U.S. EPA Office of Research & Development October 30, 2013 Prakash V. Bhave, Mary K. McCabe, Valerie C. Garcia Atmospheric Modeling & Analysis Division.

Slides:



Advertisements
Similar presentations
A Web-based Community Approach to Model Evaluation using AMET Saravanan Arunachalam 1, Nathan Rice 2 and Pradeepa Vennam 1 1 Institute for the Environment.
Advertisements

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Changes in U.S. Regional-Scale Air.
Georgia Chapter of the Air & Waste Management Association Annual Conference: Improved Air Quality Modeling for Predicting the Impacts of Controlled Forest.
COMPARATIVE MODEL PERFORMANCE EVALUATION OF CMAQ-VISTAS, CMAQ-MADRID, AND CMAQ-MADRID-APT FOR A NITROGEN DEPOSITION ASSESSMENT OF THE ESCAMBIA BAY, FLORIDA.
Halûk Özkaynak US EPA, Office of Research and Development National Exposure Research Laboratory, RTP, NC Presented at the CMAS Special Symposium on Air.
CO budget and variability over the U.S. using the WRF-Chem regional model Anne Boynard, Gabriele Pfister, David Edwards National Center for Atmospheric.
An initial linkage of the CMAQ modeling system at neighborhood scales with a human exposure model Jason Ching/Thomas Pierce Air-Surface Processes Modeling.
Three-State Air Quality Study (3SAQS) Three-State Data Warehouse (3SDW) 2008 CAMx Modeling Model Performance Evaluation Summary University of North Carolina.
Halûk Özkaynak US EPA, Office of Research and Development National Exposure Research Laboratory, RTP, NC Presented at the CMAS Special Symposium on Air.
Office of Research and Development National Exposure Research Laboratory CMAS Special Session on Human Health October 13, 2010 Combining Models and Observations.
Jenny Stocker, Christina Hood, David Carruthers, Martin Seaton, Kate Johnson, Jimmy Fung The Development and Evaluation of an Automated System for Nesting.
1 icfi.com | 1 HIGH-RESOLUTION AIR QUALITY MODELING OF NEW YORK CITY TO ASSESS THE EFFECTS OF CHANGES IN FUELS FOR BOILERS AND POWER GENERATION 13 th Annual.
Operational Air Quality and Source Contribution Forecasting in Georgia Georgia Institute of Technology Yongtao Hu 1, M. Talat Odman 1, Michael E. Chang.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling Division, Applied Modeling Research Branch October 8, 2008.
Modeling the Co-Benefits of Carbon Standards for Existing Power Plants STI-6102 Stephen Reid, Ken Craig, Garnet Erdakos Sonoma Technology, Inc. Jonathan.
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.
Task Force on Health Recent results - Particulate matter Michal Krzyzanowski TFH Chair Head, Bonn Office European Centre for Environment and Health WHO.
Examination of the impact of recent laboratory evidence of photoexcited NO 2 chemistry on simulated summer-time regional air quality Golam Sarwar, Robert.
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
Sensitivity of top-down correction of 2004 black carbon emissions inventory in the United States to rural-sites versus urban-sites observational networks.
Importance of Lightning NO for Regional Air Quality Modeling Thomas E. Pierce/NOAA Atmospheric Modeling Division National Exposure Research Laboratory.
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.
CMAS special session Oct 13, 2010 Air pollution exposure estimation: 1.what’s been done? 2.what’s wrong with that? 3.what can be done? 4.how and what to.
Impacts of Biomass Burning Emissions on Air Quality and Public Health in the United States Daniel Tong $, Rohit Mathur +, George Pouliot +, Kenneth Schere.
Fine scale air quality modeling using dispersion and CMAQ modeling approaches: An example application in Wilmington, DE Jason Ching NOAA/ARL/ASMD RTP,
Evaluation of the CMAQ Model for Size-Resolved PM Composition Prakash V. Bhave, K. Wyat Appel U.S. EPA, Office of Research & Development, National Exposure.
On the Model’s Ability to Capture Key Measures Relevant to Air Quality Policies through Analysis of Multi-Year O 3 Observations and CMAQ Simulations Daiwen.
Modeling as an exposure estimation approach for use in epidemiologic studies Part 2: Example applications KL Dionisio 1, LK Baxter 1, V Isakov 1, SE Sarnat.
Impacts of MOVES2014 On-Road Mobile Emissions on Air Quality Simulations of the Western U.S. Z. Adelman, M. Omary, D. Yang UNC – Institute for the Environment.
Predicting Long-term Exposures for Health Effect Studies Lianne Sheppard Adam A. Szpiro, Johan Lindström, Paul D. Sampson and the MESA Air team University.
Model Evaluation Comparing Model Output to Ambient Data Christian Seigneur AER San Ramon, California.
2012 CMAS meeting Yunsoo Choi, Assistant Professor Department of Earth and Atmospheric Sciences, University of Houston NOAA Air quality forecasting and.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Using Dynamical Downscaling to Project.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.
Source Attribution Modeling to Identify Sources of Regional Haze in Western U.S. Class I Areas Gail Tonnesen, EPA Region 8 Pat Brewer, National Park Service.
A Comparative Performance Evaluation of the AURAMS and CMAQ Air Quality Modelling Systems Steven C. Smyth, Weimin Jiang, Helmut Roth, and Fuquan Yang ICPET,
Session 5, CMAS 2004 INTRODUCTION: Fine scale modeling for Exposure and risk assessments.
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.
Lisa K. Baxter, Kathie L. Dionisio, Janet Burke, and Halûk Özkaynak National Exposure Research Laboratory, U.S. EPA Modeling as an exposure estimation.
William G. Benjey* Physical Scientist NOAA Air Resources Laboratory Atmospheric Sciences Modeling Division Research Triangle Park, NC Fifth Annual CMAS.
C. Hogrefe 1,2, W. Hao 2, E.E. Zalewsky 2, J.-Y. Ku 2, B. Lynn 3, C. Rosenzweig 4, M. Schultz 5, S. Rast 6, M. Newchurch 7, L. Wang 7, P.L. Kinney 8, and.
GOING FROM 12-KM TO 250-M RESOLUTION Josephine Bates 1, Audrey Flak 2, Howard Chang 2, Heather Holmes 3, David Lavoue 1, Mitchel Klein 2, Matthew Strickland.
Evaluation of Models-3 CMAQ I. Results from the 2003 Release II. Plans for the 2004 Release Model Evaluation Team Members Prakash Bhave, Robin Dennis,
Exposure Assessment for Health Effect Studies: Insights from Air Pollution Epidemiology Lianne Sheppard University of Washington Special thanks to Sun-Young.
Opening Remarks -- Ozone and Particles: Policy and Science Recent Developments & Controversial Issues GERMAN-US WORKSHOP October 9, 2002 G. Foley *US EPA.
Robert W. Pinder, Alice B. Gilliland, Robert C. Gilliam, K. Wyat Appel Atmospheric Modeling Division, NOAA Air Resources Laboratory, in partnership with.
Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Mercury, Ozone, and Particulate Matter US EPA / OAQPS / AQAD / AQMG Sharon Phillips, Kai Wang,
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division October 21, 2009 Evaluation of CMAQ.
Evaluation of CMAQ Driven by Downscaled Historical Meteorological Fields Karl Seltzer 1, Chris Nolte 2, Tanya Spero 2, Wyat Appel 2, Jia Xing 2 14th Annual.
AoH/MF Meeting, San Diego, CA, Jan 25, 2006 WRAP 2002 Visibility Modeling: Summary of 2005 Modeling Results Gail Tonnesen, Zion Wang, Mohammad Omary, Chao-Jung.
Operational Evaluation and Model Response Comparison of CAMx and CMAQ for Ozone & PM2.5 Kirk Baker, Brian Timin, Sharon Phillips U.S. Environmental Protection.
1 Preliminary evaluation of the 2002 Base B1 CMAQ simulation: Temporal Analysis A more complete statistical evaluation, including diurnal variations, of.
Response of fine particles to the reduction of precursor emissions in Yangtze River Delta (YRD), China Juan Li 1, Joshua S. Fu 1, Yang Gao 1, Yun-Fat Lam.
Georgia Institute of Technology Evaluation of the 2006 Air Quality Forecasting Operation in Georgia Talat Odman, Yongtao Hu, Ted Russell School of Civil.
Fine Scale Modeling of Ozone Exposure Estimates using a Source Sensitivity Approach Cesunica E. Ivey, Lucas Henneman, Cong Liu, Yongtao T. Hu, Armistead.
Modeling as an exposure estimation approach for use in epidemiologic studies Part 2: Example applications KL Dionisio 1, LK Baxter 1, V Isakov 1, SE Sarnat.
Advances in Support of the CMAQ Bidirectional Science Option for the Estimation of Ammonia Flux from Agricultural cropland Ellen Cooter U.S. EPA, National.
Exposure Prediction and Measurement Error in Air Pollution and Health Studies Lianne Sheppard Adam A. Szpiro, Sun-Young Kim University of Washington CMAS.
7. Air Quality Modeling Laboratory: individual processes Field: system observations Numerical Models: Enable description of complex, interacting, often.
Daiwen Kang 1, Rohit Mathur 2, S. Trivikrama Rao 2 1 Science and Technology Corporation 2 Atmospheric Sciences Modeling Division ARL/NOAA NERL/U.S. EPA.
N Engl J Med Jun 29;376(26): doi: 10
Simulation of PM2.5 Trace Elements in Detroit using CMAQ
RD Evaluation and Comparison OF Methods to Construct Air Quality Fields for Exposure Assessment haofei yu, jim mulholland, howard chang, ran huang,
Improving an Air Quality Decision Support System through the Integration of Satellite Data with Ground-Based, Modeled, and Emissions Data Demonstration.
Statistical Methods for Model Evaluation – Moving Beyond the Comparison of Matched Observations and Output for Model Grid Cells Kristen M. Foley1, Jenise.
17th Annual CMAS Conference, Chapel Hill, NC
J. Burke1, K. Wesson2, W. Appel1, A. Vette1, R. Williams1
Improving an Air Quality Decision Support System through the Integration of Satellite Data with Ground-Based, Modeled, and Emissions Data Demonstration.
Update on 2016 AQ Modeling by EPA
REGIONAL AND LOCAL-SCALE EVALUATION OF 2002 MM5 METEOROLOGICAL FIELDS FOR VARIOUS AIR QUALITY MODELING APPLICATIONS Pat Dolwick*, U.S. EPA, RTP, NC, USA.
Presentation transcript:

U.S. EPA Office of Research & Development October 30, 2013 Prakash V. Bhave, Mary K. McCabe, Valerie C. Garcia Atmospheric Modeling & Analysis Division U.S. EPA, Office of Research & Development CMAS Conference Chapel Hill, NC October 28 – 30, 2013 Estimation of human exposure to PM 2.5 components in U.S. metro areas Using routine measurements and CMAQ!

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Take-Home Messages from CMAS 2010 Special Session Land-use Regression (LUR) is most popular method for estimating exposure beyond central-site monitor -L.Sheppard AQ Modelers should focus on ↑ spatial resolution; temporal not a major need –M.Brauer Solutions: Hybrid of CMAQ+AERMOD, CMAQ+RLINE Run CMAQ at finer scales (e.g., 4km or 1km) But… these don’t take advantage of our strength in scales within-city monitoring of PM 2.5 and O 3 is fairy dense – how much value can our models add over LUR or spatial kriging?

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division 2 Ref. Dominici, F. et al. JAMA 2006 Health Impact of PM 2.5 Varies by Region

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Within-Region Variability 3 Ref. Franklin, M. et al. JESEE 2007

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Recent Investigations Baxter, Franklin, Ozkaynak, et al. The use of improved exposure factors in the interpretation of PM 2.5 epidemiological results, Air Qual. Atmos. Health, Baxter, Duvall, & Sacks. Examining the effects of air pollution composition on within region differences in PM 2.5 mortality risk estimates, JESEE, Major obstacle: Routine observations of PM 2.5 composition are very limited. Of 139 U.S. cities with chemical speciation network (CSN) sites, only 15 have >1 site.

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Hypothesis For many locations and chemical species, the PM 2.5 composition at a single CSN site is an inadequate estimate of the ambient concentrations across the metropolitan area, for assessing the compositional effects on within-region differences in PM 2.5 mortality risk estimates 5

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Methods Ambient Measurements –PM 2.5 CSN measurements in 2006 (24h obs, 1-in-3 or 1-in-6) –Subset data which are only site in their core-based statistical area (CBSA) –Compute annual avg conc of each species at each site Model Simulation –CMAQ v5.0.1 with default options (e.g., CB05tucl, AERO6, ACM2) –2006 calendar-year simulation –12km ConUS domain – 459 ×299 × 35 layers –Emissions: evaluation version D of 2008 NEI w. year-specific fire, mobile, biogenic, & point EGU –Meteorology: WRF v3.4 –Computed annual avg con for each PM 2.5 species (n = 15) in each surface-layer grid cell (n = 137,241) from hourly CMAQ output –Multiplicative bias correction for each CBSA and species –Excluded cases where model & obs differed by > 3× Population Data –2000 Census block-level data projected to 2005 and aggregated to 12km CMAQ grid cells 6

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division CSN Measurement 4.16 µg/m 3 Raw Data Example of Results Phoenix-Mesa-Glendale contains exactly 1 CSN monitor. At that site, the annual-average OC = 4.16 µg/m 3 in 2006.

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Area average = 0.71 µg/m 3 Raw Data CMAQ Model Output Example of Results CSN Measurement 4.16 µg/m 3 CMAQ model provides an estimate of spatial variability in OC across the metro area. After bias correction, avg. conc. = 0.71 µg/m 3. much lower than CSN measurement! 3.0 – – – – – 0.5

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Population Density (km -2 ) Area average = 0.71 µg/m 3 Raw Data CMAQ Model Output 0 – – – – – – – – – 0.5 Example of Results CSN Measurement 4.16 µg/m 3 Population average = 2.24 µg/m 3 But population density is correlated with OC concentration. Accounting for spatial variation in air concentrations & population density, we obtain a more accurate estimate of the average exposure across this metro area. Population-Averaged Exposure Measurement Error (due to spatial variability) = µg/m 3 *Using CMAQ, we calculate this error for 14 species across all metro areas with a single CSN site

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Exposure Measurement Error for Organic Carbon in PM (µg/m 3 ) *Substantial inter-city variability in exposure measurement error can now be accounted for in large-scale, population-based epidemiological studies SO4 err = µg/m 3 in Baton Rouge, LA NO3 err = µg/m 3 in Riverside, CA Ti err = -8.1 ng/m 3 in Colorado Springs

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division Summary & Future Work Exposure measurement error due to spatial variability in ambient concs was estimated fro 15 PM 2.5 species in >100 metro areas across the U.S. Error was typically positive (871 out of 1280 cases studied), because most CSN monitors are in urban center Errors can be quite large (e.g., OC err = μg/m 3 in Phoenix) Future work: incorporate these exposure errors in future epi studies that investigate the influence of PM 2.5 composition on mortality risk. 11