Source-Specific Forecasting of Air Quality Impacts with Dynamic

Emissions Updating &Source Impact Reanalysis

Georgia Institute of Technology

Yongtao Hu1, Cesunica Ivey1, M. Talat Odman1, Peter Vasilakos, Michael E. Chang2 and Armistead G. Russell1

1School of Civil & Environmental Engineering,2Brook Byers Institute of Sustainable Systems

Georgia Institute of Technology

With thanks to Pius Lee and the NOAA ARL Forecasting Team

AQAST 9th Semi-Annual Meeting, June 3rd, 2015

Motivation

• Air Quality Forecasting– Source specific air quality impacts used in air quality

management• Both for “static” and, in the future, targeted “dynamic” air

quality management, and for health related public and individual exposure management.

– Current source specific air quality impact forecasting/hindcasting accuracy call for improvement.• Prediction with 3-D models relies on accuracy of emissions

– Uncertain (particularly for smaller sources)– Change with time

• Source impact assessment with chemical reanalysis– Desire to integrate advanced, hybrid source impact

assessment as part of chemical re-analysis and emissions updates

• Improving model performance– Most CTMs currently are biased low when simulating

organic aerosol (OA) during the summer– Linked to secondary (SOA) formation

• Experiments suggest biogenic SOA formation underestimatedGeorgia Institute of Technology

Objective• Provide information that can assist air quality

management and exposure assessment– Source impact forecasting in addition to air quality

forecasting• Critical for dynamic air quality management

– Improve air quality and source impact forecasting accuracy using near real time measurements through dynamic adjustment of emissions

– Improve source impact hindcasting • Utilize measurements (gases, PM, AOD and PM composition)

• Work with Georgia EPD, Forest Service, Georgia Forestry Commission, Atlanta Regional Commission, and EPA on use of the products and information

• Provide source-specific impacts as part of Tiger Team reanalysis

Georgia Institute of Technology

Source-Specific Air Quality Impact Forecasting

Georgia Institute of Technology

Hi-Res2 Forecasting System

Forecasting Air Quality for CONUS

(https://forecast.ce.gatech.edu open since November 28th,2014)

• Updated base emissions to 2011NEI

• WRF3.6.1 and CMAQv5.02 used

• 72-hour forecasts at 4-km resolution for Georgia and surrounding states, 12-km for most of Eastern states and 36-km for the rest of CONUS

Forecasting Source Impacts at 4-km for Georgia

(https://forecast.ce.gatech.edu open since November 28th,2014)

Currently working with measurements at ~20 sites in Georgia and Soon with MODIS C6

AOD

An emissions and air quality auto-correction system utilizing near real-time satellite and surface observations

• Minimizes the differences between forecasted and observed concentrations

• Minimal adjustment to source emissions

• Uses impacts of emission sources calculated by CMAQ-DDM-3D – Source impacts can be used for

dynamic air quality management.(e.g., traffic and fires)

Hi-Res2: Online Auto-Adjustment Inverse Modeling Approach for Adjusting Emissions

• Solve for the Adjustment Factors, Rj, that minimize 2

J

j R

jN

i C

J

jjji

simi

obsi

jobsi

RRScc

1

2

2ln1

2

2

1,

2 )(ln)1(

Georgia Institute of Technology

DDM-3D calculated sensitivity of concentration i to source j emissions emission adjustment ratio

weight

Inverse Model Formulation

L-BFGS algorithm is used for the optimization (R package nloptr)

Remaining Error Amount of Change in Source Strengths

2Ci 2

Rj

Hindcasting Air Quality Impacts from Specific Sources: 2010 Nationwide Source Apportionment Effort within

the Tiger Team Project of Operational Chemical Reanalysis

Georgia Institute of Technology

1. CMAQ-DDM Source Impacts (~30 sources, daily)

4. Temporal Interpolation of Adjustment Factors

2. Hybrid Analysis at Monitors to find Adjustment Factors (Rj’s)

00.5

11.5

Hybrid Adjustment Fac-tors

(Atlanta, GA 1/24/2004)

Rjs

0

1

2

Rjs

0

1

2

Rjs

3. Spatial Interpolation of Adjustment Factors (Kriging)

5. Adjust CMAQ-DDM Spatial Fields (Daily, Spatially Dense)Woodstove Adjustment Factors Adjusted Woodstove ImpactOriginal Woodstove Impact

January 4, 2004

1/4

1/6

1/8

1/10

1/12

1/14

1/16

1/18

1/20

1/22

1/24

1/26

1/28

00.20.40.60.8

11.2

Adjustment Factors (Example)Monitor

Rj

Spatial Hybrid Approach (Ivey et al, GMDD, 2015)

2010 Annual PM2.5 Source Apportionment WRF-CMAQ model physics and chemistry options

42 vertical layers

WRF-ARW Both North America(12 km) & CONUS (4 km)

Mapprojection & grid

LambertConformal & Arakawa C staggering

Vert.co-ordinate 42 σ-p unevenly spaced levels

advection RK3 (Skamarock and Weisman (2008))

SW & LW radiation RRTMG (Iaconoet al. 2008))

PBL Physics Mellor-Yamada-Janjic(MYJ) level 2.5 closure

Surfacelayer scheme Monin-Obukhov Similarity with viscous sub-layer

LandSurface Model NCEP NOAH

Cloud Microphysics Thompson etal. (2008)

Cloud convective mixing

Betts-Miller-JanjicMass adjustment

CMAQ4.7.1 Both CONUS(12 km) & SENEX (4 km)

Map projection & grid Lambert Conformal & Arakawa C staggering

Vert. co-ordinate 42 σ-p unevenly spaced levels

Gas chemistry Cb05 with 156 reactions

Aerosol chemistry Aero5 with updated evaporation enthalpy

Anthropogenic emission

2005NEI as base year, mobile projected using AQS*, area and off-road used CSPR^, point source uses 2012 CEM data

WRAP oil and gas emissions data

Biogenic emission BEIS-3.14

Lateral BC RAQM (B. Pierce)

12 km nested to 4 km

CMAQ-DDM PM2.5 Biomass Burning Impact10

8

6

4

2

0

Adjusted PM2.5 Biomass Burning Impact10

8

6

4

2

0

Biomass Impact: Before/After Assimilation of Observations

• Many sources are highly variable leading to significant differences between observations and simulations– Biomass– Dust– Agriculture

• Seasonal results in better agreement– Suggests inventories

reasonable “on average”

04/01/04

2006 Seasonally-Averaged PM2.5 Impacts (ug/m3):Biomass Burning (2006)

CMAQ-DDM Spatial Hybrid CMAQ - Spatial Hybrid

WIN

TE

RS

UM

ME

R

Updated isoprene OA Mechanism

• Comparison of CMAQ-simulated SOA from isoprene to observations from AMS factors led to updates • Assimilated PBL

measurements • Improved IEPOX physics

and chemistry:• Dry deposition resistance

reduced• Updated reaction rate

constants• Modified Henry’s law for

IEPOX

•Not yet in forecasting system

Summary

• PM and ozone forecasting system (Hi-Res2) operational with source impact forecasting and dynamic emission adjustments– Supports dynamic air quality management through providing source

specific information – Currently for traffic, power plant and prescribed-burn (Talat’s talk)

emissions– Expansion to include other species measurements underway – Improved approach to assimilating AOD and PM measurements

underway (Utilizing data-fused fields)• Hybrid approach for source apportionment

– Utilizes gases concentration and PM composition measurements – Application nationwide for 2010: Tiger Team Chemical Reanalysis

project– Results for air quality management (EPD, ARC) and health (CDC)

• Extension and update of biogenic SOA formation improves SOA results– Isoprene oxidation and NO3+terpene reactions

Georgia Institute of Technology

Acknowledgements

• NASA• Georgia EPD• Georgia Forestry Commission• US Forest Service

– Scott Goodrick, Yongqiang Liu, Gary Achtemeier

• Environmental Protection Agency (EPA)

• Atlanta Regional Commission (ARC)

RD83521701