Modeling Mobile-Source Emissions (2000) / Chapter Skim
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4 Model Uncertainty and Evaluation
4 Model Uncertainty and Evaluation
Pages 135-166
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From page 135... ...
This chapter discusses uncertainty and model evaluation, and reviews previous studies on these topics. iThis report uses model evaluation in reference to assessing the ability of a model to accurately represent the real world, for example by being able to estimate the emissions from mobile sources with little error.
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From page 136... ...
This is termed model evaluation and is the subject of the second half of this chapter. Uncertainty and bias in MOBILE arise from many sources, primarily from the data used to construct the model, and from errors in analyses and assumptions leading to model formulations (discussed further below)
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M ODES UNCERTAINTY AND EVALUATION 7 3 7 0.50 0.40 ~e 0.30 o LO E 0.20 0.10 0.00 0.50 0.40._ c a ._ In ._ ~ 0.10- / /. 0.00 0.00 0.30 0.20 Accurate / / / ,,' / .
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From page 138... ...
If not, transportation and air-quality planners could be led to implement costly, unnecessary control programs. Sensitivity Model sensitivity refers to the variation in model output in response to changes in model inputs such as average speed, ambient temperature, fuel volatility, and I/M program parameters.
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From page 139... ...
, which discusses emissions variability in more detail, and also describes other issues that complicate the statistical analysis of vehicle-emissions test data. Nonrepresentative Vehicle Samples MOBILE algorithms and emissions factors are based largely on test data from in-use vehicles that are solicited through the mail or by recruitment at I/M test stations.
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From page 140... ...
emissions estimates. For MOBILES, EPA is proposing to adjust the basic emissions rates based on data from the Dayton, Ohio, IM240 program.
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. Clearly there is large uncertainty in the emissions data and consequently in the basic emissions rates for normaland high-emitting vehicles estimated from these data.2 These vehicle-tovehicle differences are critical for some uses of MOBILE, but the scatter 2EPA has updated their original analysis of these data for Tier 1 NOx emissions using additional data sets.
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From page 143... ...
Incorrect Model Formulation The emissions factors, emissions-factor adjustments, and estimates of emission control program effects in MOBILES and its predecessors are estimated from statistical analyses of available test data. The statistical models are chosen using both engineering considerations (to represent the physical process)
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From page 144... ...
Examples of incorrect statistical models include the following: In some analyses, the intercept of the statistical model is forced through zero, to match physical processes; this represents a trade-off between the correct statistical model and the correct physical model. A1though such model alteration makes sense from an engineering point of view, it introduces bias into the resulting statistical model.
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From page 145... ...
In summary, logarithmic transformations offer some convenience in analysis and the opportunity to use simple statistical models, but at a cost of introducing potentially serious errors. Figure 4-3 shows an example from MOBILES of an inappropriate use of a logarithmic model.
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From page 147... ...
Although VMT estimates are not part of the MOBILE model itself, they are essential to the use of the model, and mobile source emissions inventory results can be substantially affected by uncertainties in the VMT estimates. In addition, uncertainties in other inputs to MOBILE, such as average speed, vehicle age distributions, and vehicle mix, also contribute to uncertainties in MOBILE emissions factors and emissions inventories estimates with these factors.
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, exhaust-emissions rates (emitter category proportions and deterioration rates) , exhaust-correction factors (speed, temperature, and fuel volatility)
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, analogous to those in MOBILE, collectively introduce 20% to 40% uncertainty in the model's exhaust emissions rates. It is important to note, however, that in this and all uncertainty studies described here, the uncertainty estimates were derived considering only uncertainties in the statistical models fit to the available test data.
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Although some methods have been developed and applied to estimate some of the components of uncertainty in MOBILE and related emissions models (described above under previous studies) , there are intractable problems in estimating some of the components of the model's uncertainty.
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for a wide range of counties in the state (Keenan and Escarpeta 1994~. This study can be used to help assess the sensitivity of MOBILE's vehicle emissions rates to changes in the driving cycle and ambient temperature.
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In summary, the NYDEC study illustrates that slight inaccuracies in the assumptions, default values, or specific input of driving-cycle parameters to MOBILE (particularly speed input and the hot- and cold-start mode split) can lead to significant underprediction of real-world fleet VOC emissions.
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emissions modeling would be necessary to ensure that effects of local driving conditions are accurately reflected on emissions. STATE EMISSIONS INSPECTION TESTING State I/M testing programs provide vehicle emissions test data for a large population of the real-world fleet.
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LE-SOURCE EMISSIONS HO Wow 12( ......... of CO thumb ,~ _ In [~QX Urns 1~ _ 1~ : M' ~ ~100~ ~0 FrP—~E:D O O O WIT .~ ~0 FIGURE 4-5 EPA adjustment to MOBILES FTP data based on the analysis of IM240 data.
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Additionally a fully preconditioned IM240 data set would not provide any measure of cold-start emissions. Two elements are necessary for the accuracy of the MOBILE model regarding cold starts.
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, and 60% of those that fail continue to operate in the airshed after 6 months; and repaired vehicles have a high deterioration rate. ROADSIDE INSPECTION Roadside pullover studies in which a random sample of vehicles are pulled off the road and subject to a loaded-mode emissions tests could perhaps offers the best direct measure of the overall real-world fleet emissions rates if a sufficient sample size is collected.
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AMBIENT AIR-QUALITY MONITORING AND MODELING Using MOBILE-generated emissions data in airshed modeling and comparing the results with measured air-quaTity data offers yet another approach to testing the accuracy of MOBILE emissions predictions. An extensive ozone-modeling and emissions-inventory study comparison was made for the South Coast Air Basin in the Los Angeles area in 1987 (Chico et al 1993; Harley et al 1993b; Wagner and Wheeler 1993~.
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~ I+ / 1 ~ 1 + J I Hi. i+ L ,+ LIT ~ , , 5 ~ , ,' ~ AL / ~ _ I A ~ _ FIGURE 4-6 Comparison of airshed model predictions of diurnal ozone concentrations with observations of ambient ozone concentrations for two different VOC emissions levels.
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om several previous urban tunnel studies. The CRC tunnel study results are summarized in Table 4-4, where the average emissions factors for CO, VOCs, and NOx are given, as well as the ratios of several emissions factors.
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760 A ho CQ A · ~ a)
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The major conclusions of these studies are The sum of ambient liquid gasoline, gasoline vapors, industrial and compressed natural gas contributions agrees reasonably well with and validates the corresponding MOBILE emissions inventory estimates. The discrepancies between CMB ambient- and emissions-derived VOC to NOx ratios and ambient- and emissions-derived acetylene (a major tracer of fingerprint of motor vehicle exhaust)
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contains a recent summary of CMB studies. These studies tend to show that the relative contributions of mobile source VOC emissions to the total inventory determined by CMB are two to three times higher than those estimated using mobile source emissions factor models such as MOBILE and EMFAC.
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Knowledge of the fleet composition and the fuel economy of the different types of vehicles is also required to estimate the emissions inventory. As described in previous sections, the MOBILE model employs a travelbased method to develop emissions inventories.
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These uncertainties arise from small and nonrepresentative emissions data, statistical analyses of these data, and assumptions that underlie and define the MOBILE model's aigorithms and predictions. Quantification of uncertainties is critical for understanding the weaknesses in the model, and identifying the most critical needs for further emissions test data.
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From page 165... ...
and associated adjustment factors and default values forming the basis for MOBILE. In the real world, slower speeds, heavier-Ioaded operating conditions associated with more congested stop-and-go driving conditions, and a more dominant role of cold starts, aD of which produce enriched engine-operating conditions, appear to be the key factors in explaining the discrepancy of the real-world driving cycle compared with the average driving cycle reflected by MOBILE.
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From these sensitivity analyses, EPA should provide guidance to transportation and air-quality planners on the most critical model inputs affecting model results.
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