, and chemical speciation profiles from EPA 2011v6.3 platform

Other Nonpoint

nonpoint RWC, nonpoint other, nonpoint agriculture

2017 projection EPA data + spatial, temporal, and chemical speciation profiles from EPA 2011v6.3 platform

Biogenic Biogenic BELD v3.61 with BELD4.1 land use database which incorporates the NLCD

Fires Fires Fire INventory from NCAR (FINN) v1.5

D01 domain 12x12km,D02 domain 4x4 km

Boundary cond. From RAQMS(Brad Pierce, NOAA)

Emissions (a priori)

Front RangeCounty

Mobile emission (onroad + nonroad + rail emission, unit: ton / year)

CO NOx VOC

EPA2011 EPA2014 This study EPA_2017 EPA2011 EPA2014 This study EPA_2017 EPA2011 EPA2014 This study EPA_2017

Adams 57,998 51,070 46,377 42,933 10,440 8,580 5,661 6,297 5,327 4,463 3,554 3,513

Arapahoe 76,235 67,165 61,052 56,709 9,633 8,278 5,416 5,475 7,039 5,939 4,786 4,619

Boulder 37,889 33,483 30,007 28,004 5,015 4,378 2,987 2,845 3,576 3,025 2,486 2,350

Broomfield 7,067 6,335 6,114 4,940 1,167 1,097 665 645 613 540 434 381

Clear Creek 6,528 4,133 3,747 3,922 1,646 1,590 481 919 474 325 234 277

Denver 72,826 72,142 57,794 52,101 11,037 13,497 6,043 6,122 6,538 6,442 4,433 4,041

Douglas 38,159 34,024 32,247 33,298 6,775 6,138 3,920 4,553 3,419 2,885 2,586 2,663

Elbert 5,263 3,398 3,483 3,550 1,325 862 514 820 509 311 298 324

Gilpin 1,295 817 853 776 429 380 289 295 161 104 106 98

Jefferson 72,870 60,689 53,220 52,057 10,184 8,298 5,223 5,646 6,831 5,363 4,487 4,356

Larimer 49,457 38,041 42,838 32,797 7,232 5,419 5,110 4,003 5,081 3,761 3,646 3,109

Park 5,193 3,215 3,227 3,317 780 500 350 408 748 500 479 469

Weld 45,882 34,852 45,809 29,649 9,265 7,632 7,831 5,623 4,475 3,229 3,489 2,680

Total 476,661 409,364 386,767 344,053 74,927 66,649 44,491 43,650 44,790 36,887 31,018 28,881

Emissions (a priori)

Front RangeCounty

Oil and Gas(nonpoint oil and gas + point oil and gas, unit: Ton / year)

CO NOx VOC

EPA2011 EPA2014 This study EPA_2017 EPA2011 EPA2014 This study EPA_2017 EPA2011 EPA2014 This study EPA_2017

Adams 891 695 637 825 1,966 686 1,497 1,765 3,475 1,548 1,173 3,576

Arapahoe 208 249 286 191 515 282 480 455 532 1,112 933 553

Boulder 129 81 49 129 185 40 24 180 1,308 369 233 1,288

Broomfield 42 23 9 42 69 12 3 67 689 135 85 653

Clear Creek 0 0 0 0 0 0 0 0 0 0 0 0

Denver 18 13 2 20 43 9 7 44 133 48 29 146

Douglas 0 0 0 0 0 0 0 0 0 0 0 0

Elbert 46 13 8 49 81 9 8 87 316 73 42 355

Gilpin 0 0 0 0 0 0 0 0 0 0 0

Jefferson 11 0 13 10 6 0 11 5 8 1 6 7

Larimer 58 58 39 59 97 34 21 94 796 415 305 787

Park 0 0 0 0 0 0 0 0 0 0 0 0

Weld 14,159 20,746 21,017 13,640 18,249 17,892 19,783 17,211 123,506 91,709 75,665 120,556

Total 15,562 21,879 22,061 14,965 21,211 18,963 21,835 19,908 130,762 95,409 78,471 127,922

Emission evaluation strategy

Observations (PTRMS; A. Wisthaler, U Innsbruck)Model

Benzene at Platteville

Hourly Averages

Surface Data are essential but evaluation is impacted by grid resolution/nearby sources and local winds.

Note: scale is cut off at 3 ppb

Halliday et al., 2016

Benzene at Platteville

Diurnal Cycle in Surface and Aircraft Benzene

Maximum values up to 30 ppb

Observed Winds (8-18 LT)

~ Model Grid Size

Modeled Winds (8-18 LT)

~ Model Grid Size

Modeled versus observed winds

Ft Collins West

C-130 aircraft - PBL

CAMP


Compare measurements with model predicted mixing ratios - but select days and time periods when models represent transport well.

Estimate source contribution to each sample from surrounding grid cells using wind direction and speed

Evaluate absolute concentrations and emission ratio predictions versus measured ratios.

Adjust individual emission sectors, based on data selection.


- Identify grid boxes for 10-17 LT < 1km ag where(1) The contribution of the evaluated emission sector is at least 50%(2) Observed and modeled winds are from same sector (10-17 LT, < 1km ag)

- Compare individual samples with modeled concentrations averaged over each set of grid boxes

…


Identify grid boxes for 10-17 LT < 1km ag where(1) The contribution of the evaluated emission sector is at least 50%(2) Observed and modeled winds are from same sector (10-17 LT, < 1km ag)

…

Mobile EmissionsNOx

OnG Emissions Benzene

Aircraft Model

Comparison CMAQ to aircraft (<1km agl)

NCAR/NSF C-130NASA P-3Fort Collins

Denver

Aircraft

10-17 am

NOx

Benzene

Model

Aircraft Model

Comparison CMAQ to aircraft (<1km agl)

NCAR/NSF C-130NASA P-3Fort Collins

Denver

Aircraft

10-17 am

Toluene Model

Acetylene

Comparison CMAQ to aircraft (<1km agl)NCAR/NSF C-130

10-17 am

“PAR”Propane

Aircraft ModelAircraft Model


(1) Divide NFRMA into three sub-regions, based on to emission sector dominating each sub-region

(2) Statistical model-observation comparison of- absolute concentrations- tracer-tracer ratios (“emission ratios”)

Comparison Model to Aircraft (C-130)

• Ethyne has a low model bias in all of NFRMA, about factor of 2

• Other mobile emission tracers show reasonable absolute agreement in Denver Area

• All mobile emission tracers indicate that traffic emissions are underestimated in the non-Denver urban and oil and gas sectors


We have seen this for NEI 2011 running WRF-Chem.

NEI 2011 emissions provided by Stu McKeen, NOAA


NOxEthane

Benzene Toluene

“PAR” Propane

NOxEthane

Benzene Toluene

“PAR” Ratios to

Propane

Model versus Obs.

All measured concentrations are strongly underestimated by the model

(except Ethane)

But the ratios to Propane look pretty good

(except Ethane, *which is good*)

Current status of emission adjustments

• Ethyne (acetylene) in mobile emissions: ~ double.

• Total mobile emissions (traffic volume?) in Non-Denver NFRMA: increase by a factor of ~3.

• Oil and gas sector emissions for propane and heavier alkanes: increase by at least a factor of 4.

• Oil and Gas sector emissions of Benzene: increase also by at least a factor of 4.

• Oil and Gas sector emissions of Ethane: leave unchanged (though might be on the high side)

• Next model run iteration pending.

Mobile Ground Sample (Weld County)

Alkanes:Propane 150 ppbButane 425 ppbPentanes 700 ppbHexanes 900 ppb

Benzene 65 ppb

“strong smell”

Mobile Ground Sample (Weld County)

Benzene 120 ppb Alkanes: Propane 55 ppbMethane 1.9 ppm Butanes 300 ppb

Pentanes 600 ppb

Model evaluation strategy

Adjust individual individual emission sectors to improve inventories and perform iterative model runs

Surface In-situ to refine inventories (point sources, emission ratios, ....). TBD

Support CTM study through Box Modeling to identify relevant VOCs and characterize chemical regimes (and the model’s ability to represent the chemistry)

Denver Urban5.4 s-1

NCAR/ACOM TOGA group; Rebecca Hornbrook Rebecca Hornbrook

0.1 Commerce City

16.5 s-1

Weld County6.7 s-1

Calculated OH Reactivity by FRAPPÉ Region

Alkane OH Reactivity

Alkane OH reactivities from Commerce City and Greeley Missed Approach are greatest at C5.

Weld region reactivity is centered around C4.

Rifle and Uintah have relatively even reactivities

from C2-C6.

Using alkane data from TOGA and WAS canisters: D. Blake, UC Irvine

NCAR/ACOM TOGA group; Rebecca Hornbrook

• Puts the air parcel in a jar then follows the chemistry• MCM (Leeds Master Chemical Mechanism) plus comparison with lumped mechanisms• No dilution, entrainment, mixing, deposition, etc. • Yields the ozone formation potential of a sample/average• Simulations at 2 pm LT, 40N, clear sky, fixed Temperature (“idealized conditions!”)

BOXMOX: Knote et al., Atmos. Environ, 2015; https://www2.acom.ucar.edu/modeling/boxmox-box-model-extensions-kpp

Chemical Box Modeling (BOXMOX)

C-130 measured

sample


Chemical Box Modeling (BOXMOX)

MCMV3.3MOZART-4MOZART pre-T1MOZART T1RADM2SAPRC99

Denver (NOx(t0) = 8 ppb Comm. City (NOx(t0) = 47 ppb

Weld(NOx(t0) = 2 ppb Weld(NOx(t0) = 10 ppb

Time (hours) Time (hours)

Ozo

ne (p

pb)

Ozo

ne (p

pb)


Chemical box modeling (BOXMOX) – Commerce City

Both mechanisms indicate NOx saturation but MOZART produces much less ozone

Frank Flocke et al., NCAR

Problems found with MCM mechanism – stay tuned for updates

Chemical box modeling (BOXMOX) – Commerce City

Both mechanisms produce ozone much more efficiently without EGU NOx

Better agreement with lower NOx

Emission contribution based on SMOKE/CMAQ

MCM -EGU NOx

MOZ -EGU NOx

-90% NOx

MCM Control

MOZ Control


Ozone production in WCReduced by 23 ppb (or factor of 10 in 8h) with O&NG emissions removed

Chemical box modeling (BOXMOX) – Weld County


Chemical box modeling (BOXMOX) – Denver urban

Transportation is still important but does not appear to be the major factor in Denver Metro


• FRAPPÉ & DISCOVER-AQ analysis is coming along, but there still is work to do. We could use a larger band.

• Emissions are one of the largest source of uncertainty in current understanding and modeling of NFRMA pollution. Being close to extensive (and variable) O&NG activities doesn’t help, but industrial and mobile emissions are also uncertain.

• Transport in the NFRMA is extremely complex. WRF does a reasonable job, but could use improvement for full emission assessment. Tests with observation nudging of FRAPPE observations are pending.

• Box modeling and OH reactivity studies show promise in assessing ozone chemical regimes and sensitivities, but need more explicit model runs (GECKO-A, modified MCM) to accurately test lumped mechanism performance.

• Reactive nitrogen lifetime is a critical factor for chemistry model performance in areas with high reactivity and large NOx sources.

• Long range transport, regional background (increasing in the western US) and (maybe) stratospheric influence can be important for NFRMA ozone (including surface ozone) Current work shows shortcomings in the models in

Summary

THANK YOU

Joint FRAPPÉ and DISCOVER-AQ Science Team Meeting2-3 May, UCAR Center Green, Boulder CO

Air Quality Open House at NCAR Mesa Lab Wed, 3 May 4-7 pm

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