Comments on EPA s Co-Proposal for the State of Utah s Regional Haze State Implementation Plan (Docket ID No. EPA-R08-OAR )

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1 Comments on EPA s Co-Proposal for the State of Utah s Regional Haze State Implementation Plan (Docket ID No. EPA-R08-OAR ) Dr. H. Andrew Gray Gray Sky Solutions San Rafael, CA March 14, 2016 Introduction The State of Utah submitted State Implementation Plan (SIP) revisions to the US EPA to fulfill the Clean Air Act (CAA) requirements for Best Available Retrofit Technology (BART) in the Regional Haze Rule (RHR) for the pollutants NO X and PM 10. I reviewed EPA s co-proposal 1 in which the State of Utah s proposed better than BART Alternative control strategy was evaluated. States have the flexibility to adopt a BART alternative program as long as the alternative provides greater reasonable progress towards improving visibility than BART. The determination that an alternative control strategy will achieve greater reasonable progress than BART must include an analysis of the projected emission reductions for BART and the Alternative plan, and also an analysis that compares the reasonable progress in improving visibility for both BART and the Alternative strategy. As part of its Regional Haze SIP, Utah has opted to establish an alternative control strategy to BART for NO X emissions. Utah compared its BART Alternative against a BART Benchmark of selective catalytic reduction (SCR) on all four BART-eligible units at Hunter and Huntington (Units 1 and 2 at both power plants). Utah s BART Alternative consists of the shutdown of Carbon Units 1 and 2 and the installation of upgraded NO X combustion controls (new low-no X burners [LNB] and over-fire air [OFA]) on Hunter Unit 3 (all non-bart units). Utah s BART Alternative also includes NO X reductions from installation of upgraded combustion controls (new LNB and separated over-fire air [SOFA]) at Hunter Units 1 and 2 and Huntington Units 1 and 2 (the four BART units). All of the emission reductions in Utah s BART Alternative occurred prior to the final adoption of the Utah Regional Haze SIP, with some occurring as far back at The BART Benchmark includes the four BART units with combustion controls and SCR, Carbon s baseline emissions, and Hunter Unit 3 s emissions with original combustion controls. A summary of the State of Utah s estimated annual average emissions for their BART Alternative plan and the BART Benchmark is provided in Table 1, below. 1 US EPA, Notice of Proposed Rulemaking: Approval, Disapproval and Promulgation of Air Quality Implementation Plans; Partial Approval and Partial Disapproval of Air Quality Implementation Plans and Federal Implementation Plan; Utah; Revisions to Regional Haze State Implementation Plan; Federal Implementation Plan for Regional Haze, EPA-R08-OAR , December 16,

2 Unit Table 1. Estimated Annual Average Emissions under Utah's BART Benchmark and the BART Alternative 2 NO X Emissions (tpy) SO 2 Emissions (tpy) PM 10 Emissions (tpy) Combined (tpy) BART Benchmark Alternative BART Benchmark Alternative BART Benchmark Alternative BART Benchmark Alternative Carbon 1 1, , ,016 0 Carbon 2 1, , ,909 0 Hunter Hunter Hunter 3 6,530 4,622 1,033 1, ,685 5,777 Huntington Huntington Total 13,161 18,882 14,451 6,446 1, ,020 26,164 Alternative - BART EPA considered nine elements that had been presented by Utah in evaluating whether Utah s Alternative plan is better than BART based on the clear weight of evidence that the Alternative would achieve greater reasonable progress than would be achieved through the use of BART controls. The nine elements presented by Utah were: (1) annual emissions reduction comparison for visibility-impairing pollutants, (2) improvement in the number of days with significant visibility impairment, (3) 98 th percentile impact in delta deciviews (Δdv), (4) annual average impact (Δdv), (5) 90 th percentile impact (Δdv), (6) timing for the emissions reductions, (7) IMPROVE monitoring data, (8) energy and non-air quality benefits, and (9) cost. In Section V of its co-proposal, EPA evaluated the weight of evidence, relying primarily on four of the nine considered elements to propose approval of Utah s Alternative plan: (1) annual emissions comparison for two pollutants, (2) improvement in the number of days with significant visibility impairment, (3) IMPROVE monitoring data regarding sulfates, and (4) the early timing for installation of controls. Other considered elements were determined to either marginally support or not support approval of the Alternative plan (non-air quality benefits and implementation costs were not considered important). Despite the fact that the comparison of estimated 98 th percentile visibility impacts at the affected Class I areas 3 did not support the approval of Utah s BART Alternative plan, this key metric was not given sufficient weight in the overall assessment, in favor of less-influential metrics that do not accurately represent peak level visibility impacts within the Class I areas. A reasonable consideration of this visibility metric would lead to disapproval of Utah s Alternative plan. 2 Table 3 of EPA co-proposal (page 43 and 44). 3 Class I federal lands include areas such as national parks, national wilderness areas, and national monuments. These areas are granted special air quality protections under Section 162(a) of the federal Clean Air Act. 2

3 In section VI of its co-proposal, EPA evaluated the weight of evidence, using all nine categories of evidence, with the most weight given to the visibility impacts based on air quality modeling. Using this more-sensible (and defensible) weighting of the evidence, EPA concluded that Utah s demonstration does not show by the clear weight of evidence that the BART Alternative would achieve greater reasonable progress than would be achieved through the installation and operation of BART. The visibility impacts due to emissions from the Utah power plants at the affected Class I areas under the Alternative and BART Benchmark plans were estimated using the CALPUFF dispersion model. Utah conducted the CALPUFF modeling in accordance with federal regional haze modeling guidelines for evaluating in the impacts of individual sources on nearby Class I areas, in which the modeled impact is compared to a clean natural background to determine the increase in visibility impairment (delta dv) on an hourly basis due to the modeled source. The CALPUFF model computes the average visibility impairment (delta dv) for each day of the simulation at each modeled receptor. The maximum impact across all receptors within each Class I area is then determined, which results in a distribution of 365 daily average visibility impacts for each simulated year at each Class I area. A number of descriptive statistics (metrics) were then extracted from the distribution of daily average delta dv impacts. In this report, I examine the merits of each metric for assessing the difference between the two emission scenarios (Utah s BART Alternative and the BART Benchmark). If one considers an appropriate weighting of the nine elements, including the modeled visibility impairment metric, then one would reach the conclusion that EPA arrived at in section VI of its co-proposal, in which it was found that Utah has not demonstrated that the BART Alternative is better than BART. When comparing Utah's BART Alternative to the BART Benchmark, the BART Alternative gets credit for the NO X, SO 2 and PM 10 emission reductions associated with the April 15, 2015 shutdown of the two Carbon power plant units (i.e., the emissions from the Carbon facility were included in the modeling of the BART Benchmark scenario, however the modeled emissions are set to zero for the BART Alternative). The BART Alternative, therefore, includes overall SO 2 (and PM 10 ) emission reductions (for all seven modeled power plant units, combined) and overall NO X emission increases, as well as an overall spatial shift of emissions from the Carbon facility to the Hunter and Huntington facilities. These differences in the distribution of emissions for the three visibility-impairing pollutant species are examined in this report, including the effects that the emission changes have on the modeled visibility impairment at the affected Class I areas. In this report, I make a number of observations concerning the visibility modeling that was used to compare Utah s BART Alternative to the BART Benchmark, as well as the emission data representing each scenario. I determined that the comparisons of the BART Alternative and the BART Benchmark are very sensitive to the assumed baseline SO 2 emission rate for the Carbon facility. The modeled baseline emission rates for the two Carbon units (under the BART Benchmark scenario) were estimated by Utah using 3

4 recent emission data for which the SO 2 emissions were much higher than during many previous recent periods when lower sulfur coal was primarily being used at the facility. 4 If the SO 2 emission rates from the Carbon facility are overestimated by even a modest amount, then the comparison of the BART Alternative to the BART Benchmark will produce quite different conclusions regarding the merits of each scenario, as demonstrated below. Including a relatively small correction in the assumed baseline SO 2 emission rates for the two Carbon units results in an unambiguous determination that the BART Benchmark would achieve greater reasonable progress than the BART Alternative, and therefore, that the BART Benchmark is better than the BART Alternative. The uncertainty in Utah s assumed SO 2 emission rates for the Carbon facility casts considerable doubt Utah s visibility modeling and on the veracity of Utah s comparison of the visibility metrics between the two scenarios. I also examined a BART scenario in which it was assumed that, if the Carbon plant had continued operating beyond April 15, 2015, then a substantial SO 2 emission reduction would have had to occur at the Carbon facility to comply with the emission standards contained in the Mercury and Air Toxics Standards for Power Plants (MATS) rule. For this modeling scenario ( BART with MATS ), the modeled visibility metrics overwhelmingly favor the BART with MATS scenario over Utah s BART Alternative at all affected Class I areas. Finally, I reviewed the CALPUFF modeling that EPA conducted to evaluate the impacts of individual power plant units and to compare the merits of various control technologies for a determination of BART for NO X at each unit. I also performed modeling for this purpose (the results are in Attachment A to this report). Both sets of modeling results provide strong support for EPA s conclusion that SCR is BART at all four units subject to BART. Visibility Protection Objectives When comparing the visibility impacts that would be expected to occur between different control strategies, it is important to consider the intended goals of the RHR and how well each strategy achieves those objectives. As EPA stated in its co-proposal (pg. 9): the purpose of the Regional Haze Rule (RHR) is to remedy and prevent impairment of visibility in Class I areas resulting from anthropogenic air pollution. In evaluating an alternative control strategy to BART, the RHR also includes requirements for conducting dispersion modeling to determine the differences in visibility between control strategies for each impacted Class I area for the worst and best 20 percent of days. To determine the impacts of an individual source (or collection of a few sources) on the best and worst 20 percent of days, it would be necessary to quantify the impacts of all other sources (and background) during each day of the model simulation in order to identify which days are the best and worst. The daily modeled impacts from an individual source can then be extracted from the full distribution of daily model results 4 See Technical Support Document of Vicki Stamper dated March 14, 2016, p.33. 4

5 for just those best and worst days. There are sophisticated dispersion modeling tools that can be used to estimate the impacts of all sources in the region including background (alternatively, actual IMPROVE monitoring data could be used to supplement the model results), however it is not possible with the CALPUFF model, which only determines the impacts from an individual source (or in this case, the seven power plant units). CALPUFF estimates the distribution of daily visibility impacts at each Class I area due only to emissions from the modeled source(s). Therefore it is not possible using the CALPUFF model results alone, to determine which of the modeled daily visibility impacts occurred on the best and worst days. The CALPUFF model estimates 365 daily average visibility impacts for each year of the simulation at each modeled receptor. The location with the maximum daily average visibility impact for each modeled day is then extracted from among all the modeled receptors within each Class I area. The upper end of the distribution of the 365 daily averages represents the maximum or peak impacts that can be expected to occur anywhere in the Class I area from the modeled source(s). The bottom end of the distribution represents the impacts during times when the source is having little impact on visibility in the Class I area. The regional haze modeling guidelines 5 require that CALPUFF modeling be performed using maximum daily emission rates (rather than actual, variable or average emission rates). Using the maximum emission rates for every hour of the simulation implies that the objective of the CALPUFF model simulation is to determine the maximum potential impacts due to the modeled source(s). The distribution of modeled daily visibility impacts can be examined to fully describe the range of estimated impacts due to the modeled source(s). However it should be understood that the CALPUFF model, which uses maximum daily emission rates for every hour of the simulation, is designed to only predict the maximum potential impacts of the modeled source(s). The upper end of the distribution of modeled visibility impacts (e.g., the 98 th percentile impact) has commonly been used 6 to estimate the maximum impact that could be expected from the modeled source(s). However, the bottom part of the CALPUFF-derived distribution does not provide much useful information regarding the impacts from the modeled source(s) on the best days. On many days during the year, the visibility impact from an individual power plant at a specific Class I area will negligible (or fairly low); for example, when the winds are transporting the source s emissions in the opposite direction, and/or when the meteorological conditions do not favor high concentrations. The CALPUFF model output (using maximum daily emission rates) would include such a day as part of the distribution of modeled daily visibility impacts. However, we are not really interested in what the model predicts during a 5 Federal Land Managers Air Quality Related Values Workgroup (FLAG) Phase I Report Revised (2010). 6 The FLAG guidelines (see previous footnote) recommend that visibility impairment be measured using the modeled 98 th percentile daily delta dv, which is the 8th highest daily average (at any receptor within a Class I area) for each year. 5

6 period when the source was either not impacting the Class I area or was contributing negligible amounts to the ambient concentrations in the Class I area. When modeling with a single source model such as CALPUFF, the variation in ambient visibility conditions at each Class I area (which includes impacts from all other sources and the variability due to meteorology) is not accurately represented, so one cannot correlate the model results with the 20% best or worst days at each Class I area. However, it is more likely that the higher modeled impacts from the plant will occur on a 20 percent worst day than a best day because the same meteorological conditions that cause high impacts from the modeled source are also likely to cause high concentration levels from the other sources in the region. So although the upper end of the distribution of modeled impacts does not necessarily correspond identically with the 20 percent worst days, the upper end of the distribution of modeled visibility impacts does provide information on the potential for this source to impact the worst days. Assuming that the model results are accurate, we can be confident that the impact from the modeled source(s) cannot be any higher than the upper end of the model results on any day, including the worst 20 percent days. Protecting visibility on the 20 percent best days would require examining the impacts during that subset of modeled days when the cumulative impacts from all regional sources (plus background) at each Class I area are low. Again, CALPUFF is not designed to do this type of analysis, so the model results cannot easily be used to evaluate the best 20 percent days (the clean end of the distribution of impacts from all other sources and background). The bottom end of the distribution of modeled daily impacts therefore offers very little information regarding the impacts of the modeled source(s) during the best 20 percent days. Lowering the peak modeled impacts from an individual source will necessarily limit the maximum contribution during ANY day at the modeled Class I area, including during the worst days. Therefore, more emphasis should be placed on the upper end of the CALPUFF-modeled distribution of visibility impacts. Visibility Metrics The upper end of the distribution of daily CALPUFF-modeled visibility impacts provides useful information regarding the potential peak impacts from the modeled source(s), however the lower end of the distribution does not provide much meaningful information. Therefore, more emphasis (greater weight) should be placed on those metrics that describe the upper end, or peak part of the distribution. Some metrics are better than others at describing peak visibility impacts. EPA analyzed the results of Utah s CALPUFF modeling to compare the expected visibility impacts associated with the BART Alternative control scenario with the impacts that would result with the BART Benchmark scenario. A number of different visibility metrics were analyzed for this comparison. The model results, using Utah s assumed 6

7 emission rates for each facility 7 under each scenario, showed that some of the computed visibility metrics were slightly better and some were slightly worse for the BART Alternative relative to the BART Benchmark. The CALPUFF model was used to predict concentrations of visibility-impairing pollutant species on an hourly basis which were then used to compute the highest daily average visibility impacts (delta dv) at each Class I area. For each year, the 365 modeled daily values at each Class I area comprise a distribution of impacts. The impacts from the modeled power plant units (under each emission scenario) can be fully described by examination (and comparisons) of the entire distribution. However it is impossible to describe an entire distribution with a single statistic (such as an average or median), especially if we are mostly interested in the upper end of the distribution, i.e., those days when peak impacts occur. The visibility metrics represent various statistical measures that describe different parts of the complete distribution of impacts. In other words, each metric illuminates a different part of the complete distribution. However, some metrics are better than others are describing peak impacts. The ability of each visibility metric to represent peak impacts is examined below. Exceedance Data. Two of the visibility metrics Utah used consisted of counting the number of days in which the modeled visibility impact (delta dv) within each Class I area exceeded a specific threshold (0.5 dv and 1.0 dv). Although this metric provides information at two additional points within the distribution of daily impacts, these two points are somewhat arbitrary and can represent very different points on the distribution from one Class I area to the next. The number of days that exceeds each threshold provides an indication of the percentile level associated with the threshold. For example, if the modeled daily average delta dv exceeds 0.5 for 182 out of the 365 modeled days in a given year, then this represents (approximately) the 50 th percentile, or median, impact at that Class I area. If, on the other hand, the modeled number of exceedances is 8 (out of 365), then the 0.5 delta dv represents (approximately) the 98 th percentile impact. Depending on the modeled number of exceedances for a given threshold, the threshold may represent very different percentiles of the complete distribution of daily visibility impacts. If the modeled number of exceedances is high at a Class I area, then the threshold level does not represent a peak impact at the Class I area. In fact, the higher the exceedance counts, the lower percentile that the threshold level represents. If the modeled number of exceedances is low, representing just a few days in which the modeled visibility impacts exceeded the threshold, then the threshold does represent a peak impact. However, if there are only a few days in which the modeled visibility impact exceeds the given threshold, then a relatively small change in emissions (for example when comparing the BART Alternative to the BART Benchmark) may result in one or more of the modeled daily impacts crossing over the threshold value (in either 7 Utah s assumed SO 2 emission rates for the Carbon facility under the BART Benchmark scenario may have been overstated. Modeling using a Corrected SO 2 BART Benchmark emission rate was examined in the following section of this report. 7

8 direction), depending on how close each modeled daily impact is to the threshold value. The result is that the change in exceedance counts will go up or down based on the proximity to the threshold value of a few daily impacts. The difference in the exceedance counts when both values are relatively low is therefore an unstable statistic and is not very robust (i.e., there is a large uncertainty associated with the difference). If the annual exceedance counts for both of the compared scenarios are relatively high (e.g., dozens or hundreds of days) then the threshold value does not represent a peak visibility impact. If the exceedance counts are low (e.g., less than about 20), then the statistic is really only usable if the number of exceedances for one scenario is much greater than the other scenario (which would indicate that the threshold represents a peak impact for one scenario but a non-peak level for the other scenario). If both scenarios produce similar low exceedance counts, then the statistic is quite uncertain (and the comparison of the two scenarios is up to the whims of just a few daily averages that might happen to move up or down across the threshold level). The exceedance counts for a specific emission scenario represent very different percentile levels for each modeled Class I area (and therefore the simple addition of the exceedance counts across all the modeled Class I areas is problematic, in that each value represents a different percentile level). In addition, relatively high exceedance counts do not represent the peak of the distribution of visibility impacts. Finally, low exceedance counts, while representing peak impacts, are highly uncertain and so the difference between two relatively low exceedance counts is an unreliable metric. For these reasons, the exceedance counts should be given much lower weight than other more robust metrics which better represent peak impact levels and are less uncertain (such as the 98 th percentile delta dv, or even the 90 th percentile delta dv). Annual Average Delta dv. The annual average of the modeled daily visibility impacts represents a central statistic and is only marginally useful in describing peak impacts (to the extent that the collection of peak impacts will have an effect on the overall average). The annual average includes numerous days in which the modeled impacts were very small or negligible. Therefore the modeled annual average delta dv should be given little weight. 98 th Percentile Delta dv. The maximum (highest) modeled daily visibility impact values (either averaged over three years, or the maximum across all three years) could certainly be used to represent peak modeled impacts. However, using the 98 th percentile (8th highest out of 365 days) is somewhat more robust (and more stable) and is also representative of peak impact levels. So, for the same reasons that the 98 th percentile is commonly used for criteria pollutant NAAQS, and is also recommended by FLAG for assessing visibility impacts from individual sources, it can be used as a more robust estimate of the "peak" impacts than the maximum value. The absolute maximum impact, while obviously also 8

9 representing peak impacts, is somewhat more unstable and is subject to extreme meteorological conditions. Three different metrics that all make use of the 98 th percentile visibility impacts were developed by Utah: (1) three-year average of the 98 th percentile impact, (2) the maximum annual 98 th percentile impact (highest of three years), and (3) the 98 th percentile across all three modeled years (24 th highest out of 1095 days total). All three of the 98 th percentile metrics effectively represent peak level impacts. The maximum impact across all three years may be strongly influenced by a particularly bad meteorological year. The 98 th percentile impact averaged over the three model years somewhat mitigates that problem (by averaging in two other modeled years in addition to the maximum). The 98 th percentile across all 3 years (24 th high) is quite similar to the three-year average 98 th percentile impact and produces quite similar results (for example, see Tables 5 and 7, below). All three of the 98 th percentile visibility impact metrics produce similar conclusions regarding the comparison between the BART Alternative control strategy and the BART Benchmark. The three-year average of the 98th percentile represents peak level impacts extremely well, is less influenced by a single extreme meteorological year than the maximum 98 th percentile impact (highest of three years), and is the FLAGrecommended visibility metric when modeling the impacts of individual sources at Class I areas using the CALPUFF model. In discussing the merits of the 98 th percentile visibility impact metric (co-proposal, Page 74), EPA states: [W]e have given considerable weight to this metric in previous actions where we have evaluated BART alternatives as it captures a source s likely greatest visibility impacts at a Class I area; as such, it is a useful comparison point for determining whether one emission control scenario will have a greater impact on visibility improvement than another. EPA also indicates (co-proposal, page 94) that [t]he 98 th percentile visibility impact is a key metric recommended by the BART Guidelines when selecting BART controls. Therefore, this visibility metric should be given the highest weight among the various modeled metrics. 90 th Percentile Delta dv. The 90 th percentile visibility impact over all three modeled years (110 th highest daily impact out of 1095 values) represents a portion of the upper end of the distribution of impacts, however this metric is not nearly as good a descriptor of peak impacts as the 98 th percentile metric. The 98 th percentile level can be expected to be reasonably close to (but slightly lower than) the maximum impact level, whereas the 90 th percentile level will be far less correlated with the maximum impact. At most of the Class I areas, there are numerous days in which the modeled visibility impacts are either very low or negligible, so that the 90 th percentile impact out of the entire annual distribution essentially represents a much lower percentile point of the distribution of visibility 9

10 impacts that would actually be of any concern. 8 If the objective is to use a metric that is representative of the peak impacts, then the 90 th percentile metric should be given lower weight than the 98 th percentile impact metric (but somewhat more than the exceedance data metrics). EPA s Comparison of Alternative and BART: 1. Emissions If the emission rates for one of the two control strategies being compared are lower for all visibility-impairing pollutant species than for the other strategy, and the spatial distribution of emissions are not significantly different between the two strategies, then the comparison of emission rates can be used, on its own, to justify the selection of the control strategy with lower emissions. If dispersion modeling were conducted in such a case, it would necessarily show lower impacts for the strategy with the lower emission rates (for all visibility metrics). On page 44 of EPA s co-proposal, it states: [T]he State must provide a determination or otherwise based on the clear weight of evidence that the alternative achieves greater reasonable progress than BART. There are two different tests that the State may use to demonstrate greater reasonable progress: (1) a demonstration that the alternative would achieve greater emission reductions than BART, and (2) a weight-of-evidence analysis that can include any and all pertinent information. Although Utah asserted that their alternative plan demonstrates greater reasonable progress than BART using the greater emission reductions test, it also chose to conduct a weight-of-evidence analysis. The first emission reduction test requires that the state show that the alternative plan s distribution of emissions is not substantially different than under BART and that the alternative measure results in greater emission reductions. However, EPA rejected Utah s use of this test (in both of its co-proposals), indicating that when the alternative strategy results in reduced emissions of one pollutant but increased emissions of another, it is not appropriate to use the greater emission reductions test. Instead, the proper approach is to employ the clear weight-of-evidence test in order to demonstrate that the alternative achieves greater reasonable progress than BART. EPA explains (on page 88 of its co-proposal): A comparison of mass emissions from multiple pollutants (such as NO X and SO 2 ) is not generally informative, particularly in assessing whether the alternative approach provides for greater reasonable progress towards improving visibility. Instead, when emissions of one or more pollutants increases under an alternative, EPA has given the most weight to the visibility impacts based on air quality modeling and used modeling to determine whether or not a BART Alternative measure that relies on interpollutant trading results in greater reasonable progress. 8 For the same reason, the median visibility impact is an extremely weak metric. The median value would be over-influenced by the numerous low-impact and negligible impacts days that comprise the lower half of the annual distribution. 10

11 EPA rejected the emission reduction test, indicating that although in the aggregate there are fewer SO 2 and PM 10 emissions for the BART Alternative, the total NO X emissions are greater under the BART Alternative than for the BART Benchmark. The clear weight of evidence test can include any and all information pertinent to overall visibility improvement between the two scenarios. As part of its weight of evidence test, Utah considered a number of elements to support its assertion that the BART Alternative results in greater reasonable progress than the BART Benchmark. Despite the fact that EPA rejected the emission reduction test, Utah used the same emission comparison as the first element in its weight of evidence analysis test. However, for the same reason that EPA rejected the greater emission reductions test, this element should be given little or no weight as evidence that the BART Alternative results in greater reasonable progress than the BART Benchmark. The emissions of all visibility-impairing pollutant species are not uniformly lower for Utah s BART Alternative relative to the BART Benchmark (the combined SO 2 and PM 10 emission rates are reduced, however the combined NO X emission rates are higher under the BART Alternative). There is also a spatial shift in emissions between the two scenarios (between the Carbon plant emissions and the Hunter/Huntington emissions). Therefore the overall emission reductions cannot be used to support the assertion that the BART Alternative results in greater reasonable progress than the BART Benchmark. Instead, dispersion modeling must be conducted to determine whether the emission changes (overall reductions) would result in better than BART visibility. Therefore, the better than BART demonstration must rely on the visibility dispersion modelling results. EPA s Comparison of Alternative and BART: 2. Modeling In addition to the comparison of annual emission rates (discussed in the previous section), Utah s weight of evidence analysis also included: (1) improvement in the modeled number of days with significant visibility impairment, (2) 98 th percentile modeled visibility impact, (3) annual average modeled visibility impact, (4) 90 th percentile modeled visibility impact, (5) timing of emission reductions, (6) IMPROVE monitoring data (sulfate trend data), (7) energy and non-air quality benefits, and (8) costs. The first four of these elements were determined through the use of visibility dispersion modeling, and are addressed below. EPA did not perform independent modeling to compare the visibility impacts between Utah s BART Alternative and the BART Benchmark scenarios. Instead, EPA relied on the modeling conducted by Utah for this purpose. Utah conducted dispersion modeling using the CALPUFF model (with estimated maximum daily emission rates, as shown in Table 2) to compare the visibility impacts associated with the two different emission scenarios. A number of visibility impairment metrics were developed from the model s output and the resulting metrics were then compared between the two emission scenarios. 11

12 A careful examination of the modeled visibility metrics that Utah used to support its assertion that the BART Alternative control strategy is better than BART provides insight into why some of the metrics favor the BART Benchmark and why others favor the BART Alternative plan, and why many of the metrics appear to favor the BART Benchmark at some Class I areas, and the BART Alternative at other Class I areas. Unit Table 2. Modeled Emission Rates under Utah's BART Benchmark and the BART Alternative 9 NO X Emissions (lb/hr) SO 2 Emissions (lb/hr) PM 10 Emissions (lb/hr) Combined (lb/hr) BART Benchmark Alternative BART Benchmark Alternative BART Benchmark Alternative BART Benchmark Alternative Carbon Carbon Hunter Hunter Hunter Huntington Huntington Total Alternative - BART When comparing the distribution of emissions between Utah s BART Alternative and BART Benchmark scenarios, it can be seen that in addition to the overall reduction in SO 2 and PM 10, and an overall increase in NO X emissions, there is also a spatial shift in emissions from the Carbon facility to the Hunter and Huntington facilities. Examination of the emission data used by Utah for its visibility modeling (Table 2) shows that under the BART Benchmark scenario, there would be 3,160 lb/hr more SO 2 emissions at the Carbon facility than for the BART Alternative plan, and that the overall reduction of NO X emissions for the BART Benchmark scenario relative to the Alternative (1,660 lb/hr) is due to an increase of NO X emission at Carbon (1,134 lb/hr), combined with a larger decrease of NO X emissions at the Hunter and Huntington facilities (2,794 lb/hr). There is also an increase in PM emissions (30 lb/hr) at the Carbon facility under the BART Benchmark scenario. The Carbon facility is located about 63 km NNE of the Hunter facility and about 43 km NNE of the Huntington facility. Examination of typical transport patterns for emissions from the three facilities, in addition to comparisons of model results for the three individual facilities, shows that emission from the Carbon facility have a somewhat greater impact (per lb of emissions) at Arches National Park ( NP ) and at Flat Tops Wilderness than emissions from the Hunter and Huntington facilities. 9 Utah Division of Air Quality. Air Quality Modeling Protocol: Utah Regional Haze State Implementation Plan. Appendix B. February 13,

13 For a given level of emissions, the spreading out of those emissions from a single location to multiple locations will tend to cause a shift in the modeled distribution of visibility impacts, potentially resulting in an increase in the modeled visibility impact during many of the days in the bottom part of the distribution. Depending on the locations of the multiple emission sources and receptors, many more of the modeled transport pathways may direct emissions from multiple locations towards a specific receptor than for a single emission source. Low-level impacts from either of the (two or more) multiple source locations will occur more frequently than for a single source location (in other words, there is a higher probability that one of the emitted plumes will have a non-negligible impact at a receptor when there are more plumes in more locations being emitted). The overall distribution of modeled visibility impacts will reflect this by having an increase in the number of days that exceed threshold levels at lower percentile levels. Conversely, consolidating emissions (for example, from three source locations to two) can result in fewer lower-level impacts. Although Utah s BART Alternative would result in an overall reduction in combined emissions, it can also be seen that the BART Alternative plan would essentially spatially consolidate NO X emissions (relative to the BART Benchmark), which results in fewer lower-level impacts at most of the modeled Class I areas. Improvement in the Modeled Number of Days with Significant Visibility Impairment. The modeled number of days in which the daily average delta dv exceed the two threshold levels (0.5 dv and 1.0 dv) are shown in Tables 3 and 4. As described above, the spreading out of emissions for the BART Benchmark scenario relative to the BART Alternative scenario results in fewer lower-level impacts which is why the number of days in which delta dv exceeds 0.5 is lower for the BART Alternative scenario. 10 Examination of the modeled visibility impacts at the three most impacted Class I areas (Arches NP, Canyonlands NP and Capitol Reef NP) reveals that for Canyonlands NP and Capitol Reef NP, the peak modeled impacts (see Table 5, for example) are significantly lower for the BART Benchmark plan than for the BART Alternative despite the fact that the number of delta dv exceedances of 0.5 dv is slightly lower for the BART Alternative scenario. Emissions from the Carbon facility in the BART Benchmark scenario have a larger effect on visibility at Arches NP (and also Flat Tops, which is somewhat closer to Carbon than the other two facilities). For this reason, both the peak modeled impact and number of exceedance days are higher under the BART Benchmark scenario at these two Class I area. 10 Examination of the peak modeled impacts at each of the Class I areas, for example using the 98 th percentile impacts in Table 5, shows that the delta dv threshold of 0.5 represents a lower-level impact at all the Class I areas. The 98th percentile (peak) impacts were between 2 and 12 times as large as the 0.5 threshold level at all Class I areas (except Zion NP). 13

14 Table 3. Average Modeled Number of Days/Year with Delta dv 0.5 BART Alternative BART Benchmark Benchmark vs. Alternative* Arches National Park Black Canyon of the Gunnison National Park Bryce Canyon National Park Canyonlands National Park Capitol Reef National Park Flat Tops Wilderness Grand Canyon National Park Mesa Verde National Park Zion National Park Total (All 9 Class I Areas) * Positive values favor BART Benchmark (lower modeled visibility impact); negative values favor Alternative Table 4. Average Modeled Number of Days/Year with Delta dv 1.0 BART Alternative BART Benchmark Benchmark vs. Alternative* Arches National Park Black Canyon of the Gunnison National Park Bryce Canyon National Park Canyonlands National Park Capitol Reef National Park Flat Tops Wilderness Grand Canyon National Park Mesa Verde National Park Zion National Park Total (All 9 Class I Areas) * Positive values favor BART Benchmark (lower modeled visibility impact); negative values favor Alternative Table 4 shows the modeled number of days in which the daily average delta dv exceeds 1.0. As discussed above, the spatial shift in emissions between the two control strategies is responsible for an increase in the number of lower-level impacts which was reflected in the number of delta dv exceedances of 0.5 (Table 3). In contrast, the 1.0 delta dv threshold level, while still far below peak modeled levels at the three highest impacted Class I areas, is not very far below peak impact levels at the other Class I areas, and therefore represents a markedly higher percentile level than the 0.5 threshold. Not surprisingly, the modeled number of days exceeding the 1.0 delta dv threshold is the same or higher for Utah s BART Alternative plan at all Class I areas except for Arches NP and Flat Tops Wilderness. The BART Benchmark and BART Alternative scenarios result in almost the same number of days in which the modeled delta dv exceeds 1.0 at all Class I areas except Arches NP and Flat Tops Wilderness. The overall total difference for all Class I areas combined (6 days per year) is due to the higher impacts of Carbon emissions at Arches 14

15 NP (average of 9 days per year) and Flat Tops Wilderness (2 days per year). The average number of exceedances at the other seven parks are all within 2 days/year between the two scenarios, with the BART Benchmark scenario producing slightly better results at four of the Class I areas. Therefore, the results shown in Table 4 cannot be used to support the assertion that the BART Alternative scenario is better than BART. 98 th Percentile Modeled Impact. The increase in SO 2 emissions at Carbon and the shift of NO X emissions from the Carbon facility to the Hunter and Huntington facilities (which is what the BART Benchmark plan represents relative to the BART Alternative plan) results in a modest improvement in the three-year average modeled 98 th percentile visibility impacts (delta dv) at Arches NP and Flat Tops Wilderness (a difference of 0.21 dv and 0.15 dv, respectively), as shown in Table 5. However the BART Alternative plan would result in higher peak impacts than the BART Benchmark at all of the other seven Class I areas, especially at Capitol Reef NP and Canyonlands NP (a difference of 0.59 dv and 0.78 dv, respectively), which are two of the most impacted Class I areas. The three-year average modeled 98 th percentile delta dv is more than 15 percent higher for the BART Alternative plan than for the BART Benchmark at these two heavily impacted Class I areas. As discussed above, Arches NP and Flat Tops Wilderness are impacted by emissions from the Carbon facility somewhat more than emissions from the other two source locations (Hunter and Huntington). Therefore, even though the peak modeled impacts were lower for the BART Benchmark at most of the Class I areas (despite an overall increase in combined emissions relative to the Alternative plan), the peak modeled impacts are higher for the BART Benchmark than the BART Alternative at these two Class I areas. Table 5. Three-Year Average Modeled 98 th Percentile Delta dv BART Alternative BART Benchmark Benchmark vs. Alternative* Arches National Park Black Canyon of the Gunnison National Park Bryce Canyon National Park Canyonlands National Park Capitol Reef National Park Flat Tops Wilderness Grand Canyon National Park Mesa Verde National Park Zion National Park Average (All 9 Class I Areas) * Positive values favor BART Benchmark (lower modeled visibility impact); negative values favor Alternative 15

16 Although the three-year average modeled 98 th percentile impacts are higher at Arches NP and Flat Tops Wilderness for the BART Benchmark, the peak impacts are lower at all other Class I areas, and the overall average impact across the nine modeled Class I areas is also lower. The 0.14 dv average difference represents a difference in cumulative impact (the sum of the impacts at all nine Class I areas) of 1.30 dv. Although the modeled three-year average 98 th percentile impact metric shows lower impacts for the BART Alternative at two of the Class I areas, the difference favors the BART Benchmark at the other seven facilities and also for the overall average of all Class I areas, and therefore this metric does not support an assertion that Utah s BART Alternative is better than BART. In fact, this metric indicates the opposite: that the BART Benchmark is better than Utah s BART Alternative. As discussed above, the modeled 98 th percentile visibility impact in the highest year is a somewhat less robust metric then the three-year average. Examination of the model results using the highest-year metric is shown in Table 6. Although the three-year average modeled 98 th percentile delta dv impact at Arches NP favors the BART Alternative scenario (Table 5), the 98 th percentile impact in the highest year at Arches was higher for the BART Alternative scenario than for the BART Benchmark (Table 6). The difference in the peak impacts for the highest year favors the BART Benchmark scenario at five of the nine Class I areas, and also for the average across all nine Class I areas. The BART Benchmark scenario is significantly better than Utah s BART Alternative scenario at Canyonlands NP (difference = 0.76 dv) and Capitol Reef NP (0.57 dv), which are two of the most impacted Class I areas, and considerably better at Grand Canyon (0.17 dv). The two scenarios produce very similar results at five of the Class I areas (difference less than 0.1 dv), with the BART Alternative plan producing favorable results only at Flat Tops Wilderness (difference = 0.43 dv). Although there is somewhat more variability in the results for the highest-year peak impact metric than for the three-year average peak impact metric, this metric also does not support the conclusion that Utah s BART Alternative plan is better than BART, but rather, similar to the three-year average, that the BART Benchmark plan is actually better than Utah s BART Alternative. 16

17 Table 6. Modeled 98 th Percentile Delta dv in Highest Year BART Alternative BART Benchmark Benchmark vs. Alternative* Arches National Park Black Canyon of the Gunnison National Park Bryce Canyon National Park Canyonlands National Park Capitol Reef National Park Flat Tops Wilderness Grand Canyon National Park Mesa Verde National Park Zion National Park Average (All 9 Class I Areas) * Positive values favor BART Benchmark (lower modeled visibility impact); negative values favor Alternative The State of Utah also computed the 98 th percentile visibility impact across all three model years (equal to the 24 th highest out of 1095 daily average impacts), as shown in Table 7. This peak impact metric produces fairly similar results to the three-year average 98 th percentile impact (Table 5). Similarly to both of the other 98 th percentile metrics (Tables 5 and 6), the use of this metric would lead to the conclusion that the BART Benchmark is better overall than Utah s BART Alternative. Table 7. Modeled 98 th Percentile Delta dv Across All Three Years BART Alternative BART Benchmark Benchmark vs. Alternative* Arches National Park Black Canyon of the Gunnison National Park Bryce Canyon National Park Canyonlands National Park Capitol Reef National Park Flat Tops Wilderness Grand Canyon National Park Mesa Verde National Park Zion National Park Average (All 9 Class I Areas) * Positive values favor BART Benchmark (lower modeled visibility impact); negative values favor Alternative Annual average modeled impact. The modeled annual average visibility impacts are shown in Table 8 for the BART Benchmark and BART Alternative emission scenarios. The annual average delta dv includes the modeled impacts during all 1095 days of the model simulation. A significant fraction of the modeled daily impacts are in the bottom part of the distribution 17

18 and therefore do not reflect peak level impacts. As discussed above, the spatial shift of emissions between the BART Alternative scenario and the BART Benchmark scenario causes numerous lower-level impacts to increase in the BART Benchmark, which has the effect of slightly increasing the annual average. Examination of Table 8 shows that, although the BART Alternative has a lower modeled annual average impact at six of the nine Class I areas (as well as the average of all nine), the differences are quite small at all Class I areas (less than 0.1 dv). The overall delta dv difference (average of all nine Class I areas) is only dv, which, at best, only marginally supports the BART Alternative scenario over the BART Benchmark. EPA indicated clearly why this metric cannot be used to support Utah s assertion that the BART Alternative is better than BART (co-proposal, page 95-96): We note that the difference in the annual average metric of dv only marginally supports the BART Alternative and that this metric shows less or equal visibility improvement at four of the nine Class I areas. Because the annual average metric averages over all days, it does not represent the benefits of the BART Alternative on the maximum impact days. In previous evaluations of BART alternatives we have relied on either the 98 th percentile metric or the average improvement for the worst 20% IMPROVE monitoring days to evaluate greater reasonable progress. Therefore, we propose to find that the information from the annual average metric does not support a conclusion that the BART Alternative achieves greater reasonable progress than the BART Benchmark. Table 8. Modeled Annual Average Delta dv BART Alternative BART Benchmark Benchmark vs. Alternative* Arches National Park Black Canyon of the Gunnison National Park Bryce Canyon National Park Canyonlands National Park Capitol Reef National Park Flat Tops Wilderness Grand Canyon National Park Mesa Verde National Park Zion National Park Average (All 9 Class I Areas) * Positive values favor BART Benchmark (lower modeled visibility impact); negative values favor Alternative EPA concludes (co-proposal, page 101) that: We propose to find that the slight comparative benefits in the annual average impacts are not compelling evidence that the BART Alternative will provide for greater reasonable progress than BART. 18