Air Quality Applied Sciences Team (AQAST) EARTH SCIENCE SERVING AIR QUALITY MANAGEMENT NEEDS satellites suborbital platforms models AQAST Air Quality Management Needs Pollution monitoring Exposure assessment AQ forecasting Source attribution of events Quantifying emissions Assessment of natural and international influences Understanding of transport, chemistry, aerosol processes Understanding of climate-AQ interactions AQAST Earth science resources

AQAST Membership: PIs and Co-Is Daniel Jacob (leader), Loretta Mickley (Harvard) Greg Carmichael (U. Iowa) Dan Cohan (Rice U.) Russ Dickerson (U. Maryland) Bryan Duncan, Yasuko Yoshida, Melanie Follette-Cook (NASA/GSFC); Jennifer Olson (NASA/LaRC) David Edwards (NCAR) Arlene Fiore (Columbia); Meiyun Lin (Princeton) Jack Fishman, Ben de Foy (Saint Louis U.) Daven Henze, Jana Milford (U. Colorado) Tracey Holloway, Steve Ackerman (U. Wisconsin); Bart Sponseller (Wisconsin DRC) Edward Hyer, Jeff Reid, Doug Westphal, Kim Richardson (NRL) Pius Lee, Tianfeng Chai (NOAA/NESDIS) Yang Liu, Matthew Strickland (Emory U.), Bin Yu (UC Berkeley) Richard McNider, Arastoo Biazar (U. Alabama – Huntsville) Brad Pierce (NOAA/NESDIS) Ted Russell, Yongtao Hu, Talat Odman (Georgia Tech); Lorraine Remer (NASA/GSFC) David Streets (Argonne) Jim Szykman (EPA/ORD/NERL) Anne Thompson, William Ryan, Suellen Haupt (Penn State U.)

AQAST organization AQAST supports two types of projects:  Investigator Projects (IPs). These involve typically a single AQAST member working with an air quality management partner;  Tiger Team Projects (TTPs). These involve a collaboration of several AQAST members pooling their expertise to address urgent needs from one or more air quality management partners. AQAST projects must bridge Earth Science and air quality management:  Focus on use of Earth Science resources  Clear air quality management outcomes  1-year deliverables of value for air quality agencies AQAST has great flexibility in how it allocates its resources  Members are encouraged to adjust their IPs to the evolving needs of air quality management  The TTPs are redefined yearly to address the most pressing needs  The team is self-organizing and can respond quickly to demands Help us help you! Contact Daniel Jacob to discuss how AQAST can help address your specific AQ management

Scope of current AQAST projects (IPs and TTPs) Partner agency Local: RAQC, BAAQD State: TCEQ, MDE, Wisconsin DNR, CARB, Iowa DNR, GAEPD, GFC Interstate: EPA Region 8, LADCO National: NPS, NOAA, EPA Theme SIP Modeling AQ processes Monitoring AQ-Climate Background IC/BC for AQ models Forecasting Emissions Future satellites Earth Science resource Satellites: MODIS, MISR, MOPITT, AIRS, OMI, TES, GOES Suborbital: ARCTAS, DISCOVER-AQ, ozonesondes, PANDORA Models: MOZART, CAM AM-3, GEOS-Chem, RAQMS, STEM, GISS, IPCC

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GEOS-Chem model simulation of background ozone over the US in support of the current ozone ISA and REA 0.5 o x0.67 o resolution for North America, 2 o x2.5 o global, 48 vertical layers Driven by NASA GEOS-5 assimilated meteorological data Detailed mechanistic representation of ozone-NO x -VOC-aerosol chemistry 2006 anthropogenic emissions: NEI05 for US, CAC for Canada, BRAVO (scaled) for Mexico, EMEP for Europe, Streets for East Asia, EDGAR (scaled) for rest of world Natural sources: lightning (OTD/LIS), fires (GFED2 monthly), soils (Yienger), stratosphere (Linoz) year simulation; focus on 2006 for evaluation 2x x0.667 Color scale Indicates topography (surface pressure) Zhang et al. [2011] –

Four GEOS-Chem simulations Standard – as described above US background – no US anthro emissions NA background - no N.American anthro emissions Natural – no anthro emissions worldwide 2006 MDA8 ozone at CASTNet sites- with mean (4 th highest) inset US background: see Wang et al. [2009]

Ozone statistics for the US (CASTNet sites) + > 1.5 km PRB: 27 ± 8 40 ± 7 Natural: 18 ± 6 27 ± 6 Frequency distribution of MDA8 ozone for March-August 2006 GEOS-Chem is overall unbiased PRB is 4 ppb higher than in previous GEOS-Chem versions Model shows little interannual variability for , neither do observations Obs: 50 ± 13 Model: 52 ± ± 9 57 ± 10 Zhang et al. [2011]

Model “4 th highest” MDA8 ozone in 2006 Background may limit or confuse ability of West to achieve a 60 ppb NAAQS; no such issue in East Annual 4 th highest ozone PRB for annual 4 th highest ozone 4 th highest PRB value Zhang et al. [2011]

Model PRB statistics vs. observed ozone in Intermountain West Comparison of daily MDA8 ozone concentrations for the ensemble of elevated sites (>1.5 km; 11 sites) in Intermountain West for spring and summer 2006  Model underestimates high extrema in spring (> 75 ppbv) - stratosphere  Correlation with measurements is poor in summer – lightning, wildfires 1:1 line max min 75th 50th 25th r = 0.57 r = 0.33 Lin Zhang, Harvard

Model underestimate of stratospheric intrusions in spring Sample stratospheric intrusion at Pinedale (WY) : model captures timing but not magnitude. Obs. model PRB Observed vs. simulated MDA8 ozone in spring 2006 at the 11 elevated CASTNet sites, colored by model stratospheric ozone concentration. Model has correct STE and vertical transport; we attribute problem to the general difficulty of Eulerian models in resolving fine structures. Lin Zhang, Harvard observations stratosphere

Ozone enhancement from lightning in Intermountain West  Model lightning increases ozone concentrations by 7-19 ppbv in summer, comparable to enhancements from US anthropogenic emissions.  This lightning influence in the model is biased high, even though lightning location is constrained by OTD/LIS satellite observations;  focus future model improvement on NO x yield per lightning flash, vertical distribution Lin Zhang, Harvard model PRB observations lightning

Sensitivity of model ozone to wildfire emission inventory fire reports monthly Maximum fire enhancements of MDA8 ozone In GEOS-Chem – August 2006 Model is highly sensitive to choice of inventory, spatial and temporal resolution Influence of fires is mainly limited to fire region; little influence at CASTNet sites in 2006 Need to examine other years, MOPITT and AIRS CO data for fires Lin Zhang, Harvard

Effect of bromine chemistry on the ozone background Bromine sources in troposphere from marine bromocarbons (especially bromoform, CHBr 3 ) and sea-salt debromination Bromine chemistry drives catalytic ozone loss and NO x loss GEOS-Chem mean ozone decreases (ppb) from bromine chemistry Background surface ozone in US decreases by 3 ppb (annual mean) Justin Parrella, Harvard

Effect of HO 2 conversion to H 2 O in aerosols Atmospheric observations suggest a large loss of HO 2 to aerosols missing from current models. Coupled Cu-Fe transition metal redox chemistry could drive such an uptake and convert HO 2 to H 2 O Jingqiu Mao, submitted GEOS-Chem mean surface ozone decrease (ppb) from HO 2  H 2 O chemistry Background surface ozone in US decreases by 2-5 ppb (annual mean)

Question from Adele Malone: “where should we be making measurements (nationally; regionally) and what other parameters should we be measuring?” Spring-summer measurements at remote sites and over a range of altitudes provide the best tests of model background ozone ozone at remote sites can often approximate background background increases with altitude plateau better than mountaintop Concurrent CO measurements with 10 ppb sensitivity can greatly help for source attribution of background ozone CO very high (fires), high (urban), low (lightning), very low (stratosphere) Have money to spare? Invest in an ozone lidar continuous measurement of ozone vertical distribution Get into NASA (and NOAA)’s deep pockets with research-to-operations motivation Geostationary satellite for ozone vertical distribution (GEO-CAPE) Aircraft campaigns, ozonesondes AQAST is your advocate! Spread the word that AQAST is an important resource for AQ management McDonald-Buller et al., Establishing Policy Relevant Background (PRB) Ozone Concentrations in the United States, Environ. Sci. Technol.,