1、Ei AP API HEALTH AND SCIENCES D EPA RTM ENT VIRONMENTAL PUBLICATION NUMBER 461 6 SEPTEMBER 1994 nEL Strategies for Today% Environmenial Parnership The Importance of Using Alternative Base Cases in Photochemical Modeling American Petroleum Institute 1220 L Street, Northwest 11 Washington, D.C. 20005
2、i 0732270 0537872 715 Sfrafeaier fm TIdovr One of the most significant long-term trends affecting the future vitality of the petroleum industry is the publics concerns about the environment. Recognizing this trend, API member companies have developed a positive, forward looking strategy called STEP:
3、 Strategies for Todays Environmental Partnership. This program aims to address public conms by improving our industrys environmental, health and safety performance; documenting performance improvements; and communicating them to the public. The foundation of STEP Is the API Environmental Mission and
4、 Guiding Environmental Principles. API ENVIRONMENTAL MISSION AND GUIDING ENVIRONMENTAL PRINCIPLES The members of the American Petroleum Institute are dedicated to continuous efforts to improve the compatibility of our operations with the environment while economically developing energy resources and
5、 supplying high quality products and services to consumers. The members recognize the importance of efticiently meeting societys needs and our responsibiiit)c to work with the public, the government, and others to develop and to use natural resources in an environmentally sound mannetwhile protectin
6、g the health and safety of our employees and the public. To meet these responsibilities, API members pledge to manage our businesses according to these principies: D s B D D D B D D D B To recognize and to respond to community concerns about our raw materials, products and operations. To operate our
7、 plants and facilities, and to handle our raw materials and products in a manner that protects the environment, and the safety and health of our employees and the public. To make safety, health and environmental considerations a priority In our plannlng, and our development of new products and proce
8、sses. To advise promptly, appropriate officials, employees, customers and the public of information on significant industry-related safety, heaith and environmental hazards, and to recommend protective measures. TO counsel customers, transporters and others in the safe use, transportation and dispos
9、al of our raw materials, products and waste materials. To economically develop and produce natural resources and to conserve those resources by using energy efficiently. To extend knowledge by conducting or supporting research on the safety, heah and environmental effects of our raw materials, produ
10、cts, processes and waste materiais. To commit to reduce overall emission and waste generation. To work WW others to resolve problems created by handling and disposal of hazardous substances from our operations. To participate with government and others in creating responsible laws, regulations and s
11、tandards to safeguard the community, workplace and environment. To promote these principles and practices by sharing experiences and offering assistance to others who produce, handle, use, transport or dispose of similar raw materials, petroleum produds and wastes. API PUBL*qbltb 94 m 0732290 053989
12、3 651 - The Importance of Using Alternative Base Cases in Photochemical Modeling Health and Environmental Sciences Department API PUBLICATION NUMBER 461 6 PREPARED UNDER CONTRACT BY: STEVEN REYNOLDS, HARVEY MICHAELS, AND PHILIP ROTH ENVAIR 12 PALM AVENUE SAN RAFAEL, CA 94901 T.W. TESCHE AND DENNIS M
13、CNALLY ALP IN E GEOPHYSICS P.O. BOX 2059 CRESTED BUTE, CO 81 224 LUANN GARDNER AND GREG YARWOOD SYSTEMS APPLICATIONS INTERNATIONAL 1 O1 LUCAS VALLEY ROAD SAN RAFAEL, CA 94903 JULY 1994 American Petroleum Institute API PUBL*4hLb 94 I 0732290 0539894 598 FOREWORD API PUBLICATIONS NECESSARTLY ADDRESS P
14、ROBLEMS OF A GENERAL NATURE. WITH RESPECT TO PARTICULAR CIRCUMSTANCES, LOCAL, STATE, AND FEDERAL LAWS AND REGULATIONS SHOULD BE REVIEWED. API IS NOT UNDERTAKING TO MEET THE DUTIES OF EMPLOYERS, MANUFAC- TURERS, OR SUPPLIERS To WARN AND PROPERLY TRAIN AND EQUIP THEJR EMPLOYEES, AND OTHERS EXPOSED, CO
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16、T COV- ERED BY LETTERS PATENT. NEITHER SHOULD ANYTHING CONTAINED IN ITY FOR INFRINGEMENT OF LETTERS PATENT. THE PUBLICATION BE CONSTRUED AS INSURING ANYONE AGAINST LIABIL- Copyright 0 1994 American Petroleum Institute i API PUBLx4bLb 74 m 0732290 0539895 424 ACKNOWLEDGMENTS The American Petroleum In
17、stitute thanks the Southern California Edison Company for its financial conmbution to this work. THE FOLLOWING PEOPLE ARE RECOGNIZED FOR THEIR CONTRIBUTIONS OF TIME AND EXPERTISE DURING THIS STUDY AND IN THE PREPARATION OF THIS REPORT I STAFF CONTACT Howard Feldman, Health and Environmental Sciences
18、 Department ERS OF THF. API AIR MODFI .DIG TASK FORCE Kenneth W. Steinberg, Chuirmun, Exxon Research some aiternative base cases were identified that provided a level of UAM performance comparable to the best achieved for the episodes. Several UAM sensitivity ms were made to determine whether the ch
19、oice of base case had a significant influence on simulation results for hypothetical emission reduction strategies. The alternative base cases used in this study produced significant differences in estimates of the air quality benefits associated with hypothetical emission control scenarios. For exa
20、mple, one set of base cases indicated NO, controls would be counterproductive in reducing the estimated peak O, concentration in part of the modeling domain; another base case suggested that such controls would yield almost no change in the peak value. These analyses provide a lower bound estimate o
21、f the uncertainty attending modeling results of the air quality benefits associated with emission control plans. It is strongly recommended that current photochemical modeling practice be extended to include such analyses. These efforts will help reduce the risk of focusing emission control efforts
22、on the wrong precursors, underestimating control requirements needed to meet air quality goals, or incurring costs to implement unnecessary controls. - API PUBL*4bLL 94 E 0732290 0539897 2T7 E TABLE OF CONTENTS Section Eli32 EXECUTIVE SUMMARY . ES- 1 1 . INTRODUCTION . 1.1 BACKGROUND . 1 . 1 STUDY O
23、BJECTIVE 1-2 STRUCTURE OF THE STUDY . 1-3 STRUCTURE OF THIS REPORT 1-4 2 . PHASE 1 IMPROVING MODEL PERFORMANCE . 2-1 OBJECTIVES OF PHASE 1 . 2-1 GENERAL RULES FOR ALLOWABLE CHANGES TO THE MODEL AND ITS INPUTS . 2-1 PROCEDURES AND CRITERIA FOR JUDGING MODEL PERFORMANCE . 2-2 DIAGNOSIS OF MODEL PERFOR
24、MANCE PROBLEMS 2-2 DISCUSSION OF RESULTS 2-7 IMPLICATIONS OF PHASE 1 RESULTS 2-13 3 . PHASE 2 IDENTIFICATION OF ALTERNATIVE BASE CASES 3-1 OBJECTIVE OF PHASE 2 3-1 STUDY DESIGN . 3-1 PREPARATION OF MODEL INPUTS . 3-3 ASSESSING THE EQUIVALENCE OF MODEL INPUTS 3-6 DISCUSSION OF RESULTS 3-9 KEY FINDING
25、S 3. 14 4 . PHASE 3 CONDUCT OF SENSITIVITY STUDIES . 4-1 OBJECTIVES OF PHASE 3 . 4-1 STUDY DESIGN . 4-1 PREPARATION OF MODEL INPUTS 4.2 DISCUSSION OF RESULTS 4.3 SUMMARY OF KEY FINDINGS 4. 10 API PUBL*4bLb 94 0732290 0539898 133 TABLE OF CONTENTS Section ! on 25 June, the ES-3 percentage reductions
26、in peak values were more closely grouped, ranging from 28 to 3 1 percent. For the scenario in which anthropogenic VOC and NO, emissions were reduced by 50 and 25 percent, respectively, two of the three alternative June base cases indicated that the additional NO, control would be counterproductive (
27、Le., would yield a smaller reduction in the peak O, concentration than was estimated for the case where VOC emissions alone were reduced). The third June base case yielded peak O, concentrations that were essentially the same as those resulting from the scenario in which only VOC emissions were redu
28、ced. Simulations were also performed for this emission reduction scenario employing the two August alternative base cases; the percentage reductions in peak O, were quite different for 27 and 28 August. When a 25 percent reduction in anthropogenic NO, emissions was studied (with no change in VOC emi
29、ssions), one June base case indicated a modest reduction in peak O, (Le., a 13 to 14 percent reduction), whereas a second alternative base case yielded very little change in the peak concentration (Le., a 2 percent decrease to a 3 percent increase). For a scenario involving a 50 percent reduction in
30、 NO, emissions from large point sources, the two alternative June base cases employed here indicated little effect on the peak O, in the eastern portion of the domain. In the area northeast of Long Beach and portions of the San Fernando and San Gabriel Valleys, differences in the estimated percentac
31、e reduction in the gridded peak O, values ranged from 9 to 16 percent. The range of outcotties, both citiiong cilternatiiv base cases nntl nlternntive emission reduction outconies, are intlicntive of n lower hoirtirl on the rnnge of uncertninty for the specific case. Alternative base case analyses a
32、re carried out by varying model inputs within their range of uncertainty. The range of estimated concentrations is indicative of the uncertainty in model results. Since such analyses are conducted for a limited set of alternative input conditions, the results represent a lower bound on the range of
33、uncertainty; the use of additional alternative base cases can only broaden the bound. The UAM did not povidc uti accimite sintulntion of sanie features the O, nntl precursor concentrntion fields observed dirring the June and August I98 7 SCA QS episodes. ES-4 API PUBL*4b1b 74 0732270 0539909 849 U U
34、pon initiating this investigation, a review of existing Um4 simulation results for O, episodes occumng in June and August of 1987 indicated that the model was not replicating important features of the O, concentration field, including the relatively high peak concentrations reported at inland monito
35、ring stations and the formation of an extensive layer of high O, concentrations aloft. Attempts to diagnose and to recti5 these problems were only partially successful. Although improvements in model performance were realized, the model was still not simulating O, formation aloft to the extent indic
36、ated by available measurements. Moreover, the model generated O, concentrations in an area north of the San Fernando Valley that were much higher than the observations. The accuracy of VOC and NOz estimates was poorer than that for O, indicating that the model was not adequately simulating these spe
37、cies. IMPLICATIONS Tltere is n need to recognize tite presence of uncertainty in modeling results, to rleternzine the evtent to iuliich it cnn be qiuintjed, und to prescribe and iniplentent metltotls for doing SO. The uncertainty in modeling results stems from (1) biases in procedures employed to de
38、velop model inputs and in the conceptual representation of atmospheric processes within the model, (2) the imprecision in data employed to develop inputs, and (3) the natural variability or stochastic character of the atmosphere and the ability of a deterministic model (such as UM) to provide only a
39、 single realization of such phenomena. It is essential that the presence of uncertainty in modeling results be recognized and considered as part of the decision-making process. Of particular concern is that biased or inaccurate modeling results may cause decision makers to make inaccurate judgements
40、 concerning the most appropriate means for achieving a future air quality goal. In this case, efforts must be made to reduce bias in modeling results to an acceptable level. Once this has been accomplished, procedures should be implemented to quanti5 the remaining modeling uncertainties. A process f
41、or quantiQing uncertainties might include the following steps: O assess overall model performance and perform basic sensitivity runs to insure that the model provides a reasonable simulation of key atmospheric phenomena; ES-5 - API PUBL*4bLb 94 lsl 0732290 OC39910 560 summarize the uncertainties in
42、model inputs as well as the uncertainties inherent in the model formulation itself. use the uncertainty estimates to identi possible alternative base cases and then conduct model simulations to ascertain which base cases provide a level of performance comparable to the best achieved for each episode
43、; develop a lower bound estimate of the range of uncertainty in modeling results for proposed emission control scenarios by assessing the air quality benefits of each scenario using the alternative base cases. The range of O, concentration reductions or increases represents the range of uncertainty
44、in the modeling results. conduct corroborative and other supplemental analyses to support the findings of modeling studies. To implement this process, it will be necessary for regulatory agencies with modeling expertise to develop pertinent information concerning the uncertainties in the models form
45、ulation and its inputs. Model application programs will need to include time and budgetary provisions for evaluating model performance and conducting sensitivity, alternative base case, and corroborative analyses. We strongly recommend that existing regulatory modeling guidance be extended to encour
46、age and require the estimation of uncertainties in modeling results and to indicate how such information should be employed by decision makers. In cnses for which npwosnitrtely eqirilent alterncitive base cnses can be developed, their study and analysis slroiild prove irscfir l to policy ninkers in
47、their rleliberations. Since equivalent base cases yield results that are equally plausible, the findings of emissions reduction simulations using alternative base cases are also equally plausible. Thus, using a suite of “equally plausible“ cases (perhaps three to six in number) to examine the conseq
48、uences of emissions reduction options provides an attractive and effective means for characterizing a lower 1 For esample, suppose that tlirce alteinntive base cases are used to provide a lower bound estimate of the range of uncertainv of the air qualip. hcnelits associated with a pai-ticular emissi
49、on control scenano. Further suppose that, upon conducting the three alteinirtivc hase case simulations, peak 0,levels are reduced by IO, 12, and 15 percent. From these results, we estimau that the control scenario will produce a 10 to 15 percent reduction in the peak O, concentration. The range doutcornes (i,e., a 10 to i 5 percent reduction) represents a lower bound estimate of the uncertainty in the modeling results since additional altematk base case simulations might produce percentage changes in peak O, that are somevhat smaller than 1 O percent or
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