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

Introducing the Project This project is funded by U.S. EPA – Science To Achieve Results (STAR) Program Grant # R D ANIEL S. COHAN (PI) D ENNIS COX A NTARA DIGAR M ICHELLE BELL R OBYN WILSON J AMES BOYLAN M ICHELLE S. BERGIN

WHY IS THIS PROJECT IMPORTANT ? (TO BE REMOVED)

Background & Objective O3O3 O3O3 PM 2.5 Non-attainment In U.S. NOx VOC SOx NH 3 PM Measure: Control Emission Controlling Multiple Pollutants How Much to Control ? Which Measure is Effective? Scientists & Air Quality Modelers have come up with techniques to estimate Sensitivity of O 3 and PM 2.5 to their precursor emissions But in reality the model inputs are sometimes uncertain Uncertainty in Model Input causes Uncertainty in O 3 & PM 2.5 Sensitivities Uncertainty in Model Input causes Uncertainty in O 3 & PM 2.5 Sensitivities

HOW TO ACHIEVE THIS GOAL ? (TO BE REMOVED)

Model 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 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 O 3 and PM 2.5 sensitivity to their precursor emission control Achieving the Goal CMAQ - High-order Decoupled Direct Method -E-E A B CACA CBCB

Introducing Uncertainty Effect of Control Strategy (Emission Reduction) Effect of Uncertain Input Parameters Sensitivity to parameter j if j is uncertain: High-or Self Sensitivity Cross Sensitivity Sensitivity to parameter j if k  j is uncertain: Source: Cohan et. al., 2005 E VOC EAEAEAEA CACACACA CBCBCBCB EBEBEBEB B A Ozone A* A Modeled value Actual value E E* -  E A Modeled value Actual value

HDDM in Selection of Control Strategy % reduction in regional emission (NOx, VOC, NH 3, etc.) Specific amount of reduction at power plant (NOx, SOx) % reduction in regional emission (NOx, VOC, NH 3, 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 O 3 at worst monitor O 3 population exposure PM 2.5 at worst monitor PM 2.5 population exposure O 3 at worst monitor O 3 population exposure PM 2.5 at worst monitor PM 2.5 population exposure

Example 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 O 3 at worst monitor O 3 at Atlanta PM 2.5 at worst monitor PM 2.5 population exposure O 3 at worst monitor O 3 at Atlanta PM 2.5 at worst monitor PM 2.5 population exposure

OUR APPROACH Sensitivity of O 3 to precursor emission = f(E i, R j, Vd k, …)

Methodology MONTE CARLO CMAQ-HDDM 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

HOW ACCURATE IS OUR MODEL ? (TO BE REMOVED)

APPLYING TO GEORGIA – A CASE STUDY (MAY 30 – JUNE 06, 2009) ALGA 12km domain

Accuracy of CMAQ-HDDM R 2 > 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 ENOx HDDM Impact of Uncertainty in R(NO 2 +OH) Brute Force Sensitivity of Ozone to NOx Emission

UNCERTAIN EMISSION INVENTORY First Scenario: ENO X EVOC ESO X ENH 3 EPM

Case 1A: Self sensitivity Atlanta O 3 Scherer O 3 Atlanta O 3 Scherer O 3 Reduction in NOx emission NOx emission uncertain by ±30%

If NOx emission is larger than expected, O 3 _ENOx generally increases but some locations have NOx disbenefit Sensitivity of O 3 to Atlanta NOx Impact of Uncertainty in ENOx Sensitivity of O 3 to Scherer NOx

Case 1B: Cross Sensitivity Atlanta O 3 Scherer O 3 Atlanta O 3 Scherer O 3 Reduction in VOC emission NOx emission uncertain by ±30%

If ENOx is larger than expected, sensitivity of O 3 to EVOC is slightly increased Impact of Uncertainty in ENOx Sensitivity of O 3 to Atlanta VOC Sensitivity of O 3 to Scherer VOC

UNCERTAIN REACTION RATE Second Scenario: NO 2 +OH  HNO 3 NO 2 +h  NO+O NO 2 +NO 3  N 2 O 5 O 3 +NO  NO 2 HRVOCs+OH  products HRVOCs+NO 3  products HRVOCs+O 3  products

Case 2: Cross Sensitivity Atlanta O 3 Scherer O 3 Atlanta O 3 Scherer O 3 Reduction in NOx emission R(NO2+OH) uncertain by ±30%

If R(NO 2 +OH  HNO 3 ) is larger than expected, sensitivity of O 3 to ENOx decreases Sensitivity of O 3 to Atlanta NOx Sensitivity of O 3 to Scherer NOx Impact of Uncertainty in R(NO 2 +OH)

Preliminary Findings Uncertain NO x emissions inventory: A larger NO x inventory generally increases the sensitivity of Ozone to ENO x, however some locations show NO x disbenefit A larger NO x inventory increases the sensitivity of Ozone to EVOC Uncertain Reaction Rate of HNO 3 formation: A larger rate than expected greatly decreases the Ozone sensitivity to ENO x

Take Home Message (to be removed) CMAQ-HDDM would be able to address issues like: How uncertain is O 3 /PM 2.5 sensitivity to precursor emissions when model inputs (primary pollutant emissions, photochemical reaction rates, deposition velocities, etc.) are uncertain ? What would be the benefits of a hypothetical reduction in primary pollutant concentration like NOx, SOx, VOC at worst monitor given the emission inventory is uncertain ? What would be the benefits of a percentage reduction in anthropogenic or biogenic emission like NOx, SOx, VOC at a given region when the rate constants for photochemical reactions are uncertain ? What would be the benefits of a percentage reduction in emission when the deposition velocities and/or meteorological conditions are uncertain ?

Overall 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 Population Exposure Impact on Population Exposure ANALYSIS OUTCOME 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

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 (O 3 and PM 2.5 ) 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

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E VOC Ozone -EA-EA  ∆C∆C EAEAEAEA CACACACA Brute Force vs. HDDM CBCBCBCB EBEBEBEB B A + + Source: Cohan et. al., 2005