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

INCORPORATING UNCERTAINTY INCORPORATING UNCERTAINTY INTO AIR QUALITY MODELING & INTO AIR QUALITY MODELING &

PLANNING PLANNING – A CASE STUDY FOR GEORGIA – A CASE STUDY FOR GEORGIA

7th Annual CMAS Conference6-8th October, 2008

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

&Maudood Khan, James Boylan

Georgia Environmental Protection Division

Introducing the Introducing the ProjectProject

This project is funded by

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

Grant # R833665

DANIEL S. COHAN (PI)

DENNIS COX

ANTARA DIGAR

MICHELLE BELL

ROBYN WILSON

JAMES BOYLAN

MICHELLE S. BERGIN

Background & Background & ObjectiveObjective

O3O3

PM2.

5

PM2.

5Non-attainment

In U.S.

NOxNOx VOCVOC SOxSOx NH3NH3 PMPM

Measure: Control Emission

Controlling Multiple PollutantsHow 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

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 uncertainUncertainty in Model Input causes

Uncertainty in O3 & PM2.5

Sensitivities

Uncertainty in Model Input causes

Uncertainty in O3 & PM2.5

Sensitivities

Model UsedModel Used

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

C

E

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

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

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

Achieving the GoalAchieving the GoalCMAQ - High-order Decoupled Direct MethodCMAQ - High-order Decoupled Direct Method

2

2)2(

C

S

C

S )1(

jjj Er

jjjj

jj

)1(j

C

)E(

CE

r

CES

-E

A

B

CA

CB

Introducing UncertaintyIntroducing Uncertainty

Effect of Control Strategy (Emission Reduction)

Effect of Uncertain Input Parameters

SS)(1(S)2(

j,jj

)1(

j

)1(

j j

SSS)2(

k,jk

(1)

j

(1)

j

Sensitivity to parameter j if j is uncertain:

High-orSelf

Sensitivity

CrossSensitivit

y

Sensitivity to parameter j if k j is uncertain:

Source: Cohan et. al., 2005

EVOC

2

2)2(

C

S

C

S )1(

EEAA

CCAA

CCBB

EEBB

B

A

Ozone

?)2(

S

?)1(

SA*

AModeled value

Actual value

EE*

-EA

Modeled valueActual value

HDDM in Selection of Control HDDM in Selection of Control StrategyStrategy

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

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

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

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

Uncertainty in emission inventory

Uncertainty in reaction rate constants

Uncertainty in deposition velocities

Uncertainty in emission inventory



O3 at worst monitor

O3 population exposure

PM2.5 at worst monitor

PM2.5 population exposure

O3 at worst monitor

O3 population exposure



Example CaseExample Case

% reduction in regional NOx emission

Specific amount of reduction at power plant

% reduction in regional NOx emission

Specific amount of reduction at power plant

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



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



O3 at worst monitor

O3 at Atlanta



O3 at worst monitor

O3 at Atlanta



x

3

ENO

O

RENO

O,

x

32

OUR APPROACHOUR APPROACH

Sensitivity of O3 to precursor emission =

f(Ei, Rj, Vdk, …)

Sensitivity of O3 to precursor emission =

f(Ei, Rj, Vdk, …)

Methodology Methodology

MONTE CARLO

CMAQ-HDDM

SSS1S (2)kj,jk k(2)jj,j(1)jj1j )(

SURROGATE MODEL

SURROGATE MODEL

Monte Carlo Sampling

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

Sensitivity estimated by CMAQ-HDDM

PDFs for input parameters from literature

Develop output PDFs using Surrogate Model

Characterize uncertainty in output sensitivity, S*

Input Parameter

Output Sensitivity

APPLYING TO GEORGIA – APPLYING TO GEORGIA – A CASE STUDYA CASE STUDY(MAY 30 – JUNE 06, 2009)(MAY 30 – JUNE 06, 2009)

ALGA 12km domainALGA 12km domain

Accuracy of CMAQ-Accuracy of CMAQ-HDDMHDDM

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)

(Cross Sens)

Impact of Uncertainty in ENOxImpact of Uncertainty in ENOx

HDDMHDDM

Impact of Uncertainty in R(NO2 +OH)

Impact of Uncertainty in R(NO2 +OH)

2x32ENOO

RENOOx32

Brute ForceBrute Force

Sensitivity of Ozone to NOx Emission

Sensitivity of Ozone to NOx Emission

UNCERTAIN EMISSION UNCERTAIN EMISSION INVENTORYINVENTORY

First Scenario:

ENOENOXX

EVOEVOCC

ESOESOXX

ENHENH33

EPMEPM

Case 1A: Self sensitivityCase 1A: Self sensitivity

Atlanta O3

Scherer O3 Atlanta O3

Scherer O3

Reduction in NOx emission


NOx emission uncertain by ±30%


If NOx emission is larger than expected, O3

_ENOx generally increases but some locations have NOx disbenefit

Sensitivity of O3 to Atlanta

NOx


Sensitivity of O3 to Scherer

NOx

Case 1B: Cross Sensitivity Case 1B: Cross Sensitivity

Atlanta O3


Scherer O3

Reduction in VOC emission

Reduction in VOC emission



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



VOC


VOC

UNCERTAIN REACTION RATEUNCERTAIN REACTION RATESecond Scenario:

NONO22+OH+OHHNOHNO33

NONO22+h+hNO+ONO+ONONO22+NO+NO33NN22OO55

OO33+NO+NONONO

22

HRVOCs+OHHRVOCs+OHprodproductsucts

HRVOCs+NOHRVOCs+NO33prodproductsucts

HRVOCs+OHRVOCs+O33produproductscts

Case 2: Cross SensitivityCase 2: Cross Sensitivity

Atlanta O3


Scherer O3



R(NO2+OH) uncertain by ±30%

R(NO2+OH) uncertain by ±30%

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


NOx


NOx

Impact of Uncertainty in R(NO2+OH)

Impact of Uncertainty in R(NO2+OH)

Preliminary FindingsPreliminary 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 GoalOverall Project Goal

Response of pollutant

sensitivity to uncertainty

(CMAQ-HDDM)

Response of pollutant

sensitivity to uncertainty

(CMAQ-HDDM)

Cost of Emission Control

(Lit / AirControlNET / CoST)

Cost of Emission Control

(Lit / AirControlNET / CoST)

Health Impacts & Benefits of

Emission Control(BENMAP)

Health Impacts & Benefits of

Emission Control(BENMAP)

Impact on pollutant level

at worst monitor

Impact on pollutant level

at worst monitor

Impact on Population Exposure

Impact on Population Exposure

ANALYSISANALYSIS OUTCOMEOUTCOME

Impact on Population Exposure &

Human Health

Impact on Population Exposure &

Human Health

Control Strategy that satisfies the 3 criteria

• Reduces multiple pollutants (air quality)

• Cost Effective (economic)

• Maximum health benefit (health)

air quality

economic

health

An Optimum Control Strategy

An Optimum Control Strategy

Future Plan of ActionFuture 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 EPDFor providing emission dataByeong Kim for technical assistance

CMAS

For further information & updates of our For further information & updates of our projectproject

Contact: [email protected] on to http://uncertainty.rice.edu/

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

Documents

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