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7 th Annual CMAS Conference 6-8 th October, 2008. INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & PLANNING – A CASE STUDY FOR GEORGIA. Antara Digar, Daniel S. Cohan, Dennis Cox, Wei Zhou Rice University & Maudood Khan, James Boylan Georgia Environmental Protection Division.

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7 th annual cmas conference 6 8 th october 2008

7th Annual CMAS Conference

6-8th October, 2008

INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & PLANNING – A CASE STUDY FOR GEORGIA

Antara Digar, Daniel S. Cohan, Dennis Cox, Wei Zhou

Rice University

&

Maudood Khan, James Boylan

Georgia Environmental Protection Division


Introducing the project
Introducing the Project

This project is funded by

U.S. EPA – Science To Achieve Results (STAR) Program

Grant # R833665

JAMES BOYLAN

MICHELLE S. BERGIN

DANIEL S. COHAN (PI)

DENNIS COX

ANTARA DIGAR

MICHELLE BELL

ROBYN WILSON


Background objective
Background & Objective

Measure: Control Emission

NOx

VOC

SOx

NH3

PM

PM2.5

O3

Non-attainment

In U.S.

Controlling Multiple Pollutants

How Much to Control ?

Which Measure is Effective?

Scientists & Air Quality Modelers have come up with techniques to estimate Sensitivity of O3 and PM2.5 to their precursor emissions

But in reality the model inputs are sometimes uncertain

GOAL:

Estimate this Uncertainty

Uncertainty in Model Input

causes

Uncertainty in O3 & PM2.5

Sensitivities


Model used
Model Used

Achieving the Goal

CMAQ - High-order Decoupled Direct Method

  • H- High-order sensitivity analysis

  • N- Nonlinear relationship between secondary pollutants and its precursor emission

  • N- Non-liner sensitivity model can be used to determine the impact of uncertain Emission inventory, Photochemical rate constants, Deposition velocities on O3 and PM2.5 sensitivity to their precursor emission control

HDDM determines slope at any point by calculating the local derivative at that point

C

A

CA

CB

B

E

-E

‘E’ denotes precursor emission; ‘C’ denotes secondary pollutant concentration

Source: Hakami et. al. 2003; Cohan et. al. 2005


Introducing Uncertainty

Effect of Uncertain Input Parameters

Ozone

Modeled value

Actual

value

A

A

CA

Actual

value

Modeled value

B

E

E*

A*

CB

-D EA

Effect of Control Strategy (Emission Reduction)

High-or

Self

Sensitivity

Sensitivity to parameter j if j is uncertain:

Cross

Sensitivity

EVOC

EB

Sensitivity to parameter j if k  j is uncertain:

EA

Source: Cohan et. al., 2005


Hddm in selection of control strategy
HDDM in Selection of Control Strategy

Control measures

  • % reduction in regional emission (NOx, VOC, NH3, etc.)

  • Specific amount of reduction at power plant (NOx, SOx)

Pollutant Levels &

Exposure Metrics

  • O3 at worst monitor

  • O3 population exposure

  • PM2.5 at worst monitor

  • PM2.5 population exposure

  • Uncertainty in emission inventory

  • Uncertainty in reaction rate constants

  • Uncertainty in deposition velocities

Uncertainties


Example case
Example Case

Control measures

Pollutant Levels &

Exposure Metrics

  • % reduction in regional NOx emission

  • Specific amount of reduction at power plant

  • O3 at worst monitor

  • O3 at Atlanta

  • PM2.5 at worst monitor

  • PM2.5 population exposure

  • Uncertainty in emission – self/cross (NOx, VOC, etc.)

  • Uncertainty in reaction rate constants

  • Uncertainty in deposition velocities

Uncertainties


Our approach

Sensitivity of O3 to precursor emission = f(Ei, Rj, Vdk, …)

Our Approach


Methodology
Methodology

Sensitivity of secondary pollutant to any parameter j given both j and any other input parameter k  j is also uncertain:

SURROGATE MODEL

CMAQ-HDDM

MONTE CARLO

Input Parameter

  • Sensitivity estimated by CMAQ-HDDM

  • PDFs for input parameters from literature

  • Monte Carlo Sampling

  • Develop output PDFs using Surrogate Model

  • Characterize uncertainty in output sensitivity, S*

Output Sensitivity


Applying to georgia a case study may 30 june 06 2009
Applying to Georgia – A Case Study(May 30 – June 06, 2009)

ALGA 12km domain


Accuracy of cmaq hddm
Accuracy of CMAQ-HDDM

Sensitivity of Ozone to NOx Emission

Impact of Uncertainty in ENOx

R2 > 0.99

Limitation: CMAQ-HDDM is not yet capable of handling high-order PM sensitivities, hence BF will be used for such cases

(Self Sens)

Impact of Uncertainty in R(NO2 +OH)

(Cross Sens)

Brute Force

HDDM


Uncertain emission inventory

EVOC

ENOX

ESOX

First Scenario:

ENH3

EPM

Uncertain Emission Inventory


Case 1a self sensitivity
Case 1A: Self sensitivity

Control measures

Pollutant Levels &

Exposure Metrics

Reduction in NOx emission

  • Atlanta O3

  • Scherer O3

NOx emission uncertain by ±30%

Uncertainties


If NOx emission is larger than expected, O3 _ENOx generally increases but some locations have NOx disbenefit

Impact of Uncertainty in ENOx

Sensitivity of O3 to Atlanta NOx

Sensitivity of O3 to Scherer NOx


Case 1b cross sensitivity
Case 1B: Cross Sensitivity

Control measures

Pollutant Levels &

Exposure Metrics

Reduction in VOC emission

  • Atlanta O3

  • Scherer O3

NOx emission uncertain by ±30%

Uncertainties


If ENOx is larger than expected, sensitivity of O3 to EVOC is slightly increased

Impact of Uncertainty in ENOx

Sensitivity of O3 to Atlanta VOC

Sensitivity of O3 to Scherer VOC


Uncertain reaction rate

HRVOCs+NO3products

HRVOCs+O3products

O3+NONO2

NO2+hNO+O

NO2+NO3N2O5

Second Scenario:

NO2+OHHNO3

HRVOCs+OHproducts

Uncertain reaction Rate


Case 2 cross sensitivity
Case 2: Cross Sensitivity

Control measures

Pollutant Levels &

Exposure Metrics

Reduction in NOx emission

  • Atlanta O3

  • Scherer O3

R(NO2+OH) uncertain by ±30%

Uncertainties


If R(NO2+OH  HNO3) is larger than expected, sensitivity of O3 to ENOx decreases

Impact of Uncertainty in R(NO2+OH)

Sensitivity of O3 to Atlanta NOx

Sensitivity of O3 to Scherer NOx


Preliminary findings
Preliminary Findings

  • Uncertain NOx emissions inventory:

    • A larger NOx inventory generally increases the sensitivity of Ozone to ENOx, however some locations show NOx disbenefit

    • A larger NOx inventory increases the sensitivity of Ozone to EVOC

  • Uncertain Reaction Rate of HNO3 formation:

    • A larger rate than expected greatly decreases the Ozone sensitivity to ENOx


Overall project goal
Overall Project Goal

AnOptimum Control Strategy

ANALYSIS

OUTCOME

  • Control Strategy that satisfies the 3 criteria

  • Reduces multiple pollutants (air quality)

  • Cost Effective (economic)

  • Maximum health benefit (health)

Response of pollutant sensitivity to uncertainty

(CMAQ-HDDM)

air quality

Impact on pollutant level at worst monitor

Cost of Emission Control

(Lit / AirControlNET / CoST)

economic

Impact on

Population Exposure & Human Health

Impact on

Population Exposure

Health Impacts & Benefits of Emission Control

(BENMAP)

health


Future plan of action
Future Plan of Action

  • Estimate cost of control strategies

  • Calculate health benefits for a given population exposure

  • Interlink CMAQ-HDDM sensitivity output with health and cost assessment

  • Select control strategy that reduces multiple pollutants (O3 and PM2.5) based on maximum health benefit and minimum cost of implementation


Acknowledgement
Acknowledgement :

  • U.S. EPA

    • For funding our project

  • GA EPD

    • For providing emission data

    • Byeong Kim for technical assistance

  • CMAS


  • Contact antara@rice edu log on to http uncertainty rice edu

    Contact: [email protected]

    Log on to http://uncertainty.rice.edu/

    For further information & updates of our project


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