Biogenic Emissions in Southeast Texas

Slides:

Advertisements

Similar presentations

Impacts of Emission from Power Plants on the Ambient Ozone Concentration in Southern Taiwan Yee-Lin Wu and Der Ming Tsai Department of Environmental Engineering.

Advertisements

Interpolations of Ambient Air Pollution Concentrations in Austin and Houston Heather Simon.

Dylan Millet Harvard University with

REDUCTION OF HIGHLY REACTIVE VOLATILE ORGANIC COMPOUNDS & VARIABLE EMISSIONS IN HOUSTON/GALVESTON: MONITORING, MODELING, MEASURING, RULEMAKING David Allen.

The Impact of Drought on Leaf Area Index (LAI) over Texas Ling Huang 12/04/2012.

Incorporation of the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) into CMAQ Yang Zhang, Betty K. Pun, Krish Vijayaraghavan,

Improving the Representation of Atmospheric Chemistry in WRF William R. Stockwell Department of Chemistry Howard University.

BlueSky-EM, SMOKE and Data Linkages Andy Holland Carolina Environmental Program Symposium on Climate, Forest Growth, Fire and Air Quality Interactions.

Discussion Space Research Centre. Urbanization and Industrialization: in 2008, more than half of humans live in cities UN Population Report 2007.

4-km AIRPACT vs 12-km AIRPACT Both with dynamic boundary conditions from MOZART-4 Figures created on 5/29/2011 (corrected corrupted JPROC input file)

Regional Air Quality Modeling: Long Range Global Change Simulations.

The Impact of Land Use Changes on the Future Tree Distributions Presented by Jin-Oh Kim Community and Regional Planning School of Architecture University.

Template CAMx Ancillary Input Development Chris Emery ENVIRON International Corporation, Novato CA November 14, 2012.

Simulation of Houston-Galveston Airshed Ozone Episode with EPA’s CMAQ Daewon Byun: PI Soontae Kim, Beata Czader, Seungbum Kim Emissions input Chemical.

Beta Testing of the SCICHEM-2012 Reactive Plume Model James T. Kelly and Kirk R. Baker Office of Air Quality Planning & Standards US Environmental Protection.

Xuexi Tie Xu Tang,Fuhai Geng, and Chunsheng Zhao Shanghai Meteorological Bureau Atmospheric Chemistry Division/NCAR Peking University Understand.

Krish Vijayaraghavan, Prakash Karamchandani Christian Seigneur AER San Ramon, CA 3rd Annual CMAS Models-3 Conference October 18-20, 2004 Chapel Hill, NC.

New York City Case Study: Methods of Analysis David J. Nowak USDA Forest Service Northeastern Research Station Syracuse, NY.

Modeling Aerosol Formation and Transport in the Pacific Northwest with the Community Multi-scale Air Quality (CMAQ) Modeling System Susan M. O'Neill Fire.

Weather forecasting began in the mid 1800’s when basic tools, like the thermometer and barometer, were invented Global Weather Reporting Weather observations.

Performance evaluation of isoprene in ozone modeling of Houston Mark Estes, Clint Harper, Jim Smith, Weining Zhao, and Dick Karp Texas Commission on Environmental.

The Net Effects of Vegetation on Air Quality June 28, 2001.

Mapping isoprene emissions from space Dylan Millet with

Template Evaluation of an Advanced Reactive Puff Model using Aircraft-based Plume Measurements Krish Vijayaraghavan, Prakash Karamchandani, Bart Brashers,

BIOGENIC VOC EMISSIONS ESTIMATION BY USING THE GLOBAL LAND COVER CHARACTERISTICS DATABASE Ozan Devrim Yay Anadolu University, Dept. Of Environmental Engineering,

Soontae Kim and Daewon W. Byun Comparison of Emission Estimates from SMOKE and EPS2 Used for Studying Houston-Galveston Air Quality Institute for Multidimensional.

Georgia Environmental Protection Division Uncertainty Analysis of Ozone Formation and Emission Control Responses using High-order Sensitivities Di Tian,

WRPLOT View & “Some” Wind Rose Uses Glenn Gehring, Technology Specialist III Tribal Air Monitoring Support Center

Estimating Biogenic VOC Emissions for the SCOS Domain Using the BEIGIS Model Michael Benjamin & Klaus Scott California Air Resources Board SCOS97 regional.

Request for LAOF facilities in support of SOAS: Southern Oxidant and Aerosol Study SOAS Science Objective: To quantify biogenic emissions and anthropogenic.

Meteorological Data Analysis Urban, Regional Modeling and Analysis Section Division of Air Resources New York State Department of Environmental Conservation.

Interannual variability in biogenic emissions driven by dynamic vegetation conditions Daniel Cohan (PI), Adetutu Aghedo, Erin Chavez-Figueroa, and Ben.

The GEOS-CHEM Simulation of Trace Gases over China Li ZHANG and Hong LIAO Institute of Atmospheric Physics Chinese Academy of Sciences April 24, 2008.

Assessment of the Impacts of Global Change on Regional U.S. Air Quality: A synthesis of climate change impacts on ground-level ozone An Interim Report.

CE-QUAL-W2 Shasta Modeling

William G. Benjey* Physical Scientist NOAA Air Resources Laboratory Atmospheric Sciences Modeling Division Research Triangle Park, NC Fifth Annual CMAS.

Effects of Emission Adjustments on Peak Ground-Level Ozone Concentration in Southeast Texas Jerry Lin, Thomas Ho, Hsing-wei Chu, Heng Yang, Santosh Chandru,

1. How is model predicted O3 sensitive to day type emission variability and morning Planetary Boundary Layer rise? Hypothesis 2.

May 22, UNDERSTANDING THE EFFECTIVENESS OF PRECURSOR REDUCTIONS IN LOWERING 8-HOUR OZONE CONCENTRATIONS Steve Reynolds Charles Blanchard Envair 12.

On/Off Operation of Carbon Capture Systems in the Dynamic Electric Grid On/Off Operation of Carbon Capture Systems in the Dynamic Electric Grid Rochelle.

1 Prakash Karamchandani 1, David Parrish 2, Lynsey Parker 1, Thomas Ryerson 3, Paul O. Wennberg 4, Alex Teng 4, John D. Crounse 4, Greg Yarwood 1 1 Ramboll.

Climatic implications of changes in O 3 Loretta J. Mickley, Daniel J. Jacob Harvard University David Rind Goddard Institute for Space Studies How well.

Global high-resolution marine isoprene emission derived from VIIRS-SNPP and MODIS-Aqua ocean color observations 1/25/2016Air Resources Laboratory1 Daniel.

Georgia Institute of Technology SUPPORTING INTEX THROUGH INTEGRATED ANALYSIS OF SATELLITE AND SUB-ORBITAL MEASUREMENTS WITH GLOBAL AND REGIONAL 3-D MODELS:

Peak 8-hr Ozone Model Performance when using Biogenic VOC estimated by MEGAN and BIOME (BEIS) Kirk Baker Lake Michigan Air Directors Consortium October.

MAPPING ISOPRENE EMISSIONS FROM SPACE USING OMI FORMALDEHYDE MEASUREMENTS Dylan B. Millet, Daniel J. Jacob, K. Folkert Boersma, Justin P. Parrella Atmospheric.

Top Down Emission Analyses Theme 17 th GEIA Conference Nov. 19, 2015 Alex Guenther Department of Earth System Science University of California, Irvine.

Sensitivity of Biogenic Emissions to Climate Jihee Song Global Physical Climatology December 5, 2006.

Dynamic Model Performance Evaluation Using Weekday-Weekend and Retrospective Analyses Air Quality Division Jim Smith and Mark Estes Texas Commission on.

BAY AREA MODELING Status of Modeling Work Technical Committee Meeting November 18, 2003 Bay Area Air Quality Management District and Environ Corp.

Sensitivity of PM 2.5 Species to Emissions in the Southeast Sun-Kyoung Park and Armistead G. Russell Georgia Institute of Technology Sensitivity of PM.

Aerosol simulation with coupled meteorology-radiation- chemistry model WRF/Chem over Europe.

Assessment and Calculations of Plume Rise for Forest Fires during Texas Air Quality Study period. Uarporn Nopmongcol Dept. of Chemical Engineering The.

Ship emission effect on Houston Ship Channel CH2O concentration ——study with high resolution model Ye Cheng.

Yuqiang Zhang1, Owen R, Cooper2,3, J. Jason West1

Ozone Transport Analysis Using Back-Trajectories and CAMx Probing Tools Sue Kemball-Cook, Greg Yarwood, Bonyoung Koo and Jeremiah Johnson, ENVIRON Jim.

The 96th AMS Annual Meeting

Photochemical Modeling of Industrial Flare Plumes with SCICHEM 3.1

Forecasting the Impacts of Wildland Fires

17th Annual CMAS Conference

CMAS 2018 conference Chi-tsan Wang

Lily Li, Qing Lu, Jingyu An, Cheng Huang

Sensitivity Analysis of Ozone in the Southeast

Jenny L. Perry and Patrick M. Owens Department of Chemistry

Increasing Canopy Cover in Urban Areas

Introduction to Meteorology

Georgia Institute of Technology

Georgia Institute of Technology

Diagnostic and Operational Evaluation of 2002 and 2005 Estimated 8-hr Ozone to Support Model Attainment Demonstrations Kirk Baker Donna Kenski Lake Michigan.

Climatic implications of changes in O3

Presentation transcript:

Biogenic Emissions in Southeast Texas Ji Hee Song Environmental and Water Resources Engineering University of Texas at Austin

Outline Characteristics of Isoprene Meteorological data from the TexAQS 2000 episode Biogenic emission model Time varying distribution of Isoprene emissions Comparison of emission by different model version My first task was to see the performance of CAMx model, comparing with observed data from NOAA aircraft. The time period of my interest was from August 25 to September 1 in 2000.

Isoprene Highly reactive hydrocarbon Mostly emitted by trees and plants ; oaks, popular, etc. Isoprene + NOx  Ozone formation

GloBEIS 3.0 (Global Biosphere Emissions and Interactions System version 3.0)

Inputs to GloBEIS 3.0 Meteorology based on MM5 outputs Satellited based cloud cover data Texas specific landcover data

Hourly Biogenic Emissions in tons Strongly related to sunlight year day hour Isoprene TMT OVC NOx 2000 238 31 22 2.5 1 30 2 29 21 2.4 3 20 4 28 5 27 19 2.3 6 26 7 73 8 147 39 2.6 9 206 44 2.8 10 253 48 34 2.9 11 292 52 37 3.1 12 319 55 3.3 13 328 57 40 3.4 14 316 41 3.5 15 290 58 3.6 16 234 17 146 18 53 47 33 42 38 35 25 2.7 23 24 Daily Totals 2603 999 713 68.6 Hourly Biogenic Emissions in tons Strongly related to sunlight For the temperature and cloud cover sensitivity, modified GloBEIS 2 was used. First the temperature,

Distribution on the ArcMap Isoprene emissions w/ Graduated colors Units : g/each cell

Hourly Based Isoprene Distribution 7:00 am 12:00 pm However, these four graphs show that varied temperature and cloud cover input data don’t shift the result much. Which indicates that the isoprene concentration is not very sensitive to temperature and cloud cover. 3:00 pm 5:00 pm

Comparison of model results (GloBEIS 3.0 vs. Modified GloBEIS 2.0) Older version of GloBEIS underpredicted the isoprene emissions The conclusion. Model is well predicted the isoprene concentration, however, the discrepancy is due to the meteorological data such as rapid changing wind direction. And anthropogenic sources, which can not be predicted by the model. And certain types of landcover data.

Special Thanks to.. Dr. David Maidment from University of Texas, Austin Dr. David T. Allen from University of Texas, Austin Alex Guenther from NCAR Christine Wiedinmyer from NCAR Mark Estes from ENVIRON Yosuke Kimura from University of Texas, Austin Matt Russell from University of Texas, Austin Gary McGaughey from University of Texas, Austin Special thanks to …

Questions?