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Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Mercury, Ozone, and Particulate Matter. US EPA / OAQPS / AQAD / AQMG. Sharon Phillips, Kai Wang, Carey Jang, Norm Possiel, Madeleine Strum, and Tyler Fox. 7 th Annual CMAS Conference – October 6-8, 2008. Outline.

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evaluation of 2002 multi pollutant platform air toxics mercury ozone and particulate matter

Evaluation of 2002 Multi-pollutant Platform: Air Toxics, Mercury, Ozone, and Particulate Matter

US EPA / OAQPS / AQAD / AQMG

Sharon Phillips, Kai Wang, Carey Jang, Norm Possiel, Madeleine Strum, and Tyler Fox

7th Annual CMAS Conference – October 6-8, 2008

outline
Outline
  • 2002 modeling platform and components
    • Criteria Air Pollutants Only “CAP-only”
    • Criteria Air Pollutants + Hazardous Air Pollutants “CAP+HAP”
  • Model Evaluation of 2002 CMAQ CAP & CAP+HAP predictions with ambient data
  • Possible causes for model performance issues and analyses to further explore
2002 modeling platform
2002 Modeling Platform
  • Developed jointly between OAQPS & ORD
  • Promotes multi-pollutant assessments
    • Integrated inventory (criteria and air toxics)
    • “One-atmosphere” CMAQ with AERMOD / Fine scale grid modeling for selected urban areas (e.g. Detroit MP Study)
    • Currently moving towards 2005 MP modeling platform (CMAQv4.7)
  • Provides consistency, transparency, and efficient development of baselines for:
    • OAR regulatory assessments
    • CMAQ evaluations & research efforts by ORD
    • Accountability efforts across EPA
    • Public health & exposure assessments
  • Provides data and examples for others (e.g., State/local agencies)
    • Emissions data: http://www.epa.gov/ttn/chief/emch/index.html#2002
components of 2002 mp modeling platform
Components of 2002 MP Modeling Platform
  • 2002 National Emissions Inventory (NEI) v3
    • Criteria (CAPs) and HAPs
  • 2002 Meteorological Data
    • MM-5 and MCIP v3.1
    • 36km US, 12 km EUS, 12km WUS (from WRAP)
  • Emissions Models, Tools and Ancillary Data
    • Emissions Modeling Framework (EMF)
    • Emissions processing: SMOKE version 2.3.2
    • Biogenics: BEIS 3.13
    • Onroad/nonroad emissions: NMIM (w/ MOBILE6 & NONROAD 2005)
    • EGU projections: IPM 3.0
    • Ancillary data: speciation, temporal, spatial allocation
  • Boundary Condition Concentrations
    • 2002 simulations of GEOS-Chem: 2° x 2° grids & 30 layers up to stratosphere
    • 36-km US domain for CAPS, mercury, and some HAPS (e.g. formaldehyde)
    • For toxics not simulated by GEOS-Chem we used concs based on remote measurements and literature values (joint effort b/n AQAG & ORD)
  • Air Quality Model
    • CMAQ v4.6 (base CAP only & “proto-type” multi-pollutant version)
    • CB-05 chemical mechanism with mercury and chlorine chemistry
    • Ozone, PM, and additional 38 HAPs
slide5

Model Domains

36km Domain Boundary

12km West Domain Boundary

12km East Domain Boundary

model performance analysis
Model Performance Analysis
  • PM2.5 Species: SO4, NO3, TNO3, OC, EC, SO2
    • STN, CASTNet, IMPROVE, NADP
  • Ozone: 1 hr-max & 8 hr-max
    • AQS, SEARCH
  • HAPs: Mercury, Formaldehyde, Acetaldehyde, Benzene, etc., + metals
    • MDN, NATTS, STN, IMPROVE
  • Graphics and Statistics: AMET 1.1 (www.cmascenter.org)
    • hourly, daily, monthly, seasonal & annual
    • Spatial tile maps comparing observed and predicted species concentrations/deposition
    • Scatter plots of observations vs predictions
    • Time-series plots of observations vs predictions
    • Statistics:
      • Normalized Mean Bias (NMB) / Fractional Bias (FB)
      • Normalized Mean Error (NME) / Fractional Error (FE)
      • Root Mean Square Error (RMSE)
slide7
Examples of Model Performance For Selected CAPsBased on 2002 CAP-only CMAQ Modeling at 12 km EUS & WUS
highlights of 2002 model evaluation for caps
Highlights of 2002 Model Evaluation for CAPs
  • Ozone
    • Under predicted for 1-hr & 8-hr daily max. especially O3 > 60 ppb
  • Sulfate PM
    • Under predicted (~ up to 20%) in all seasons in the East & West
  • Sulfur Dioxide
    • Over predicted (~ 15 to 65%) in all seasons in the East & West
  • Nitrate PM
    • Over predicted (~ 5 to 40%) in Fall, Winter, and in northern areas of the East in the Spring
  • Organic Carbon
    • Over predicted in the North and under predicted in South and West in the Winter
    • Under predicted in all areas (~ 25 to 65%) in Fall, Spring & Summer
  • Elemental Carbon
    • Mostly over predicted in urban areas (~ 40%) in all seasons in the East and West
    • Mostly under predicted in rural areas (10 to > 40%) in all seasons in the East and West
slide9

12-km EUS

SO4 Wet Deposition2002 Total

NADP

12-km WUS

slide10

12-km EUS

NO3 Wet Deposition2002 Total

NADP

12-km WUS

cap hap vs cap only differences
CAP+HAP vs CAP-only differences
  • Emission Differences
    • Use HAPs for speciation for select sources
    • Use formaldehyde, acetaldehyde and methanol for all sources
    • Very small spatial/temporal profile differences in some geographic areas
  • Model Differences
    • Chlorine chemistry
    • Added air toxics
  • Differences in Predictions
    • Slight differences in Summer & Winter Ozone (Northeast, CA, UT)
    • Slight differences in Winter NO3 (Northeast, GA, UT)
    • Negligible differences in Summer SO4
    • Some differences in Winter & Summer Formaldehyde & Acetaldehyde
2002 july ozone cap hap cap only
2002 July Ozone: CAP+HAP – CAP-only

July: CAP+HAP slightly less ozone in Northeast, more ozone in Utah in vicinity of large source of chlorine emissions.

slide13

2002 January Formaldehyde: CAP+HAP – CAP-only

Difference in FORM Emissions

Difference in CMAQ FORM

CMAQ differences in winter appear to be due to differences in speciation of residential wood combustion CAP VOC vs formaldehyde in the HAP inventory (CAP << HAP).

slide14
Examples of Model Performance For Selected HAPsBased on 2002 CAP+HAP CMAQ Modeling for the 12 km EUS
slide15

2002 Formaldehyde – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = - 51.3% NME = 55.2%

NMB = - 31.5% NME = 64.1%

High observations – may be due to short-term releases, near-source monitors, or instrument issues

slide16

2002 Formaldehyde – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = - 15.7% NME = 56.9%

NMB = - 35.6% NME = 42.1%

Same data, but without high observed values

slide17

2002 Acetaldehyde – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = 54.9% NME = 77.9%

NMB = - 27.8% NME = 44.2%

Slightly higher concs. observed at sites in MA & GA

slide18

2002 Benzene – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = 41.0% NME = 55.0%

NMB = - 27.3% NME = 58.3%

Slightly higher concs. observed at 3 sites in TX & 1 site IN

Slightly higher concs. observed at 1 site in TX & IN

slide19

2002 Lead PM2.5 – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = - 53.8% NME = 62.6%

NMB = - 33.8% NME = 54.6%

Slightly higher concentrations observed at Birmingham, AL

slide20

2002 Chromium PM2.5 – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = - 14.0% NME = 82.6%

NMB = - 47.3% NME = 72.3%

Slightly higher concentrations observed at same site in Birmingham, AL

slide21

2002 Carbon Tetrachloride – EUS 12km

Summer – Monthly Mean

Winter – Monthly Mean

NMB = - 73.0% NME = 73.0%

NMB = - 59.7% NME = 60.6%

Influence of detection level in observations

slide22
Examples of Model Performance For Mercury Wet DepositionBased on 2002 CAP+HAP CMAQ Modeling at 12 km EUS
slide23

2002 Annual Mercury Wet Deposition

12 km EUS

NMB = - 10.1% NME = 23.2%

slide24

2002 Seasonal Hg Wet Deposition – EUS 12-km

Winter

Spring

Over-prediction in Winter & Spring

NMB = 30.7% NME = 43.4%

NMB = 15.1% NME = 32.7%

Fall

Summer

Under-prediction in Fall & Summer

NMB = -15.5% NME = 39.9%

NMB = -34.2% NME = 39.8%

identified technical issues with tools and data for mp analyses
Identified Technical Issues with Tools and Data for MP Analyses
  • Model performance for some non-ubiquitous HAPs is not as “good” as that for ozone and PM2.5
    • Uncertainties in monitoring methods
    • Limited measurements in time/space to characterize ambient concentrations (“local in nature”)
      • Given local nature of some toxics, fine scale modeling may be needed
    • Commensurability issues between measurements and model predictions
    • Emissions and science uncertainty issues may also affect model performance
    • Limited data for estimating intercontinental transport (i.e., Boundary Conditions)
      • Boundary estimates for some species are much higher than predicted values inside the domain
identified technical issues with tools and data for mp analyses1
Identified Technical Issues with Tools and Data for MP Analyses
  • Emissions-related issues
    • No available HAP inventories for Canada (except Hg) & Mexico
    • Inconsistencies between emissions factors for CAPs and HAPs
    • Criteria/HAP emissions are not easily integrated
      • Inconsistencies in CAP/HAP emissions reported by States
      • Inconsistencies between VOC speciation and HAP inventories – need for coordinated research effort
    • Periodic nature of some toxic releases that are not well characterized in our inventories
  • Uncertainties in science (e.g., mercury chemistry and re-emissions) and evolving science for various components of the modeling system
future efforts
Future Efforts
  • Continue model evaluation efforts and investigate performance issues in conjunction with ORD
  • Prepare 2002 Multi-Pollutant Platform Report
  • Complete 2005 Multi-Pollutant Modeling Platform
  • Provide feedback to the 2008 NEI integrated inventory process and future modeling platforms
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