incorporating uncertainty into air quality modeling & planning – a case study for georgia
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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
Uncertainty in reaction rate constants
Uncertainty in deposition velocities
O3 at worst monitor
O3 population exposure
PM2.5 at worst monitor
PM2.5 population exposure
O3 at worst monitor
O3 population exposure
PM2.5 at worst monitor
PM2.5 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 reaction rate constants
Uncertainty in deposition velocities
Uncertainty in emission – self/cross (NOx, VOC, etc.)
Uncertainty in reaction rate constants
Uncertainty in deposition velocities
O3 at worst monitor
O3 at Atlanta
PM2.5 at worst monitor
PM2.5 population exposure
O3 at worst monitor
O3 at Atlanta
PM2.5 at worst monitor
PM2.5 population exposure
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
Reduction in NOx emission
NOx emission uncertain by ±30%
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
Impact of Uncertainty in ENOxImpact of Uncertainty in ENOx
Sensitivity of O3 to Scherer
NOx
Case 1B: Cross Sensitivity Case 1B: Cross Sensitivity
Atlanta O3
Scherer O3 Atlanta O3
Scherer O3
Reduction in VOC emission
Reduction in VOC emission
NOx emission uncertain by ±30%
NOx emission uncertain by ±30%
If ENOx is larger than expected, sensitivity of O3 to EVOC is slightly increased
Impact of Uncertainty in ENOxImpact of Uncertainty in ENOx
Sensitivity of O3 to Atlanta
VOC
Sensitivity of O3 to Scherer
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 Atlanta O3
Scherer O3
Reduction in NOx emission
Reduction in NOx emission
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
Sensitivity of O3 to Atlanta
NOx
Sensitivity of O3 to Scherer
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/