Quantifying CMAQ Simulation Uncertainties of Particulate Matter in the Presence of Uncertain Emissions Rates Wenxian Zhang, Marcus Trail, Alexandra Tsimpidi,

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Presentation transcript:

Quantifying CMAQ Simulation Uncertainties of Particulate Matter in the Presence of Uncertain Emissions Rates Wenxian Zhang, Marcus Trail, Alexandra Tsimpidi, Yongtao Hu, Athanasios Nenes, and Armistead Russell CMAS Annual Conference Oct 17, 2012

Acknowledgements U.S. EPA Southern Company/ Georgia Power Phillips 66 RD

Overview Uncertainties in regional air quality models Method for uncertainty analysis - Monte Carlo method - Reduced-form model based on high-order DDM sensitivity analysis Quantification of uncertainties in simulated PM 2.5 concentrations due to uncertain emissions Quantification of uncertainties in model response to emissions control in the presence of uncertain emissions Quantification of uncertainties in first-order sensitivities of PM 2.5 due to emission uncertainties

Uncertainties in Air Quality Models

Original AQM How to Quantify Uncertainties? Concentration 1 Concentration 2 Concentration 3. Concentration N Sample 1 Sample 2 Sample 3. Sample N Computationally Expensive! Uncertainty Traditional: Monte Carlo Method Nonroad Mobile EGU Area Biogenic

RFM Concentration 1 Concentration 2 Concentration 3. Concentration N Sample 1 Sample 2 Sample 3. Sample N New approach: Monte Carlo Method with reduced-form model (RFM) Uncertainty Nonroad Mobile EGU Area Biogenic

Reduced-Form Model CMAQ HDDM-3D CMAQ Input Parameters Pollutant Concentrations RFM - Constructed based on sensitivity coefficients - Directly reflects pollutant-parameter response - Substantially reduces the computational cost

Evaluation of RFM [Zhang et al., 2012 GMD] Nitrate concentration with 50% reductions in domain-wide NO x Nitrate concentration with 50% reductions in domain-wide SO 2

Air Quality Modeling in Houston Region 36x36 km 12x12 km 4x4 km Modeling domain - Nested 4x4km grids - Houston region, border of Texas and Louisiana Episode - July 12 – 23, 2006 Modeling system - SMOKE v2.6 - WRF v3.0 - CMAQ v4.7.1 with HDDM

Model Performance PM 2.5 concentration July 23, h average /2 Clinton Dr /2 Mae Drive /2 Aldine Mail Rd Date (July 2006 CDT) PM 2.5 Concentrations ( μ g m -3 ) Model Evaluation July 12-23, 2006 hourly average PollutantsO3O3 PM 2.5 MOC54.8 ppb12.8 μg m -3 MB1.8 ppb-1.8 μg m -3 MNB7.28%10.5% MNE23%66.2% FB3.38%-24.4% FE22.26%51% Performance Metrics

Emission Uncertainties and Sampling Log-normal distribution Emission uncertainty factors [ E / f, E x f ] Random sampling with N = 1000 Source Categories Source Categories Uncertainty Factors References EGU 1.03Napelenok, 2011 Mobile 2Hanna et al., 2001 Non-road 1.5Chi et al., 2010 Area 2Hanna et al., 2001 Biogenic 3Hanna et al., 2005 E/E 0 Sampling Results

Uncertainties in PM 2.5 Simulations PM 2.5 Concentrations (μg m -3 ) Uncertainty (%) Simulated Concentrations (μg m -3 ) Concentration Percentiles (μg m -3 ) 2.5th 97.5th 50th 95% CI of 24-hr average PM 2.5 July 23, 2006 Uncertainty of 24-hr average PM 2.5 July 23, 2006 [Tian et al., 2010]

Uncertainties in PM 2.5 Response to Emission Controls Emission reduction in point source Concentration Reduction (μg m -3 ) Emission reduction in mobile source  Larger uncertainty with larger emission reduction  Larger uncertainty for more uncertain sources δε i - Emission uncertainties ΔE i - Emission reduction

Uncertainties in First-Order Sensitivity of PM % CI of 24-hr average PM 2.5 sensitivity to point source emissions July 23, 2006 Sensitivity Percentiles (μg m -3 ) Simulated Sensitivity (μg m -3 ) 97.5 th 50 th 2.5 th 95% CI of 24-hr average PM 2.5 sensitivity to mobile source emissions July 23, th 50 th 2.5 th Sensitivity Percentiles (μg m -3 ) Simulated Sensitivity (μg m -3 ) Uncertainty ≤ 36% Uncertainty ≤ 18%

Summary Reduced-form model has been constructed using first- and second-order sensitivities obtained from CMAQ-HDDM-3D Quantified emission-associated uncertainties of simulated 24-hr average PM Lower than 45% in the presence of assumed emission inventory uncertainties - Does not capture upset emission biases - Can be easily applied to different combinations of emission uncertainties Quantified uncertainties of emission control response - Higher uncertainties with larger emission reductions - Higher uncertainties for more uncertain emissions Quantified uncertainties of first-order PM 2.5 sensitivities - Dependent on the uncertainty of the sensitivity parameter Future studies - Bias analysis using observations - Control strategy optimization

Questions?