Sensitivity to changes in HONO emissions from mobile sources simulated for Houston area Beata Czader, Yunsoo Choi, Lijun Diao University of Houston Department.

Slides:



Advertisements
Similar presentations
Fong (Fantine) Ngan and DaeWon Byun IMAQS, Department of Earth Sciences, University of Houston 7 th Annual CMAS Conference, October 6th, 2008.
Advertisements

Click to edit Master title style Click to edit Master subtitle style 1 Modeling of 1,3-Butadiene for Urban and Industrial Areas B. Rappenglück and B. Czader.
Bay breeze enhanced air pollution event in Houston, Texas during the DISCOVER-AQ field campaign Christopher P. Loughner (University of Maryland) Melanie.
Photo image area measures 2” H x 6.93” W and can be masked by a collage strip of one, two or three images. The photo image area is located 3.19” from left.
Three-State Air Quality Study (3SAQS) Three-State Data Warehouse (3SDW) 2008 CAMx Modeling Model Performance Evaluation Summary University of North Carolina.
Havala O. T. Pye 1, Rob Pinder 1, Ying Xie 1, Deborah Luecken 1, Bill Hutzell 1, Golam Sarwar 1, Jason Surratt 2 1 US Environmental Protection Agency 2.
Improving the Representation of Atmospheric Chemistry in WRF William R. Stockwell Department of Chemistry Howard University.
The Framework of Modeling SOA Formation from Toluene Oxidation Di Hu and Richard Kamens Department of Environmental Sciences and Engineering, University.
1 Surface nitrogen dioxide concentrations inferred from Ozone Monitoring Instrument (OMI) rd GEOS-Chem USERS ` MEETING, Harvard University.
Ozone Production Efficiency in the Baltimore/Washington Urban Plume Presentation by Linda Hembeck Co-Authors: Christopher Loughner, Timothy Vinziguerra,
Evaluation of the AIRPACT2 modeling system for the Pacific Northwest Abdullah Mahmud MS Student, CEE Washington State University.
Office of Research and Development Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory Simple urban parameterization for.
6. Atmospheric Photochemical Reactions
The Sensitivity of Aerosol Sulfate to Changes in Nitrogen Oxides and Volatile Organic Compounds Ariel F. Stein Department of Meteorology The Pennsylvania.
Texas Rural Air Monitoring Sites Sonia Uribe November 16, 2004.
Simple Chemical modeling of ozone sensitivity
Air Resources Laboratory Yunsoo Choi 12, Daewon Byun 1, Pius Lee 1, Rick Saylor 1, Ariel Stein 12, Daniel Tong 12, Hyun-Cheol Kim 12, Fantine Ngan 13,
Xuexi Tie Xu Tang,Fuhai Geng, and Chunsheng Zhao Shanghai Meteorological Bureau Atmospheric Chemistry Division/NCAR Peking University Understand.
Midterm Matters any appeals regarding the test must be communicated to Dr. Gentleman by THURSDAY, NOVEMEBER 4 Next week: Lab.
STUDY OF SOURCE ATTRIBUTION OF UNSATURATED HYDROCARBONS FOR OZONE PRODUCTION IN THE HOUSTON-GALVESTON AREA WITH THE EXTENDED SAPRC99 CHEMICAL MECHANISM.
Examination of the impact of recent laboratory evidence of photoexcited NO 2 chemistry on simulated summer-time regional air quality Golam Sarwar, Robert.
COMPARISON OF LINK-BASED AND SMOKE PROCESSED MOTOR VEHICLE EMISSIONS OVER THE GREATER TORONTO AREA Junhua Zhang 1, Craig Stroud 1, Michael D. Moran 1,
Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Evaluation and Intercomparison of N.
Urban Air Pollution Public and Environmental Health Concerns –Elevated levels of toxic compounds Regional and Global Impacts –Background Chemistry and.
Earth&Atmospheric Sciences, Georgia Tech Modeling the impacts of convective transport and lightning NOx production over North America: Dependence on cumulus.
1 Using Hemispheric-CMAQ to Provide Initial and Boundary Conditions for Regional Modeling Joshua S. Fu 1, Xinyi Dong 1, Kan Huang 1, and Carey Jang 2 1.
Air Quality Impacts from a Potential Shale Gas Emissions Scenario - Photochemical Modeling of Ozone Concentrations in Central North Carolina Presented.
Copyright © 2014 R. R. Dickerson & Z.Q. Li 1 Spectroscopy and Photochemistry AOSC 620 R. Dickerson Fall 2015.
Soontae Kim and Daewon W. Byun Comparison of Emission Estimates from SMOKE and EPS2 Used for Studying Houston-Galveston Air Quality Institute for Multidimensional.
Presentation by: Dan Goldberg Co-authors: Tim Vinciguerra, Linda Hembeck, Sam Carpenter, Tim Canty, Ross Salawitch & Russ Dickerson 13 th Annual CMAS Conference.
10/28/2014 Xiangshang Li, Yunsoo Choi, Beata Czader Earth and Atmospheric Sciences University of Houston The impact of the observational meteorological.
Air Resources Laboratory CMAS meeting Chapel Hill, North Carolina Yunsoo Choi 1,2, Hyuncheol Kim 1,2, Daniel Tong 1,2, Pius Lee 1, Rick Saylor 3, Ariel.
Nitrogen Oxide Emissions Constrained by Space-based Observations of NO 2 Columns University of Houston Amir Souri, Yunsoo Choi, Lijun Diao & Xiangshang.
Wildland Fire Impacts on Surface Ozone Concentrations Literature Review of the Science State-of-Art Ned Nikolov, Ph.D. Rocky Mountain Center USDA FS Rocky.
Traffic Related Emissions of Radical Precursors HCHO and HONO in Los Angeles During CalNex S. Alvarez 1, B. Rappenglück 1, P.R. Veres 2,3, J.M. Roberts.
An Evaluation of the Influence of the Morning Residual Layer on Afternoon Ozone Concentrations in Houston Using Ozonesonde Data.
Evidence for an increase in the photochemical lifetime of ozone in the eastern United States Presented at the 14 th CMAS Meeting Wednesday October 7 th,
2012 CMAS meeting Yunsoo Choi, Assistant Professor Department of Earth and Atmospheric Sciences, University of Houston NOAA Air quality forecasting and.
Applications of Models-3 in Coastal Areas of Canada M. Lepage, J.W. Boulton, X. Qiu and M. Gauthier RWDI AIR Inc. C. di Cenzo Environment Canada, P&YR.
1. How is model predicted O3 sensitive to day type emission variability and morning Planetary Boundary Layer rise? Hypothesis 2.
Office of Research and Development Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory Simple urban parameterization for.
Chemical Condition and Surface Ozone in Urban Cities of Texas During the Last Decade: Observational Evidence from OMI, CAMS, and Model Analysis Yunsoo.
Department of Chemistry CHEM1020 General Chemistry *********************************************** Instructor: Dr. Hong Zhang Foster Hall, Room 221 Tel:
Yunseok Im and Myoseon Jang
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.
Peak 8-hr Ozone Model Performance when using Biogenic VOC estimated by MEGAN and BIOME (BEIS) Kirk Baker Lake Michigan Air Directors Consortium October.
Center for Environmental Research and Technology/Environmental Modeling University of California at Riverside Data Needs for Evaluation of Radical and.
Impact of Temporal Fluctuations in Power Plant Emissions on Air Quality Forecasts Prakash Doraiswamy 1, Christian Hogrefe 1,2, Eric Zalewsky 2, Winston.
W. T. Hutzell 1, G. Pouliot 2, and D. J. Luecken 1 1 Atmospheric Modeling Division, U. S. Environmental Protection Agency 2 Atmospheric Sciences Modeling.
Emission reductions needed to meet proposed ozone standard and their effect on particulate matter Daniel Cohan and Beata Czader Department of Civil and.
Visualizing Winter Nitrate Formation Using CMAQ Process Analysis Charles Stanier – University of Iowa CENTER FOR.
Comparison between Forecasting and Retrospective Air Quality Simulations of 2006 TexAQS-II Daewon W. Byun* D.-G. Lee, F. Ngan, H.-C. Kim, B. Czader Arastoo.
Georgia Institute of Technology Evaluation of the 2006 Air Quality Forecasting Operation in Georgia Talat Odman, Yongtao Hu, Ted Russell School of Civil.
Atmospheric Chemistry and Transport Science Questions.
Ozone Budget From: Jacob. Ozone in the Atmosphere Lifetime: –~1 month –Highly variable – dependent on season, latitude, altitude, etc. Background concentrations:
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.
Ship emission effect on Houston Ship Channel CH2O concentration ——study with high resolution model Ye Cheng.
Off-line Air Quality Modeling Paradigms:
Use of Satellite Data for Georgia’s Air Quality Planning Activities Tao Zeng and James Boylan Georgia EPD – Air Protection Branch TEMPO Applications.
Workshop on Air Quality Data Analysis and Interpretation
SMOG by Emre YAZICIOĞLU 1140 , 12 / C.
The 96th AMS Annual Meeting
16th Annual CMAS Conference
Charles University in Prague
Paul Kelley1,2, Winston Luke2, Xinrong Ren1,2
Shell Center for Sustainable Development Seminar
17th Annual CMAS Conference
Characteristics of Urban Ozone Formation During CAREBEIJING-2007 Experiment Zhen Liu 04/21/09.
Satellite Remote Sensing of Ozone-NOx-VOC Sensitivity
Space-based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions Randall Martin Aaron Van Donkelaar Arlene Fiore.
Presentation transcript:

Sensitivity to changes in HONO emissions from mobile sources simulated for Houston area Beata Czader, Yunsoo Choi, Lijun Diao University of Houston Department of Earth and Atmospheric Sciences

Background University of Houston 1.Nitrous acid (HONO) is an important source of hydroxyl radical (OH), which plays a crucial role in oxidation of volatile organic compounds (VOCs) leading to the formation of ozone. Czader et al., JGR, 2013 CMAQ results for SHARP, 2009

Background University of Houston 2.Accurate estimation of HONO in air quality modeling is important as it affects predictions of HO x (OH + HO 2 ) as well as ozone concentrations. 3.SMOKE estimates HONO emissions based on the HONO/NOx ratio derived from the tunnel studies done in 2001.

HONO sources Emissions from combustion processes 1. Gas phase formation OH + NO → HONO 2. Heterogeneous formation 2 NO 2 + H 2 O → HONO + HNO 3 can be parameterized as NO 2 → HONO with reaction rate coefficient 3. Photolytic sources Experiments show enhanced HONO formation when the sunlight is available, suggested uptake coefficient r = 2∙10 -5 with dependence on light intensity Based on Kurtenbach et al.,2001. Direct emissionsChemical formation University of Houston r = 1 – 5 ∙10 -6 r = 1∙10 -6 used in CMAQ

HONO partitioning in CMAQ University of Houston Czader et al., ACP, 2012 CMAQ results TexAQS 2006 emissions are mostly daytime source of HONO (orange) heterogeneous formation is the main nighttime source (green) gas phase formation is a midday source of HONO (purple) Emissions: 27% - morning 50% - around noontime

University of Houston Czader et al., ACP, 2012 TexAQS 2006 red – gas phase HONO source (CMAQ 4.6 and earlier versions) blue – gas phase, emissions, and heterogeneous formation (CMAQ 4.7 and later versions) Sarwar et al., AE, 2008; Foley et al., GMD, 2010 yellow – gas phase, emissions, and heterogeneous formation + photolytic source CMAQ modeling of HONO for Houston

University of Houston Czader et al., ACP, 2012 CMAQ modeling of HONO for Houston

University of Houston Czader et al., ACP, 2012 TexAQS 2006 CMAQ modeling of HONO for Houston

University of Houston Czader et al., ACP, 2012 TexAQS 2006 CMAQ modeling of HONO for Houston

University of Houston Czader et al., ACP, 2012 TexAQS 2006

HONO measurements in Houston University of Houston Rappenglueck et al., JAWMA, 2013 reports HONO measurement in Houston. and suggest much higher HONO/NO x emissions ratio than Kurtenbach et al Highway Junction I-59 South/610 in Houston Partial view of where the measurements were taken

HONO measurements in Houston University of Houston 1.7*10 -3 (±0.9*10 -3 ) r 2 =0.75 based on all data, July 15 – Oct. 15, *10 -3 r 2 =0.88 for Sep. 28, 2009 In order to determine traffic related emissions only a subset of 10-min averaged data was used which met the following conditions: 1) weekdays 2) rush hour time 4:00-8:00 a.m. CST 3) global radiation < 10 Wm-2 4) PAN < 50 pptv 5) no precipitation 6) RH > 80% Rappenglueck et al., JAWMA, 2013

Goals University of Houston 1.Apply the latest HONO/NO x ratio in estimating emissions of HONO from mobile sources; 2.Perform air quality simulations with the CMAQ model; 3.Evaluate the effect of changing mobile emissions on HONO predictions as well as on O 3 and HO x mixing ratios.

Methodology University of Houston Time period: September 2013, same as DISCOVER AQ in Houston Emissions: 2008 NEI processed with SMOKE v. 3.1 Meteorological parameters: WRF v. 3.5 driven by NAM for AQF and re-simulated with NARR data AQM: CMAQ v , cb05tucl_ae5_aq chemical mechanism Poster: THE EVALUATION OF AIR QUALITY FORECASTING SYSTEMS BASED ON WRF-CMAQ AND WRF-CHEM OVER HOUSTON DURING THE DISCOVER-AQ HOUSTON: SURFACE O3 AND PM2.5 by Lijun Diao, Yunsoo Choi, Beata Czader, Sunyeon Choi, Joanna Joiner, Hyuncheol Kim

HONO emissions University of Houston Speciation profiles for mobile sources (gspro file): NO X → NO 2 → 9.2% → 8.4% NO X → NO → 90.0% → 90.0% NO X → HONO → 0.8% → 1.6% HONO/NO x = 8*10 -3 base case HONO/NO x =1.6*10 -3 sensitivity case

Evaluation of modeling results: CO University of Houston sub-urban site urban/industrial site

Evaluation of modeling results: NOx University of Houston urban site sub-urban site

Evaluation of ozone predictions University of Houston September 23 – 27, 2013 (25-26 are high ozone days) Due to too high NOx morning time ozone is underpredited in the morning Afternoon values well captured by the model Background: CMAQ values Dots: CAMS measurements (not validated) provided by M. Estes, TCEQ

HONO mixing ratios University of Houston Base caseDifferences in HONO

Effect of increased HONO on OH radical University of Houston Higher daytime OH from additional HONO case

Effect of increased HONO on O 3 University of Houston Difference occurs in the morning ~3%, changes to the afternoon peak are marginal

Effect on other pollutants University of Houston Higher daytime mixing ratios of PAN and HNO3 from additional HONO The maximum difference occurs in the morning and reaches ~5% of PAN value, and ~2% for HNO3

Summary University of Houston  Recent measurement studies in Houston suggest that HONO mobile emissions are higher than currently implemented in AQM.  Increasing (doubling) HONO emissions resulted in up too 1 ppb higher HONO mixing ratios.  Additional HONO emissions from mobile sources resulted in slightly higher daytime values of hydroxyl radical and consequently other pollutants, such as ozone, PAN, and HNO3, especially during morning times.  NEI2008 overpredicts NO x emissions and is being adjusted based on CAMS and remote sensing data.