Improving air quality analysis through closer integration of observations models
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Improving air quality analysis through closer integration of observations & models. Greg Carmichael , Scott Spak Air quality management contacts: Joe Hoch, Wisconsin DNR Matthew Johnson, Iowa DNR Donna Kenski , LADCO. Motivations. Applied science for AQ decision support.

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Improving air quality analysis through closer integration of observations models

Improving air quality analysis through closer integration of observations & models

Greg Carmichael, Scott Spak

Air quality management contacts:

Joe Hoch, Wisconsin DNR

Matthew Johnson, Iowa DNR

Donna Kenski, LADCO


Motivations

Motivations

Applied science for AQ decision support

Forecasting for AQ decision support

4.Daily resource for AQ managers

  • Public benchmark data archive to inform planning & forecasters

  • Integrated forecasting—support products for transportation, energy production, hazards

  • Demonstrated need for urban-scale nowcasting tools to support local public health response to urban air quality events

  • Model skill for fine particle concentrations during wintertime episodes: how to improve?

  • AQ-WX feedbacks for both AQ & WX

  • Uncertainties & error attribution in derived policy metrics:

    • met?

    • ICs/BCs?

    • model?

  • Value in assimilating remote sensing for AQ

    • atmospheric composition

    • land surface

    • meteorology

    • individual vs. synergistic effects?


Assessing the ozone secondary standard with the stem adjoint tes assimilation

Assessing the Ozone Secondary Standard with the STEM adjoint & TES assimilation

Huang et al. (2012). Impacts of transported background pollutants on summertime Western US air quality: model evaluation, sensitivity analysis and data assimilation, ACPD, 12, 1–73.

  • W126 monthly index, June-July 2008

    • 60 km CONUS

    • western US 12 km

  • Estimate background contributions

    • Compare contributions from NA biomass burning emissions by turning off FINN & RAQMS BB emissions

    • Compare contributions from transported background (model boundary) by perturbing RAQMS and GEOS-ChemBCs for O3 & precursors:

      CO, NO, NO2, NO3, HNO3, HNO4, PAN, N2O5

    • Evaluate W16 metric suitability and uncertainties for policy applications


Improving air quality analysis through closer integration of observations models

Model horizontal/vertical resolution & BC uncertainties strongly affect W126 value...


Improving air quality analysis through closer integration of observations models

… and affect W126 adjoint sensitivities almost as strongly as local precursors


Transported background o 3 in the adjoint of a regional ctm

Transported Background O3in the adjoint of a regional CTM

extrapolated from sensitivities to 75% perturbations in BCs

Huang et al. (2012). Impacts of transported background pollutants on summertime Western US air quality: model evaluation, sensitivity analysis and data assimilation, ACPD, 12, 1–73.


Improving air quality analysis through closer integration of observations models

LADCO Winter Nitrate Study: Phase II

How does interactive snow affect

winter PM2.5 episodes?

Most effective local & regional emissions control? NH3

  • During episodes, meteorology sensitivity >= emissions sensitivity

  • Urban impacts of local (100 km, 250 km) NOx & NH3 controls >60% of regional total

  • CSAPR enhances effectiveness of regional NOx controls, nearly neutral to NH3


Improving air quality analysis through closer integration of observations models

Integrated forecast goals

  • air quality

    • PM2.5 & ozone

    • NAAQSexceedances

    • Background

  • aerosol-climate effects

  • carbon cycle

  • renewable energy

  • road conditions

  • floods, agriculture

  • Toward 4DVAR in highresolution coupled Earth System modeling for prediction, process studies, climate, decision support, and policy applications: 2015-2030


Improving air quality analysis through closer integration of observations models

Iowa City Landfill Fire 2012

This is a job for AQAST

  • 7.5 acres burn, beginning 5/26

  • ~1 million shredded tires

  • Uncontrolled tire fires (EPA, 1997)

    • 40+ species of concern

    • 35,000x more mutagenic than coal-fired power plants

  • Immediate state & federal activity

    • Whole air samples, GC/MS (IA SHL)

    • Regional HYSPLIT dispersion (NOAA NWS)

    • MODIS visible (IA National Guard)

    • Fire containment & treatment (US EPA)

  • Limited decision support response for air quality & public health


Improving air quality analysis through closer integration of observations models

5/30 1st AERMOD forecast

  • 3DVAR IMAPP MODIS L2 DB

  • 35 vertical levels in WRF-Chem

  • AERMOD @ 100 mx 100 m

  • National Elevation Dataset


Improving air quality analysis through closer integration of observations models

Interdisciplinary rapid response

3 intensive sampling platforms

Betsy Stone (Chemistry)

  • PM2.5 mass and speciation

  • 40+ organics

    Charles Stanier (CBE, IIHR)

  • <1 Hz SO2, CO, CO2

  • Ultrafine Particle Counter (CPC)

  • Scanning Mobility Particle Sizer (0.015-.64 µm)

  • Aerosol Particle Sizer (0.54-20 µm)

  • Weather station

    Tom Peters (Public Health)

  • Grimm Optical Particle Counter (0.3-20 µm)

  • Portable black carbon monitor

    Tom Schnell (CoE Operator Performance)

  • HUMVEE with power for all instruments


Improving air quality analysis through closer integration of observations models

Plume chasing: 150+ µg/m3 urban

toxic PM2.5 source

Landfill fire

0.8km

2.7 km

1.8 km

Sampling points during plume chase on June 01 2012.

15-minute average concentrations of 157 µg/m3 PM0.3-2.5 at plume centerline @ 0.8 km, 34 µg/m3 PM0.3-2.5 @ 2.7 km.


Improving air quality analysis through closer integration of observations models

5/31 WRF-Chem+ AERMOD + obs ->

Background PM2.5/CO/SO2 + landfill emissions

~4 µg/m3 per ppb SO2

APS PM 0.5- 2.5 (µg/m3)

APS PM 0.5- 2.5 (µg/m3)

Iowa City SO2

SO2 (ppb)

Estimated PM2.5 emissions rate:

10 ± 3 µg/m2/s ~5 g/s


Improving air quality analysis through closer integration of observations models

6/1 60hr weekend forecast to Johnson County Public Health using US EPA recommended metrics


Improving air quality analysis through closer integration of observations models

Verification:

Increment within 3 µg/m3

AQS monitor 11.5 km east hit 77 µg/m3

APS PM 0.5- 2.5 (µg/m3)


Improving air quality analysis through closer integration of observations models

Learning from this event:

on-demand decision support products

  • AQAST + Johnson County + Iowa DNR Guidelines & toolkit for state/local public health response to urban fires & toxic releases

  • New decision support features in AQAST forecast

    • AERMOD meteorology inputs web portal

    • Rapid response capacity for the next year: urban AERMOD + WRF-Chem background

  • Urban exposure & health modeling/monitoring


Recent synergistic highlights

Recent synergistic highlights

  • Santiago, Chile: daily operational WRF-Chem PM10/PM2.5 forecast

  • WRF-Chem aerosol-cloud assimilation

    • MODIS cloud optical depth retrievals alter droplet concentration and effective radius

    • AOD + COD constrain aerosols throughout scenes

  • Improving parcel-level urban renewable energy estimates with NASA cloud retrievals

P. Saideet al. (2012). Seeing aerosol through clouds: assimilating submicron aerosols from satellite cloud retrievals, PNAS, in press.

P. Saideet al. (2011). Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model, Atmospheric Environment, doi:10.1016/j.atmosenv.2011.02.001.


Year 2 plans

Year 2 Plans

  • Aerosol RF in72-hour operational forecast system

  • Disaggregate AQ impacts from assimilation fields

  • Publishmaps & data download online

  • Extendto custom AQ management products

  • Evaluateand refine configuration

  • ImplementAERMOD rapid response capability

  • Supportyourtiger team activities


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