Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering A Process Analysis for Analyzing and Evaluating Photochemical Air Quality Models: Asking More of the Models March 15, 2000 Gail Tonnesen University of California, Riverside Bourns College of Engineering Center for Environmental Research and Technology

University of California, Riverside Bourns College of Engineering [C i ] = concentration P(C i ) = production rate VOC = volatile organic compounds NO x = NO + NO 2 + NO N 2 O 5 + HONO +... NO z = HNO 3 + NO PAN + RNO 3 O x = O 3 + NO 2 + NO z + 2 NO 3 + O + 3 N 2 O Definitions

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering PSS Equilibrium NO 2 + h   NO + O O + O 2  O 3 O 3 + NO  O 2 + NO 2 P(O x ): RO 2 + NO  RO + NO 2

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Fundamental Photochemistry Tropospheric gas phase chemistry is driven by the OH radical: Radical Initiation Radical Propagation Radical Termination NO x termination

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Radical Initiation O 3 + h   O (1) D O (1) D + H 2 O  2 OH * HCHO + h   2 HO 2 * + CO HO 2 * + NO  OH * + NO 2 HONO + h   OH * + NO PAN  RO 3 * + NO 2

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Radical Propagation OH * + CH 4 + O 2  CH 3 O 2 * + H 2 O CH 3 O 2 * + NO  NO 2 + CH 3 O * CH 3 O * + O 2  HO 2 * + HCHO HO 2 * + NO  NO 2 + OH * 2 NO 2 + h  + 2 O 2  2 O NO Net Reaction: CH O 2  2 O 3 + HCHO + H 2 O

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Radical and NO x termination OH * + NO 2  HNO 3 HO 2 * + HO 2 *  H 2 O 2 HO 2 * + RO 2 *  ROOH RO 2 * + NO  RNO 3 RO 3 * + NO 2  PAN N 2 O 5 + H 2 O  2 HNO 3

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Pathway Fractions f OH+HC + f OH+NO2 + f OH+misc = 1 f HO2+NO + f HO2+XO2 + f HO2+misc = 1

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Trace Gas Governing Equations N coupled PDEs  C j  t =  v.  C j + D  2 C j + P j (C)  L j (C) C j + E j  D j, j=1,N P j =   i k i  C m L j =  k i  C n, n  j

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Traditional AQM Approach Solve for state variables: O 3, NO 2, NO, VOC, … C i (t) =C 0 +  ( P(C) - L(C)C i +E - D ) dt, i=1,N Limitation: No explanatory value.

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Process Analysis –Makes explicit the component processes: Integrated Reaction Rates (IRR) Integrated Process Rates (IPR) - transport, deposition, emissions, etc. –IRR: Use trapezoidal integration:  mass =  t ( R n+1 + R n ) /2.0 –IPR: Use flux or  C

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Opens up the Black Box –improved understanding of model dynamics. Good QA Procedure for Model Runs –easily identifies flaws. Provides a Detailed Explanation of: –O 3 production using HO x, NO y & O x budgets Suggests Diagnostics for: –Model Evaluation –Model Inter-comparison. What is Process Analysis Good For?

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Does not have predictive value: –tells you how much O 3 was produced from X –does not tell you how O 3 will change when you change X. Sensitivity Analysis and Process Analysis are Complementary: –Sensitivity Analysis has predictive value, –Process Analysis has explanatory value. Process Analysis explains the IR of VOCs 1Run base and sensitivity simulation to get IR 2Do PA on both simulations to explain IR. Sensitivity Analysis Alone is Insufficient –Chameidies et al. (1988) What Can’t Process Analysis Do?

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering PKSS - UNC Chamber Modeling (1984) –lotus spreadsheet, IRR OZIP/EKMA (1990) –fortran post-processor, IRR/MB RADM (1993), UAM (1995) –fortran post-processor – using IRR & IPR RADM (1995) –hard-coded internal processing, IRR & IPR History of Process Analysis

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Large Output Files –210 IRRs for SAPRC99 mechanism –Several IPRs for each species. Expensive Post-Processing –Can only get detailed analysis for a small sub-domain –Tedious and Time Consuming Analysis is Complicated by Transport –Need to know the history of material transported into the cell. –Mass Tracking Algorithm requires flux between cells: for each species, each time step, each cell full domain analysis is prohibitively expensive can only do a small sub-domaim Post-Processor Limitations

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Integrate the Processing into the Chemical Solver –at each time step,calculate the budgets of O x, HO x, NO y –accumulate and output at hourly intervals. Output 30 PA terms describing the budgets –Uses less disk space & No post-processing. –PA info is immediately available for the full model domain & can be visalized along with concentration fields. –Code is available for CB4, RADM2, RACM, SAPRC99 –added to RADM, SAQM, CAMx, Chronos. Models-3 has flexible user interface (Jerry Gipson) –Command language allows you to reproduce all or part of the output that is hard-coded in RADM Solution

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering EKMA Control Predictions

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Radical Initiation: – – J-values & HCHO, O 3, H 2 O, HONO, H 2 O 2, PAN OH Chain Length: – –NO, NO 2, speciated VOC, total RO 2, O 3 Radical Termination: – –NO 2 & OH, HO 2 & RO 2, NO, O 3 NO x Termination, P(NO z ): – – NO 2 & OH, NO & RO 2, RCO 3, N 2 O 5, H 2 O P g (O x ): – –NO, HO 2, RO 2. Model Evaluation: Local Diagnostics

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Radical Initiation: – 2 peroxides + NO z OH Chain Length: –O 3 / (2 peroxides + NO z ) Radical Termination: –2 peroxides + NO z, peroxides/NO z NO x Termination, P(NO z ): – HNO 3, speciated RNO 3, PAN, MPAN P(O x ): –O 3, NO 2, NO z Model Evaluation: Cumulative Diagnostics

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering SOS-Nashville 1995 –Brookhaven Group (Kleinman, Daum et al.) have calculated several of these diagnostics from measurements. HO x Budgets –Idaho Hill, 1993:(Crosley; Stevens et al.; Cantrell et al.; JGR 1997) –Mauna Loa (Cantrell et al., JGR 1996) –Southern Ontario 1992 (Plummer et al., Atmos Env., 1996) Research Need: Chamber Experiments –Compare observations with models in a controlled environment. –Budgets of HO x, O x and NO y ; & PSS. Model Evaluation: Applications

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering For a particular source, do we care about the exposure that results from that source, or the average mass contribution from a source? For cross-border transport, do we care about effects on O 3 exposure, or the mass of O 3 transported across the border? Changes in exposure (from sensitivity runs) can underestimate the effect of a given source because O 3 is relatively insensitive to changes in model inputs. – –Reduce NO x emissions from one source, and P(O x )/P(NO z ) increases for all other source, L(O x ) decreases, etc. Transport & Area of Influence

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Transport: Current Approaches Sensitivity Runs – –results can be misleading. Statistical Analyses : – –OTAG, Rao et al., Pudykiewicz et al. – –useful for charactizing the model, but they do not quantify mass production & transport. Mass-Tracking (Wang and Jeffries) – –Compute intensive, limited domain. OSAT (Yarwood, Morris) – –tracers used for attribution to individual sources, – –attributes P(O 3 ) to NO x - or ROG-sensitive conditions.

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering Use a tagged-species approach to track the mass contribution for just a few source regions. – –For a NO x source, define a “probe” array to trace the fate of NO y. – –IRR and IPR are used to determine the change in mass due to chemical reactions, transport, deposition, etc. Similar to Mass-Tracking, but internal processing. Provides a full mass accounting for just a few sources. – –Mass balance on contribution to P(O x ). – –Account for continuing transformations of PAN, NO z. Use this to determine mass attribution to source areas in the base case, sensitivity case, & attainment scenario. Research Need: Evaluation of Mass Transport

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering AQMs are sufficiently complex that scientific intuition is inadequate for assessing cause and effect. Process Analysis is a useful tool for QA and for understanding the results of Model Simulations. Useful for validating the component chemical and sink/source processes of AQMs if we can get the necessary ambient data. PA and sensitivity methods are complementary. Conclusions

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering

Center for Environmental Research and Technology University of California, Riverside Bourns College of Engineering