EPA REGION 6

                   2015 8-HOUR OZONE TRANSPORT SIP PROPOSAL

                          TECHNICAL SUPPORT DOCUMENT

                             February 1[st], 2022

                                      By
                                  Erik Snyder
                       Lead Regional Air Quality Modeler
                          EPA Region 6  -  Dallas, TX

Contents
1.	Maintenance Receptor Methodology	3
2.	Underestimation Concerns	36
a.	Underprediction in the base case 2012 period of Texas ozone at DFW/HGB monitors	37
  b.	Underprediction in the TCEQ's base case 2012 period at downwind areas in Midwest and West.	52
c.	Underestimation of Future year projections at DFW/HGB receptors	57
d.	Underestimation in TCEQ's 2023 Future year projections at downwind areas	62
e.	Summary	70
3.	Comparison of TCEQ and EPA 2023 future modeling and identified maintenance and nonattainment receptors	71
4. 	Other Potential Modeling Concerns	82
5.	TCEQ Other Factor Analysis (Weight of Evidence)	83
6.	EPA Summary	102

This Technical Support Document (TSD) provides additional analysis of TCEQ's air quality modeling relied upon in its August 17, 2018, SIP submission addressing interstate transport obligations for the 2015 ozone NAAQS. As discussed in the notice of proposed disapproval, EPA has identified several concerns with the reliability in TCEQ's modeling and application of those model results in assessing Texas' potential contributions to downwind ozone. States are free to develop their own modeling, but that modeling and analysis of the modeling and other technical analyses must be technically supportable, and the EPA is obligated to assess and evaluate the reliability of each state's technical demonstration when determining whether the Act's requirements are met. Based on the EPA's evaluation of the SIP submission, the EPA is proposing to find that TCEQ's August 17, 2018, SIP submission does not meet the State's obligations with respect to prohibiting emissions that contribute significantly to nonattainment or interfere with maintenance of the 2015 ozone NAAQS in any other state. EPA's assessment and evaluation of the reliability of the technical demonstration in TCEQ's SIP is discussed in this document. Oklahoma also relied on TCEQ's modeling analysis in their SIP. EPA's concerns with TCEQ's SIP also impact the approvability of Oklahoma's 2015 Ozone Transport SIP.

1.	Maintenance Receptor Methodology
      As discussed in Section V.A of this action, in addition to the use of an alternative modeling platform, TCEQ also created its own method for identifying maintenance receptors. TCEQ has not adequately explained or justified how its method for identifying maintenance receptors reasonably identifies areas that will have difficulty maintaining the NAAQS. The EPA proposes to find that TCEQ has not provided a sufficient technical basis for how its chosen methodology gives meaning to the CAA's instruction that states submit good neighbor SIPs that prohibit their states' emissions from interfering with the maintenance of the NAAQS in another state.
      In North Carolina v. EPA, 531 F.3d 896, 909-11 (D.C. Cir. 2008), the D.C. Circuit rejected the EPA's CAIR on the basis that the EPA had not adequately given meaning to the phrase "interfere with maintenance" in the good neighbor provision. Specifically, North Carolina argued that it had counties that were projected to attain the NAAQS in the future analytic year but were at risk of falling back into nonattainment due to interference from upwind sources, particularly given year-to-year variability in ozone levels. The court agreed, holding that the EPA's rule did not adequately protect "[a]reas that find themselves barely meeting attainment." Id. at 910. Consequently, the EPA has developed a methodology, as described elsewhere in this action and used in its 2011 CSAPR and its 2016 CSAPR Update and Revised CSAPR Update, for identifying areas that may struggle to maintain the NAAQS. See 76 FR at 48227-28. The EPA's approach to addressing maintenance receptors was upheld in the EME Homer City litigation. See 795 F.3d 118, 136-37. It was also upheld in Wisconsin. 938 F.3d at 325-26. In Wisconsin, the court noted that four upwind states were linked only to maintenance receptors and rejected the argument that application of the same control level as the EPA imposes for those states linked to nonattainment receptors was unreasonable or unlawful absent a particularized showing of overcontrol. Id. at 327.
      To explain the differences between TCEQ's and the EPA's methodology for identifying maintenance receptors, it is helpful to provide some additional context of how the EPA projects future air quality. The EPA's air quality modeling guidance has long recommended developing a base design value (DV) (i.e., the design value that will be used as a starting point to model and analyze for purposes of projecting future air quality concentrations) that is the average of three DVs spanning a five-year period, centered around one year for which an emissions inventory will be submitted (e.g., if 2011 was the base emissions inventory year, a state would use monitored values from 2009-2011, 2010-2012, 2011-2013 as the starting point for projecting air quality concentrations in future years). The average of these three DVs is then multiplied by a relative response factor (RRF) to generate an average DV for the future year. If a receptor's average future year DV is greater than or equal to the level of the NAAQS, and the receptor has recent monitored data that violates the NAAQS, that receptor is considered a "nonattainment" receptor at Step 1. To identify maintenance receptors, the EPA's methodology looks to the highest DV of the three DVs used to calculate the 5-year weighted average design value (e.g., in the 2011 example, if 2009-2011 had the highest design value of 2009-2011, 2010-2012, and 2011-2013). The EPA then applies the same relative response factor to that highest design value to generate a projected future maximum design value. Where a receptor's maximum design value exceeds the level of the NAAQS, the EPA has deemed those receptors to be "maintenance" receptors. This methodology was designed to address the D.C. Circuit's holding that the CAA's "interference with maintenance" prong requires states and the EPA to protect areas that may struggle with maintaining the standard in the face of inter-annual variability in ozone-conducive conditions.
      In its modeling, TCEQ adopted an identical approach to the EPA's for identifying nonattainment receptors -- it looked at three sets of DVs over a five-year period and averaged those DVs to generate a base year DV. TCEQ then applied a relative response factor to that base year design value to project a receptor's average design value in the future year. For its maintenance receptors, however, TCEQ used only the most recent design value of the set of three DVs, regardless of whether the most recent design value was highest or lowest, instead of considering variability in conditions over a five-year period, or using the highest DV of the three DVs making up the base year design value. TCEQ's proffered explanation for using the most recent DV to identify maintenance receptors was that the latest DV "takes into consideration . . . any emissions reductions that might have occurred." However, TCEQ in its submission does not explain how this methodology takes into account meteorological variability in identifying those areas that may be meeting the NAAQS or that may be projected to meet the NAAQS but may nevertheless struggle to maintain the NAAQS.
      TCEQ argued that the 3-year DV used includes some meteorological variability. Unfortunately, the three years of variation that TCEQ accounted for is already built into the structure of the standard. Thus, the TCEQ method gave no consideration to the variability between calculated DVs, which provides a direct indication of the difficulty a receptor will have in maintaining the standard. In other words, to determine whether a receptor will have difficulty maintaining the standard, one must consider the variation in the metric that will be used to determine compliance with the standard. An indication of the variability of a metric cannot be determined by only considering a single estimate of that metric.
      TCEQ's stated purpose in using the most recent DV was to capture more recent emissions reductions.  TCEQ's methodology, however, limits receptors which could be identified as maintenance receptors, compared to the EPA's methodology largely because it only looks at one design value period rather than selecting the maximum of the three DV periods EPA's methodology considers. Thus, TCEQ's methodology greatly reduces the probability that meteorological conditions which make it difficult to maintain the standard will be considered. As discussed further below, the effects of emissions trends are already captured through other aspects of the methodology to identify receptors. So, in trying to give more weight to emission reductions, by selecting only one design value (2012-2014) for its base year, TCEQ's methodology did not give any consideration to interannual variability in ozone-conducive meteorology as does the EPA's method.
   The EPA's methodology, using the maximum DV which accounts for the variability in ozone concentrations and DVs due to changes in meteorology over the five years of the base year DV period, was designed to identify those areas that might struggle to maintain the NAAQS in particularly ozone conducive conditions. TCEQ claimed that the EPA's method undervalues changes in air quality due to emission reductions and overvalues changes due to variation in meteorology. TCEQ pointed out that emissions nationwide are generally trending downward as a result of federal motor vehicle standards and other technological improvements. The EPA agrees that ozone levels generally trend downward, but there is not a steady decline from year to year in ozone concentrations. Rather, ozone levels tend to vary from year to year with some years showing an increase instead of a decrease mainly due to inter-annual variability in ozone-conducive meteorology. The variation of DVs at individual monitors from year to year can be significant, even where emissions trend downwards. The EPA also assessed a number of monitored DV trends that were provided in TCEQ's SIP submission and previous TCEQ attainment demonstration SIPs indicating that there are at times large annual fluctuations upward from year to year in monitored DVs (sometimes 2-3 ppb increase in one year) that are due to variations in meteorology. As discussed further below, EPA also assessed recent monitoring trends in Texas and in downwind areas identified by EPA or TCEQ's modeling. This is precisely why it is important to consider highly variable meteorology and its influence on DVs -- the issue at the heart of the D.C. Circuit's finding on "interference with maintenance" in North Carolina. Areas that are required under the Act to attain by an attainment date may fail to attain because of a combination of both local emissions, upwind emissions, and ozone conducive meteorology, among other factors. The North Carolina decision made clear that in interpreting the good neighbor provision, upwind state and the EPA obligations to reduce emissions must account for variable conditions that could cause an area that is sometimes attaining the NAAQS to fall out of attainment. See also Wisconsin, 938 F.3d at 327 ("Variations in atmospheric conditions and weather patterns can bring maintenance receptors into nonattainment even without elevated emissions.").
      In addition, TCEQ claimed that its use of the 2012-2014 DV (i.e., the most recent in the 5-year base period it examined) is more reliable than the EPA's method because the more recent DV accounts for both emission reductions and because there is a shorter interval between the monitored DV and the projected DV. As we note elsewhere, the TCEQ's base year modeled inventory is 2012 emissions and the TCEQ's model projections for 2023 include the expected emission reductions from 2012 thru 2014 as well as to 2023. By just using the 2012-2014 DV data, TCEQ claimed they are giving weight to emission reductions during the final base years where EPA's method does not. The effect of emission reductions, however, is already factored in the modeling analysis since the modeling projection to 2023 is explicitly designed to project the changes in ozone due to emission reductions from the 2012 base year emission levels.  So, in fact, the EPA method does give weight to emission reductions by including the 2012-2014 as one of the 3 DVs considered in EPA's maintenance methodology despite emission reductions in 2012 and 2014 are also included in the model projections from 2012 to 2023. The TCEQ's modeling includes emission reductions that occur in 2013 and 2014 that are also reflected in the monitoring values that TCEQ is using (2012-2014 monitored DVs). Thus,  in essence, TCEQ is double counting the benefit of 2013 and 2014 emission reductions. Furthermore, since TCEQ agrees that the average of the DVs based on 2010-2014 ozone levels are reliable enough to use in the identification of nonattainment receptors, it is unclear how the 2012-2014 period is deemed more reliable for the identification of maintenance receptors since the modeled emissions are still for 2012. We also note, as discussed throughout this action, the EPA has updated its modeling to use a 2016 base year -- that is, a five year period spanning 2014-2018, and applied its methodology for defining maintenance receptors using that five year base period. Using a more recent base period (EPA's 2016v2) provides the most recent design values, shorter period of projection (2016 to 2023 versus a 2011 or 2012 base year), and a more accurate basis for projections of future air quality. We note that the EPA undertook a large collaborative multi-year effort with states (including Texas) and other stakeholders input and review in developing the 2016v2 emission inventories. By virtue of this update, any monitored DV used by the EPA to identify maintenance receptors in this action accounts for more recent emission reductions and provides a shorter interval between base year monitored DV and the projected future analytic year.
      The EPA has reviewed the set of 21 receptors for which Texas had contributions of 0.7 ppb or more in the EPA's 2016 base year modeling analyses, or TCEQ's modeling (2012 base year), and evaluated the results of using TCEQ's alternate maintenance methodology (see Table 1.1 below).  In Table 1.1 below, the EPA compiled the 2023 Average DVs, which is the DV that is used to identify if a receptor may be a nonattainment receptor for the receptors that EPA and TCEQ identified in Illinois, Wisconsin, Colorado, Arizona, and California. TCEQ's modeling uses a base year of 2012 and the EPA's 2016v2 modeling uses a base year of 2016.  EPA utilized TCEQ's modeling file outputs to obtain the data needed to calculate 2023 Average and Maintenance DVs for the receptors in Illinois and Wisconsin. For these 21 receptors, TCEQ's method resulted in 15 of the 21 2023 maintenance DVs predicted to be lower than the 2023 nonattainment DVs from the nonattainment methodology that uses the 5-year center weighted average. Of these 15 receptors, three receptors have 2023 maintenance DVs that are 3 ppb lower, five receptors have 2023 maintenance DVs that are 2 ppb lower, and seven receptors have 2023 maintenance DVs that are 1 ppb lower. In comparison, using the EPA's maintenance methodology results in all 21 2023 maintenance DVs being equal or up to 4 ppb higher than the 2023 nonattainment DVs. Again, the EPA uses the average of the three DVs that contain the base year modeled for the nonattainment methodology and the maximum of these three DVs for the maintenance methodology. Because TCEQ's maintenance methodology of just using the most recent DV (2012-2014 DV) often results in maintenance DVs lower than the 2023 nonattainment DVs methodology results, the EPA finds that the TCEQ methodology is not adequately identifying conditions when a receptor would have more difficulty maintaining the standard. In fact, the TCEQ's method also identified one receptor in their SIP submission as a nonattainment receptor in 2023 that would not have been identified as a maintenance receptor, which further highlights the concern that TCEQ's method did not adequately identify areas that may struggle to maintain the standard. TCEQ did not address whether the three years that comprise the most recent design value (i.e., 2012, 2013, and 2014) had meteorological conditions highly conducive for formation of high ozone concentrations and thus would be an appropriate time period to assess whether area could have difficulty maintaining the standard. The EPA's analysis confirms that this time period is not highly conducive to ozone formation, at least for many receptors. The consequence of TCEQ's maintenance method is that it often results in lower DVs than the nonattainment methodology as demonstrated by our analysis, which indicates that it is often not considering conditions when an area would have difficulty maintaining the standard. It is also unreasonable to have a methodology that would not identify nonattainment receptors also as maintenance receptors.
   TCEQ also made several additional assertions in support of their conclusion that their method for identifying maintenance receptors was the better reading of the CAA, compared to the EPA's. TCEQ claimed that its approach was more consistent with the CAA's concept of maintenance as areas that were formerly nonattainment and that have since attained and will continue to maintain by accounting for: (1) emissions reductions occurring in the later design values of the base DV period; (2) "commitments regarding contingency measures to address future emission reductions;" and (3) the impact of any maintenance plans that are in place. TCEQ also asserted that the EPA's approach conflates the likelihood of attaining the standard in a future year and the ability of an attainment monitor to maintain that attainment status. Specifically, TCEQ argued that because any remedies devised to address nonattainment monitors would have to apply to maintenance monitors, a practical consequence of the EPA's approach is that it could lead to over-control and that it might require upwind states to consider or implement controls when the downwind state in which the monitor is located does not have any obligations to control local emissions. TCEQ argued that this "conflation" of nonattainment and maintenance results in there being no independent meaning to "maintenance."
   With respect to the first of these assertions from TCEQ, we note that TCEQ's methodology for identifying receptors (like the EPA's) is entirely distinct from ozone designations under the Clean Air Act; neither TCEQ nor the EPA take current or presumed future designations of areas into account, and any implementation requirements like a maintenance plan under CAA section 175A, in identifying receptors. TCEQ's discussion, therefore, of maintenance plan contingency measures or maintenance plans generally is irrelevant and misplaced. None of the areas to which Texas is linked in the EPA 2016v2 modeling has been redesignated to attainment for the 2015 ozone NAAQS, and none of the areas to which Texas is linked in its own modeling has been redesignated to attainment for the 2015 ozone NAAQS. We also fail to see how TCEQ's approach to identifying maintenance receptors differs in any relevant respect from the EPA's approach with regard to the alleged "conflation" of projecting attainment in a future year rather than the ability of an attainment receptor to maintain attainment. Both TCEQ and the EPA identify maintenance receptors based on projections of air quality in a future year to determine whether the receptor will have difficulty attaining or maintaining the standard. TCEQ's arguments about overcontrol based on the application of a uniform remedy to states linked to both nonattainment and maintenance receptors were also not germane; in this case, TCEQ had identified no remedy to apply whatsoever because it had failed to identify that the emissions from Texas cause a problem in the first instance. The D.C. Circuit has already rejected the idea that the application of a uniform control to both nonattainment and maintenance receptors is on its face overcontrol or impermissible under the interstate transport provision. See Wisconsin, 938 F.3d at 327. Based on our evaluation of TCEQ's approach to identify maintenance receptors for 2023, we propose to find the State's approach is inadequate as it does not sufficiently identify maintenance receptors. Further, TCEQ had not explained how its approach meets the statutory requirement to address areas that, even if meeting the NAAQS, may struggle to maintain the standard in years where conditions are conducive to ozone formation. Rather, the TCEQ had created its own approach to identify these areas that they describe as designed to account for the most emission reductions possible -- i.e., the most recent DV of the three under analysis; an approach that likely under-identifies areas that will struggle to maintain the NAAQS and that certainly is not designed to capture potential air quality problems.

Table 1.1 Comparison of Nonattainment methodology (Avg DV) and Maintenance methodology results for receptors identified by EPA and TCEQ in modeling of 2012 and 2016 base case years.
                             Receptor (Monitor ID,
                                County, State)
                          TCEQ 2023 Average DV (ppb)
                        TCEQ 2023 Maintenance DV (ppb)
                     TCEQ DV Delta (Maintenance - Average)
                                     (ppb)
              EPA 2023 Avg DV/Maintenance DV                (ppb)
                       EPA DV Delta (Maintenance - Avg)
                                     (ppb)

                             80350004, Douglas, CO
                                      73
                                      72
                                      -1
                                     71/72
                                       1
                            80590006, Jefferson, CO
                                      72
                                      73
                                       1
                                     72/73
                                       1
                            80590011, Jefferson, CO
                                      71
                                      71
                                       0
                                     73/74
                                       1
                             80690011, Larimer, CO
                                      72
                                      71
                                      -1
                                       ?
                                       ?
                            80050002, Arapahoe, CO
                                      70*
                                      71
                                       1
                                     68/68
                                       0
                             40038001, Cochise, AZ
                                      71
                                     69**
                                      -2
                                     70/72
                                       2
                           60371201, Los Angeles, CA
                                      80
                                      78
                                      -2
                                     82/85
                                       3
                           60371701, Los Angeles, CA
                                      80
                                      82
                                       2
                                     85/88
                                       3
                           60376012, Los Angeles, CA
                                      87
                                      86
                                      -1
                                     91/93
                                       2
                            60658001, Riverside, CA
                                      88
                                      85
                                      -3
                                     89/90
                                       1
                            60658005, Riverside, CA
                                      84
                                      83
                                      -1
                                     87/90
                                       1
                         60710001, San Bernardino, CA
                                      71
                                      72
                                       1
                                     74/75
                                       1
                         60710306, San Bernardino, CA
                                      76
                                      77
                                       1
                                     76/78
                                       2
                         60711004, San Bernardino, CA
                                      91
                                      90
                                      -1
                                    97/100
                                       3
                         60714001, San Bernardino, CA
                                      82
                                      79
                                      -3
                                     82/83
                                       1
                         60714003, San Bernardino, CA
                                      94
                                      91
                                      -3
                                     95/98
                                       3
                          170310001, Cook County, IL
                                      60
                                      58
                                      -2
                                     69/73
                                       4
                          170310032, Cook County, IL
                                      68
                                      66
                                      -2
                                     69/72
                                       3
                          170314201, Cook County, IL
                                      64
                                      62
                                      -2
                                     69/73
                                       4
                          170317002, Cook County, IL
                                      66
                                      65
                                      -1
                                     70/73
                                       3
                         550590019, Kenosha County, WI
                                      67
                                      66
                                      -1
                                     72/73
                                       1
                         550590025, Kenosha County, WI
                                   No data*
                                   No data*
                                   No data*
                                     69/72
                                       3
                         551010020, Racine County, WI
                                   No data*
                                   No data*
                                   No data*
                                     71/73
                                       2
* Monitors were installed in 2013 and 2014 so they did not have a 3 year DV period (2012-2014) or previous years, so no projections future year DVs are possible with TCEQ's modeling. Kenosha 550590025 was installed and began operating May 13, 2013, so the first three year DV available is 2013-2015. Racine 551010020 was installed in April 14, 2014 so the first three year DV available is 2015-2017
** Calculated from the Relative Response Factor in TCEQ spreadsheet of future 2023 DVs with state contributions and the monitor's 2012-2014 DV (0.983 X 71 ppb, truncation applied).
      EPA's modeling guidance for 8-hour ozone NAAQS attainment demonstrations (1999- 2007)  initially considered using a three year period that included the modeled year (inventory year modeled) as the middle year of the three year period but the EPA ultimately went with 5 consecutive years with the modeled year being the middle year (covering the three DVs that include the base year) to address potential variability in meteorological/ozone conduciveness and emissions. EPA analyzed the variability of using this 5 years center-weighted average method compared with using a 3 year average DV and found that the 5 year center-weighted approach was more stable and had a lower standard deviation. The goal of using 5 years of data as a basis for an attainment demonstration is that such demonstrations be based on an assessment of a range of meteorological conditions rather than a limited set of conditions in one 3-year DV period that may be conducive to higher or lower ozone levels. EPA's use of the highest maximum 3-year DV that includes the base modeled year is intended to provide an indication of conditions that might make it difficult for an area to maintain the ozone NAAQS. As such it accounts for potential meteorological and emissions variability that TCEQ's method does not. Also, TCEQ did not support their claim that there were large reductions in emissions that made the higher DV periods of 2010-2012 or 2011-2013 unreasonable to be used for determining a maintenance receptor.  Furthermore, TCEQ did not provide sufficient data and analysis of the meteorology for the 2010-2014 period to support their claim that 2012-2014 period was a worst-case combination of meteorology compared to the 2010-2012 and 2011-2013 periods. If the future DV projected from this highest value is below the standard, one can be reasonably certain the receptor will not have difficulty maintaining the standard and, as such, upwind states will not interfere with maintenance in downwind states. Because the TCEQ method only looks at one DV and does not account for the variability in DVs due to meteorological conditions, it is less likely to identify maintenance receptors than the EPA method.
      To further examine TCEQ's assertion that the last DV period including the modeled base year should be used in the maintenance methodology, EPA reviewed a number of ozone design value trends, including several that were included in TCEQ's SIP and in a previous attainment demonstration SIP for Dallas-Fort Worth Nonattainment Area (DFW). EPA also looked at monitoring trends for the most recent 10 years of certified data (2009-2011 DV up to 2018-2020 DV) using ozone monitoring data from EPA's Air Quality System (AQS).  In general, as shown in the figures below, our evaluation of monitor data for Dallas-Fort Worth and Houston-Galveston-Brazoria as well as at at receptors that have been identified in Colorado, Arizona, Southern California, and Midwest Region (Illinois, Michigan, and Wisconsin), the long-term average annual decreases in ozone due to emissions reductions are usually on the order of 0-1 ppb/year at monitors in large metropolitan nonattainment areas. However, variations in monitored 8-hour DVs are not always negative from year to year and sometimes increase by 2 through-4 ppb from one year to the next due to meteorological variations.

      For the DFW area, looking at Figures 1.1 and 1.2 (From DFW AD SIP) the long-term DFW DV trend is less than 1 ppb/year in Figure 1.1 and 1.1 ppb/yr in Figure 1.2. For DFW, we started with the 2010-2012 DV as there were some reductions from a previous DFW ozone SIP that had compliance dates in early 2010. In Figure 1.3, the DVs at most of the higher DFW area monitors (2018-2020 DVs greater than 70 ppb) are decreasing at approximately 1 ppb/year for the last 9 years of certified DVs. In evaluating Houston-Galveston-Brazoria trends, TCEQ implemented multiple emission reductions for both NOx and a subset of VOCs that resulted in large decreases of ozone from 2000 to 2007, so EPA is evaluating trends for DVs including 2007 and later as there has not been as significant emission reductions implemented by TCEQ since that time. For the Houston-Galveston-Brazoria Nonattainment area (HGB), looking at Figures 1.4, 1.5, and 1.6 (from HGB AD SIP) the long-term HGB DV trend and DV trends at the monitors with higher ozone DVs from 2009 DV to current DV is less than 1.0 ppb/year. EPA evaluated the trends using the most recent DVs available in Figure 1.6. In Figure 1.7, the DVs at most of the higher HGB area monitors (2018-2020 DVs greater than 70 ppb) are decreasing at 1 ppb/year or less for the last 10 years of certified DVs (2011-2020 DVs).
      EPA also evaluated DV trends using the most recent DVs available for the last 10 years for receptors identified by EPA's 2011 base year modeling, the EPA's 2016 base year (2016v2) modeling analyses, or TCEQ's modeling (2012 base year), in Colorado, Arizona, Southern California and the Midwest Region including receptors in Illinois, Wisconsin and Michigan. For the Colorado receptors see TCEQ's SIP Figure 3-40 (included as Figure 1.8 below) that show ozone DVs  from 2007-2016 decreased less than 1 ppb/yr on average. EPA also evaluated the trends at the Colorado receptors using the most recent DVs available EPA in Figure 1.9, which shows that the DVs at Colorado receptors have either increased or slightly decreased at less than 0.5 ppb/year for the last 10 years of certified DVs (2011-2020 DVs). For the Arizona receptor see TCEQ's SIP Figure 3-55 (included as Figure 1.10 below) that show ozone DVs from 2007-2016 decreased less than 1 ppb/yr on average but had several ppb increases or decreases from one year to the next. EPA also evaluated the trends at the Arizona receptor using the most recent DVs available in Figure 1.11, which shows that the DVs at Arizona receptor have decreased at less than 1.0 ppb/year on average for the last 10 years of certified DVs (2011-2020 DVs). California receptors (see TCEQ's SIP Figure 3-40 included as Figure 1.12 below) show ozone DVs from 2007-2016 decreased less than 1 ppb/yr on average. EPA also evaluated the trends at the California receptors using the most recent DVs available EPA in Figure 1.13 which show that the DVs at California receptors have either increased or are slightly decreased at less than 0.5 ppb/year on average for the last 10 years of certified DVs (2011-2020 DVs). EPA evaluated the trends at the Chicago Nonattainment Area receptors (IL and WI) using the most recent DVs available EPA in Figure 1.14, which show that the DVs at these receptors have either increased or are slightly decreased at less than 0.7 ppb/year on average for the last 10 years of certified DVs (2011-2020 DVs). EPA also evaluated the trends at the other Wisconsin and Michigan receptors using the most recent DVs available EPA in Figure 1.15, which show that the DVs at Sheboygan Wisconsin and Allegan Michigan receptor have decreased approximately 1 ppb/year on average for the last 10 years of certified DVs (2011-2020 DVs).
      The long-term average annual decreases in ozone due to emissions reductions are usually on the order of 0-1 ppb/year at monitors in large metropolitan nonattainment areas such as Dallas-Fort Worth and Houston-Galveston-Brazoria, and at receptors that have been identified in Colorado, Arizona, Southern California, and Midwest Region (Illinois, Michigan, and Wisconsin) but variations in monitored 8-hour DVs are not always negative and sometimes increase by 2-4 ppb from one year to the next due to meteorological variations.  We also included TCEQ's monitored trends figure for the Arizona receptor that also shows increases of 2 ppb and 3 ppb annually, and from 2009 to 2012 the DV increased from 66 ppb to 73 ppb for a total of 7 ppb increase in 3 years. These larger annual fluctuations upward in DVs demonstrate the amount of changes possible in DV due the meteorological difference between different 3 year design value periods. TCEQ's approach fails to take the much larger influence of meteorological variability into account in their maintenance receptor test.
Figure 1.1 - TCEQ's Figure 1-1: One-Hour and Eight-Hour Ozone Design Values and DFW Population, from DFW 2008 8-hour Attainment Demonstration SIP (July 6, 2016).

Figure 1.2 - TCEQ's Figure 5-1:One-Hour and Eight-Hour Ozone Design Values in the DFW Area from 1997 through 2014 from DFW 2008 8-hour Attainment Demonstration SIP (July 6, 2016).

        Figure 1.3 DFW Monitor 8-hour Ozone DV (2012-2020 DVs) trends.

Figure 1.4 -  TCEQ's Figure 1-1: Ozone Design Values and Population in HGB Area, from TCEQ's HGB Serious AD SIP Revision for 2008 Ozone (2020).

Figure 1.5 -  TCEQ's Figure 5-4: Eight-Hour and One-Hour Ozone Design Values in the HGB Area From TCEQ's HGB Serious AD SIP Revision for 2008 Ozone (2020)

Figure 1.6 - From TCEQ's HGB Serious AD SIP Revision for 2008 Ozone (2020) (Figure 5-5: Eight-Hour Ozone Design Values by Monitor in the HGB Area.)

Figure 1.7 HGB Monitor 8-hour Ozone DV (2011-2020 DVs) trends.

Figure 1.8 TCEQ's SIP Figure 3-40.

                Figure 1.9 EPA Denver NAA receptors DV trends.

                     Figure 1.10 TCEQ's SIP Figure 3-55.

                  Figure 1.11 EPA Arizona receptor DV trend.

                     Figure 1.12 TCEQ's SIP Figure 3-56.

           Figure 1.13 EPA Southern California receptors DV trends.

               Figure 1.14 EPA Chicago NAA receptors DV trends.

       Figure 1.15 EPA Other Wisconsin and Michigan receptors DV trends.

2.	Underestimation Concerns
	As discussed in this section below and in section 3, below, EPA has evaluated the future year model projections for the DFW and HGB areas within Texas and also at downwind areas (Colorado, Arizona, Southern California, Illinois, Wisconsin, and Michigan) that have been identified in EPA's 2011 base case modeling, EPA's 2016v2 modeling, and TCEQ's modeling and compared these 2023 projections with recent DVs (2018-2020) and preliminary DVs (2019-2021). This evaluation (TCEQ's 2023 modeled DVs to recent monitoring) and expected decreases in DVs indicates that the TCEQ's modeling underestimates future ozone levels. When the TCEQ 2023 projected concentrations are compared to 2020 and preliminary 2021 monitor values, it is clear that the TCEQ modeling is projecting an unusual decline in ozone levels without there being an unusual level of emission reductions to support the decline. The EPA compared recent monitoring values and reasonably anticipated decreases (0-1 ppb/year  -  see discussion on ozone trends) in DVs by 2023 both within Texas and in other parts of the country. These underestimations likely result in TCEQ's modeling not adequately identifying nonattainment and/or maintenance receptors in 2023. These underestimations also result in smaller projected contributions from Texas emissions to downwind states.
   In this section, the EPA has evaluated TCEQ's base case (2012) modeling for the DFW and HGB areas. TCEQ's modeling does have some underestimation of higher ozone levels in these two areas in the base case year that could lead to underestimation of Texas's emissions impacts on downwind areas in the future year and also in ozone levels downwind of Texas in the base year and future year. EPA also evaluated TCEQ's model performance for downwind areas and our review indicated some underestimation bias in the base case and general model performance concerns but nothing that was a clear cause of the much lower 2023 DVs that TCEQ's modeling is projecting.
a.	Underprediction in the base case 2012 period of Texas ozone at DFW/HGB monitors
      TCEQ used the same 2012 year for the base case year for DFW and HGB ozone attainment demonstration SIPs. TCEQ's 2012 Transport modeling base case has some of the same concerns noted in review of the attainment demonstration SIPs (i.e., that high ozone levels are often underestimated in DFW and HGB areas). TCEQ provides some Model Performance Evaluation data on a website that EPA used to generate the figures below demonstrating underestimation issues at key monitor/receptors in the DFW and HGB area. The EPA has provided time series that indicates the observed Maximum Daily Average 8-hour ozone (MDA8) value in red and modeled value in blue for two monitors in DFW area and one in the HGB area that were identified in EPA's March 2018 memorandum and three additional monitors (Eagle Mountain Lake, Grapevine, and Frisco) in the DFW area, which are some of the monitors that often set the DFW area DV (Figures 2.1 a-f).  These figures also include quantile-quantile (Q-Q) scatter plots, comparing observed to modeled MDA8 and showing the underestimation in the modeled values for the high ozone days.

Figure 2.1(a) Brazoria County  -  Manvel Croix Time Series MDA8 Observed (red) and Modeled (blue) and Scatter Q-Q plots.

Figure 2.1(b) Denton County  -  Denton Airport South Time Series MDA8 Observed (red) and Modeled (blue) and Scatter Q-Q plots.

Figure 2.1(c) Tarrant County  -  FAA Site Time Series MDA8 Observed (red) and Modeled (blue) and Scatter Q-Q plots.

Figure 2.1(d) Tarrant County  -  Eagle Mountain Lake Time Series MDA8 Observed (red) and Modeled (blue) and Scatter Q-Q plots.

Figure 2.1(e) Tarrant County  -  Grapevine Time Series MDA8 Observed (red) and Modeled (blue) and Scatter Q-Q plots.

Figure 2.1(f) Collin County  -  Frisco Time Series MDA8 Observed (red) and Modeled (blue) and Scatter Q-Q plots.

EPA has also analyzed the data from the MDA8 time series (2012 Observed and 2012 Modeled) for these monitors and evaluated modeled daily MDA8 values for all days above thresholds of 70 ppb, 75 ppb, 80 ppb, and 85 ppb. Table 2.1 includes the mean, minimum, and maximum bias for days over the observed MDA8 threshold (70, 75, 80, and 85 ppb) and the number of days of positive and negative bias over the observed MDA8 threshold. EPA also calculated for each of these observed thresholds, the number of days that modeled negative bias is 0 to less than -5 bias, -5 to -10 ppb, and more than -10 ppb for the days over the observed MDA8 threshold.

Table 2.1 DFW and HGB monitors that have been identified by EPA as nonattainment/maintenance receptors and additional high DV monitors.
Manvel Croix Brazoria County Texas AQS ID480391004

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >70 ppb
                                    -14.02
                                    -33.83
                                     -2.59
                                       0
                                      13
                                       1
                                       4
                                       8
OBS >75 ppb
                                    -16.20
                                    -33.83
                                     -6.74
                                       0
                                      10
                                       0
                                       2
                                       8
OBS >80 ppb
                                    -16.68
                                    -33.83
                                     -6.74
                                       0
                                       9
                                       0
                                       2
                                       7
OBS >85 ppb
                                    -21.81
                                    -33.83
                                    -12.14
                                       0
                                       5
                                       0
                                       0
                                       5
Denton Airport South Denton County Texas AQS ID 481210034

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >70 ppb
                                     -5.24
                                    -15.98
                                     5.19
                                       5
                                      19
                                       9
                                       4
                                       6
OBS >75 ppb
                                     -9.28
                                    -15.98
                                     -3.04
                                       0
                                      10
                                       3
                                       2
                                       5
OBS >80 ppb
                                    -10.28
                                    -15.98
                                     -5.29
                                       0
                                       5
                                       0
                                       2
                                       3
OBS >85 ppb
                                     -9.70
                                    -12.48
                                     -5.29
                                       0
                                       3
                                       0
                                       1
                                       2

FAA Tarrant County Texas AQS ID 484392003

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >70 ppb
                                     -5.49
                                    -14.97
                                     0.74
                                       3
                                      10
                                       3
                                       6
                                       1
OBS >75 ppb
                                     -5.63
                                    -14.97
                                     0.53
                                       2
                                       7
                                       2
                                       4
                                       1
OBS >80 ppb
                                     -3.75
                                     -7.66
                                     0.28
                                       1
                                       2
                                       1
                                       1
                                       0
OBS >85 ppb
                                     -3.75
                                     -7.66
                                     0.28
                                       1
                                       2
                                       1
                                       1
                                       0

Table 2.1 (continued) DFW and HGB monitors that have been identified by EPA as nonattainment/maintenance receptors and additional high DV monitors.
Eagle Mountain Lake Tarrant County Texas AQS ID 484390075

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >70 ppb
                                     -4.36
                                    -14.78
                                     5.39
                                       5
                                       7
                                       1
                                       4
                                       2
OBS >75 ppb
                                     -8.41
                                    -14.78
                                     -4.39
                                       0
                                       6
                                       1
                                       4
                                       1
OBS >80 ppb
                                     -8.31
                                    -14.78
                                     -4.39
                                       0
                                       4
                                       1
                                       2
                                       1
OBS >85 ppb
                                     -8.31
                                    -14.78
                                     -4.39
                                       0
                                       4
                                       1
                                       2
                                       1

Grapevine Tarrant County Texas AQS ID 484393009

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >70 ppb
                                     -7.06
                                    -18.30
                                     3.11
                                       7
                                      21
                                       4
                                       8
                                       9
OBS >75 ppb
                                     -9.47
                                    -18.30
                                     0.82
                                       1
                                      10
                                       0
                                       6
                                       4
OBS >80 ppb
                                    -12.23
                                    -18.30
                                     -6.37
                                       0
                                       6
                                       0
                                       3
                                       3
OBS >85 ppb
                                     -9.77
                                    -17.29
                                     -6.37
                                       0
                                       4
                                       0
                                       3
                                       1

Frisco Collin County Texas AQS ID 480850005

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >70 ppb
                                     -7.57
                                    -18.27
                                     4.11
                                       4
                                      22
                                       5
                                       8
                                       9
OBS >75 ppb
                                    -10.28
                                    -18.27
                                     1.17
                                       1
                                      13
                                       2
                                       3
                                       8
OBS >80 ppb
                                    -11.69
                                    -18.27
                                     -4.81
                                       0
                                       9
                                       1
                                       3
                                       5
OBS >85 ppb
                                    -15.57
                                    -18.27
                                    -12.52
                                       0
                                       3
                                       0
                                       0
                                       3

      As can be seen in Figure 2.1(a) and Table 2.1, TCEQ's modeling is not replicating the higher ozone monitored values on many days at the HGB Manvel Croix monitor in the time series and scatter plot. Table 2-1 shows that the model is underestimating ozone by 2.59-33 ppb on all days with monitored values above 70 ppb and a mean bias of -14.02 ppb for all days using the 70 ppb threshold. On the higher ozone days with a minimum observed MDA8 threshold of 75 ppb or 80 ppb, the bias is worse with the mean bias of greater than -16 ppb on all days and a mean bias of -21 ppb on days using the 85 ppb threshold. We have looked at some other monitors in the HGB area that are typically the highest ozone monitors in HGB and TCEQ's model performance varies but there is a general underprediction bias at several of the monitors.
      As can be seen in Figure 2.1(b) and Table 2.1, TCEQ's modeling is not replicating the higher ozone monitored values on many days at the DFW area Denton Airport South monitor in the time series and in the scatter plot. Table 2-1 shows that the model is underestimating ozone by 0-15.98 ppb on most days with monitored values above 70 ppb and a mean  bias of -5.24 ppb for all days using the 70 ppb threshold. On the higher ozone days with a minimum observed MDA8 threshold of 75 ppb or 80 ppb the bias is worse with the mean bias of -9 ppb or worse on all days.
      As can be seen in Figure 2.1(c) and Table 2.1, TCEQ's modeling is not replicating the higher ozone monitored values on many days at the DFW area FAA Site monitor in the time series and in the scatter plot. Table 2-1 shows that the model is underestimating ozone by 0-14.97 ppb on most days with monitored values above 70 ppb and a mean bias of -5.49 ppb for all days using the 70 ppb threshold.
      As can be seen in Figure 2.1(d) and Table 2.1, TCEQ's modeling is not replicating the higher ozone monitored values on many days at the DFW area Eagle Mountain Lake monitor in the time series and in the scatter plot. Table 2-1 shows that the model is underestimating ozone by 0-14.78 ppb on most days with monitored values above 70 ppb and a mean bias of -4.36 ppb for all days using the 70 ppb threshold. On the higher ozone days with a minimum observed MDA8 threshold of 75 ppb, 80 ppb, or 85 ppb the bias is worse with an underestimation of 4.39 to 14.78 ppb and mean bias of -8.3 ppb or worse on all days (-8.31 ppb and -8.41 ppb depending on which minimum cutoff).
      As can be seen in Figure 2.1(e) and Table 2.1, TCEQ's modeling is not replicating the higher ozone monitored values on many days at the DFW area Grapevine monitor in the time series and in the scatter plot. Table 2-1 shows that the model underestimates ozone by 0-18.3 ppb on most days with monitored values above 70 ppb and a mean bias of -7.06 ppb for all days using the 70 ppb threshold. On the higher ozone days with a minimum observed MDA8 threshold of 75 ppb, 80 ppb, or 85 ppb the underestimation bias is worse. Specifically, the mean bias is -9.4 ppb or worse on all days (-9.47 ppb using 75 ppb threshold, -12.23 ppb using 80 ppb threshold, and -9.77 ppb using the 85 ppb threshold).
      As can be seen in Figure 2.1(f) and Table 2.1, TCEQ's modeling is not replicating the higher ozone monitored values on many days at the DFW area Frisco monitor in the time series and in the scatter plot. Table 2-1 shows that the model is underestimating ozone by 0-18.27 ppb on most days with monitored values > 70 ppb and a mean bias of -7.57 ppb for all days using the 70 ppb threshold. On the higher ozone days with a minimum observed MDA8 of 75 ppb, 80 ppb, or 85 ppb the underestimation bias is worse. For those thresholds,  the mean bias is -10.28 ppb, -11.69 ppb, and -15.57 ppb respectively.
      Overall, TCEQ's 2012 base case modeling is not replicating the higher monitored ozone levels at a number of monitors in the DFW and HGB area. This indicates that TCEQ's modeling is not replicating the amount of ozone generated by the emissions from the largest source categories in these two large urban areas. This raises a concern that TCEQ's 2012 modeling underestimates ozone levels generated by Texas's emissions which results in the underestimation of both downwind ozone levels in other states and the amount of Texas's contribution to downwind states and their potential receptors. This underestimation of ozone levels from Texas's emissions in the base year can also lead to underestimations of Texas projected ozone generation in the future year modeling, underestimation of downwind ozone levels outside of Texas, and also underestimation of Texas's impact on downwind receptors. This underestimation that exists in TCEQ's modeling also raises a concern in how it impacts model projections from that the Oklahoma Department of Environmental Quality (ODEQ) relied upon in its 2015 8-hour Ozone Transport SIP to analyze Oklahoma's potential contribution to downwind ozone at receptors in DFW and HGB areas of Texas.
      To further evaluate ozone production in the model for the Texas areas, the EPA also evaluated monitors typically upwind of DFW (Corsicana monitor in Navarro County, Texas {AQS ID 483491051}and Italy monitor in Ellis County, Texas {AQS ID 481391044}) and HGB (a monitor in Jefferson County, Texas {AQS ID 482450022}and a Livingston monitor in Polk County Texas {AQS ID 483739991}). EPA evaluated the time series and scatter plots for these monitors available on TCEQ's website. EPA also generated the same information included in Table 2-1 for these monitors in Table 2-2 below. Since these are typically background monitors, the EPA used a lower threshold of  >= 60 ppb MDA8 to analyze model performance. Overall the mean bias at these four monitors that represent upwind/background for the DFW and HGB areas has a slight negative mean bias for days with monitored MDA8s >=60 ppb, but the underestimation is smaller with mean bias ranging from -1.0 ppb to -3.2 ppb. This further indicates that underestimation at the higher ozone values in DFW and HGB discussed above in the 2012 base case modeling is more of a result of the model not producing enough ozone in these nonattainment areas from local emission sources on the high ozone monitored days in the HGB and DFW. As discussed below, we also note Figures 2.2 and 2.3 below (reproduced from TCEQ's SIP Figure 3-26 and 3-27) indicate that most monitors in Texas have an underprediction bias and a high Root Mean Square Error (RMSE), further raising concern with the accuracy of Texas's modeling.

               Table 2.2. DFW and HGB Upwind/background Monitors
Corsicana TX AQS ID 483491051

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >60 ppb
                                     -3.2
                                     -14.0
                                      5.2
                                       4
                                      19
                                      12
                                       5
                                       2

Italy TX AQS ID 481410029

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >60 ppb
                                     -1.0
                                     -10.5
                                     12.6
                                      10
                                      16
                                      12
                                       3
                                       1

Jefferson County TX AQS ID 482450022

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >60 ppb
                                     -2.2
                                     -28.6
                                     10.8
                                       8
                                      10
                                       6
                                       1
                                       3
Livingston TX AQS ID 483739991

                  Modeled Value  vs Observed Value MDA8 (ppb)
                      Number of Days above OBS Threshold
                Number of Days Modeled MDA8 is negative biased

                                                            OBS Threshold Value
                                     MEAN
                                      MIN
                                      MAX
                                 Positive Bias
                                 Negative Bias
                               0 to > -5 ppb
                                 -5 to -10 ppb
                              More than (-10) ppb
OBS >60 ppb
                                     -1.6
                                     -7.8
                                      6.2
                                       3
                                       4
                                       1
                                       3
                                       0

b.	Underprediction in the TCEQ's base case 2012 period at downwind areas in Midwest and West.
      TCEQ included some Model Performance Evaluation (MPE) analysis in the main SIP documents that indicates model performance varied across the U.S. but had underestimation at key areas in the Midwest and west including the Chicago area, southeast Wisconsin, Denver, Arizona, and Southern California areas.  These areas include sites that were identified in EPA's modeling as nonattainment/maintenance receptors. We have included 2 figures from TCEQ's SIP that show Mean Bias and Root Mean Square Error (RMSE) for each monitor in the domain. Included as Figure 2.2 is TCEQ's Figure 3-26 in their SIP submission, which provides Mean Bias for the May through September 2012 Episode at all the EPA Air Quality System (AQS) monitoring sites for days with observed Maximum Daily Average Eight-Hour (MDA8) ozone concentration >= 60 ppb. Figure 2.2 shows that the mean biases for days with MDA8 ozone concentrations >= 60 ppb differ by region, but at a number of monitors in the Chicago, southeastern Wisconsin, Denver, Arizona, and southern California areas TCEQ's modeling has a negative mean biases of more than 5 ppb.  TCEQ's modeling having a mean bias underestimation of more than 5 ppb in areas that EPA's modeling identified as having nonattainment/maintenance receptors is concerning because the underestimation in TCEQ's modeling in the base and future years may result in their modeling not identifying receptors as nonattainment/maintenance receptors.
      Figure 2.3 (TCEQ's Figure 3-27 in their SIP submission) provides RMSE for May through September 2012 Episode at AQS Monitoring Sites for each of the days with observed MDA8 > 60 ppb. The RMSE at most monitors in the modeling domain were in the range of 6 to 12 ppb. However, many monitors in the Midwest including Chicago area and southeastern Wisconsin and monitors in Arizona and California have worse model performance with higher deviations with RMSE in the 8 to 14 ppb range.

    Figure 2.2 TCEQ's SIP Figure 3-26 showing Mean Bias at each monitor.

Figure 2.3 TCEQ' SIP Figure 3-27 showing Root Mean Square Error (RMSE) at each monitor.

TCEQ's model performance evaluation for their 2012 base case modeling is included in their SIP submission as "Appendix C  -  Photochemical Model Performance Evaluation for the Transport State Implementation Plan Revision for the 2015 Eight-Hour Ozone National Ambient Air Quality Standard".  EPA evaluated information in Appendix C and available on TCEQ's websites in our review of TCEQ's modeling.
      TCEQ's modeling underestimates observed values at key monitor/receptors in Cook County, IL, Wisconsin, and Michigan. This underprediction bias is especially troublesome at the monitors/receptors located in Kenosha County WI, Sheboygan County WI, and Allegan County, MI as many of these monitors typically have the highest DVs in the multi-state area around Chicago. The underprediction at these monitors/receptors in TCEQ's modeling likely results in some of these monitors not being identified as potential nonattainment/maintenance receptors. Both EPA's initial modeling and  most recent modeling has consistently identified nonattainment/maintenance receptors in the Chicago area and downwind areas in Wisconsin and Michigan with Texas emissions having impacts >=0.7 ppb.  TCEQ's modeling, however, is underestimating the ozone at these nonattainment/maintenance receptors in their 2012 base case modeling it does have similar contributions from Texas's emissions (>= 0.7 ppb).  If TCEQ's modeling had less underprediction issues and actually identified nonattainment and/or maintenance receptors in the Chicago Nonattainment Area or downwind areas in Wisconsin and Michigan, TCEQ would have identified linkages to Texas as EPA's modeling has consistently identified.

      TCEQ's modeling also underestimates observed values at key monitor/receptors in Colorado. Even with these underprediction concerns, TCEQ's modeling analysis still identified 4 nonattainment/maintenance receptors in Colorado including the Jefferson County (AQS ID 80590011), which the EPA also identified as a receptor with Texas linkage in modeling results included in our March 2018 memorandum. If TCEQ's modeling had less underprediction bias, there may have been more monitors/receptors identified with Texas emissions linkage in Colorado and other downwind areas. The ability of TCEQ's modeling to replicate the highest days both in Texas and at downwind areas are important to having an adequate modeling analysis to both assess potential nonattainment/maintenance receptors and linkages to upwind states. In addition to the underprediction at these monitors/receptors that may result in some monitors not being identified as potential nonattainment/maintenance receptors, underprediction issues at monitors in Texas in TCEQ's modeling results in an underestimation of Texas's impacts at Colorado monitors. Both EPA's modeling included in the March 2018 memorandum and TCEQ's modeling identified nonattainment/maintenance receptors in Jefferson County, CO.  We also note that TCEQ's analysis also identified one additional receptor in Jefferson County, CO, one monitor in Larimer County, CO, and one receptor in Douglas County, CO with linkage impacts greater than 0.7 ppb from Texas emissions despite the multiple underestimation issues with TCEQ's modeling.
      TCEQ's modeling also underestimates observed values at key monitor/receptors in Arizona and California that TCEQ identified as nonattainment and/or maintenance receptors with linkages to Texas.. Despite this underprediction of high ozone values within Texas and at these downwind receptors, TCEQ did identify Texas emissions as having impacts of more than 0.7 ppb, and thus, linked to some nonattainment/maintenance receptors in Arizona and California.
c.	Underestimation of Future year projections at DFW/HGB receptors

      TCEQ's modeling includes projections of future year 2023 Design Values for 2015 8-hour ozone levels for monitors within the large metropolitan areas of DFW and HGB, both of which continue to monitor ozone nonattainment and have been designated as 2015 8-hour ozone nonattainment areas. To support attainment demonstration planning, TCEQ has in the past performed photochemical modeling for both of these HGB and DFW nonattainment areas. TCEQ also included in these previous attainment demonstration SIPs that long term ozone DV trend analysis for both the HGB and DFW areas indicate that the average annual decrease in ozone DVs was approximately 1 to 1.2 ppb/year. EPA's analysis of the most recent 9 years of DVs in DFW and 10 years of DVs in HGB indicate a similar trend of 1 ppb/year or less. Using these DV trends from TCEQ's previous 2008 Ozone Attainment Demonstration SIP submittals indicate that emissions decreases between 2020 and 2023 would be expected to result in a drop in ozone concentrations of approximately 3-4 ppb if no variability in meteorology is considered and no large emission reduction programs are implemented in Texas or on a national level. In evaluating TCEQ's model projections for 2023 in their 2015 Ozone Transport SIP, this information provided a helpful comparison that indicates a good approximation of the difference in 8-hour ozone DVs at monitors/receptors in DFW and HGB from 2020 to 2023 should be approximately 3-4 ppb at most for the higher DV monitors.  That is to say, without significant unusual reductions, not just those expected to from fleet turnover, 3-4 ppb during this 3 year period is the most that should be expected.  To assess if TCEQ's ozone transport modeling is potentially underestimating future year 2023 modeled DVs in DFW and HGB areas, EPA  compared 2018-2020 monitored DVs at many typically higher ozone monitors in the DFW and HGB area with TCEQ's projected 2023 DVs in Table 2.3. While not as exact as developing new modeling of emission changes from 2020 to 2023 to project 2023 DVs, using the general 3-4 ppb provides a ballpark estimate to evaluate whether TCEQ's modeling might be underpredicting 2023 future DVs. As can be seen in Table 2.3, there are many monitors in DFW and HGB that would need a drop in ozone DVs over the next 3 years of  >5 ppb (values of -5 ppb or more in the far right column) to reach TCEQ's 2023 modeled projected levels. The average decrease needed to meet TCEQ's modeled DV projections is 4.97 ppb, which is more than 3-4 ppb. EPA also evaluated just the 2023 DVs for monitors with 2018-2020 DVs of 74 ppb or greater.  For these monitors, a 2023 monitored DV of 70 ppb or less is needed to monitor attainment, implying a drop of at least 3-4 ppb. For this subset of monitors with 2018-2020 DVs of 74 ppb or more, the average amount of decrease in DVs needed to match TCEQ's 2023 projection is 7.56 ppb. In other words, for those monitors with the highest monitored DVs for 2018-2020, TCEQ's modeling projected average reductions in DVs over the three year period of 7.56 ppb, much larger than the 3-4 ppb that would be typical for these areas. Both in TCEQ's past SIP modeling and EPA's modeling experience, drops in 8-hour ozone DVs on average of 7.56 ppb in three years do not typically occur unless there is a large change in emissions or large change in meteorological conduciveness for ozone generation. TCEQ did not identify any large emission reductions to be implemented in the 2021-2023 timeframe. This information supports our finding that TCEQ's modeling is underestimating future ozone levels in the two nonattainment areas in Texas that make up a large proportion of the total ozone and emissions of ozone pre-cursors that transport to downwind areas. This underestimation of future year ozone levels from Texas emissions can cause both an underestimation of ozone in downwind areas (discussed further below)  and an underestimation of Texas's impacts on downwind State's ozone nonattainment and maintenance receptors.

Table 2.3 TCEQ Projected Future 2023 DVs Compared to 2018-2020 Monitored DVs.
State
County
AQS ID
                    TCEQ 2023 Future Design Value-DV (ppb)
                          EPA Monitored Data from AQS
                              2018-2020 DV (ppb)
         Delta in 3 years 2018-2020 Monitor DV - TCEQ Future DV (ppb)
Texas
Brazoria
                                                                      480391004
                                                                             78
                                                                             73
                                                                              5
Texas
Brazoria
                                                                      480391016
                                                                             60
                                                                             65
                                                                             -5
Texas
Collin
                                                                      480850005
                                                                             66
                                                                             75
                                                                             -9
Texas
Dallas
                                                                      481130069
                                                                             64
                                                                             69
                                                                             -5
Texas
Dallas
                                                                      481130075
                                                                             63
                                                                             74
                                                                            -11
Texas
Dallas
                                                                      481130087
                                                                             61
                                                                             69
                                                                             -8
Texas
Denton
                                                                      481210034
                                                                             68
                                                                             72
                                                                             -4
Texas
Denton
                                                                      481211032
                                                                             66
                                                                             72
                                                                             -6
Texas
Ellis
                                                                      481390016
                                                                             60
                                                                             64
                                                                             -4
Texas
Ellis
                                                                      481391044
                                                                             57
                                                                             63
                                                                             -6
Texas
Galveston
                                                                      481671034
                                                                             67
                                                                             74
                                                                             -7
Texas
Harris
                                                                      482010024
                                                                             68
                                                                             79
                                                                            -11
Texas
Harris
                                                                      482010026
                                                                             66
                                                                             69
                                                                             -3
Texas
Harris
                                                                      482010029
                                                                             67
                                                                             73
                                                                             -6
Texas
Harris
                                                                      482010046
                                                                             66
                                                                             64
                                                                              2
Texas
Harris
                                                                      482010047
                                                                             69
                                                                             72
                                                                             -3
Texas
Harris
                                                                      482010051
                                                                             68
                                                                             70
                                                                             -2
Texas
Harris
                                                                      482010055
                                                                             68
                                                                             76
                                                                             -8
Texas
Harris
                                                                      482010062
                                                                             72
                                                                             67
                                                                              5
Texas
Harris
                                                                      482010066
                                                                             68
                                                                             69
                                                                             -1
Texas
Harris
                                                                      482010416
                                                                             72
                                                                             73
                                                                             -1
Texas
Harris
                                                                      482011015
                                                                             64
                                                                             67
                                                                             -3
Texas
Harris
                                                                      482011034
                                                                             71
                                                                             73
                                                                             -2
Texas
Harris
                                                                      482011035
                                                                             68
                                                                             70
                                                                             -2
Texas
Harris
                                                                      482011039
                                                                             74
                                                                             78
                                                                             -4
Texas
Harris
                                                                      482011050
                                                                             68
                                                                             70
                                                                             -2
Texas
Johnson
                                                                      482510003
                                                                             62
                                                                             73
                                                                            -11
Texas
Kaufman
                                                                      482570005
                                                                             58
                                                                             64
                                                                             -6
Texas
Montgomery
                                                                      483390078
                                                                             64
                                                                             74
                                                                            -10
Texas
Tarrant
                                                                      484390075
                                                                             65
                                                                             75
                                                                            -10
Texas
Tarrant
                                                                      484391002
                                                                             63
                                                                             72
                                                                             -9
Texas
Tarrant
                                                                      484392003
                                                                             66
                                                                             73
                                                                             -7
Texas
Tarrant
                                                                      484393009
                                                                             68
                                                                             76
                                                                             -8
Texas
Tarrant
                                                                      484393011
                                                                             62
                                                                             69
                                                                             -7

                                    Avg ALL
                                                                          -4.97

                                                 Avg if Current DV >= 74 ppb
                                                                          -7.56
      We also note that ODEQ relied on TCEQ's future year 2023 modeled DVs at three nonattainment/maintenance receptors in their SIP submission.  Oklahoma was linked to these three receptors in previous modeling results included in the EPA's March 2018 memorandum.  These monitors included 2 monitors in DFW area (Tarrant County AQS ID 484392003 and Denton County AQS ID 481210034) and one monitor in the HGB area (Brazoria County AQS ID 480391004). Included as Table 2.4 is information from the Oklahoma 2015 Ozone Transport SIP that utilized information from the EPA's March 2018 memorandum on Transport Modeling Results.
Table 2.4 Oklahoma's Transport SIP Information Referring to Linkages to Texas Receptors.
                                   Receptor
                                   (Site ID,
                                County, State)
                             2023 Average DV (ppb)
                             2023 Maximum DV (ppb)
                             Oklahoma Contribution
                                     (ppb)
                    Oklahoma's Step 1 and 2 Determination
                             481210034, Denton, TX
                                     69.7
                                     72.0
                                     1.23
             Maintenance receptor identified for further analysis.
                            484392003, Tarrant, TX
                                     72.5
                                     74.8
                                     1.71
            Nonattainment receptor identified for further analysis.
                            480391004, Brazoria, TX
                                     74.0
                                     74.9
                                     0.90
Nonattainment receptor with contribution less than 1 ppb; no further analysis.

      From Table 2.3 we note that the two receptors in DFW area are currently monitoring (2018-2020 DV) nonattainment with a 72 ppb at the Denton County maintenance receptor and 73 ppb at the Tarrant County nonattainment receptor. We note that the Nonattainment receptor in HGB (Brazoria County AQS ID 480391004) is currently monitoring a 73 ppb (2018-2020 DV) but was projected to be 78 ppb in TCEQ's 2023 modeled projections. We note that in the HGB area the maximum monitored values can vacillate between monitors depending upon the variations in local meteorology from one year to the next. Based on most recent DVs at these three monitors it is possible that they may reach attainment by 2023, however even if they attain, it is likely that they would be close to the NAAQS, and therefore, still be considered maintenance receptors. A drop of 3 ppb (1 ppb/year) would result in DVs of 70 ppb at two monitors and 69 ppb at the Denton monitor. As discussed above, TCEQ's 2023 model projections for these three monitors has underestimation concerns for the two DFW monitors and raises concerns with the adequacy of relying on TCEQ's modeling to conclude that the two DFW monitors are not at least maintenance receptors in 2023 for which Oklahoma is linked.

d.	Underestimation in TCEQ's 2023 Future year projections at downwind areas
      As discussed above, EPA has concerns that TCEQ's 2012 base case modeling is underestimating ozone levels in downwind areas in Illinois, Wisconsin, Michigan, Colorado, and California.  These underpredictions in the base case also result in TCEQ's modeling underestimating 2023 Ozone DVs at downwind monitors/receptors that TCEQ may be linked to. As discussed above, absent variability in meteorology and any large local/regional/national implementation of emission reductions, the drop in monitored Ozone DVs from 2020 to 2023 would be expected to be 3-4 ppb at large nonattainment metropolitans like DFW and HGB.  While not as exact as developing new modeling of emission changes from 2020 to 2023 to project 2023 DVs, using the general 3-4 ppb provides a ballpark estimate to evaluate if TCEQ's modeling might be underpredicting 2023 future DVs at similar size nonattainment areas in the  Chicago, IL/Wisconsin/Michigan area; the Denver, CO area; Cochise County, AZ receptor; and Southern California areas. We note that in our evaluation of most recent DV trends in these areas that the annual average change from 2010 to 2020 DVs ranged from increasing to decreasing at most approximately 1 ppb/year, so use of 3-4 ppb is still an optimistic assessment of how much the DVs might drop between 2020 and 2023 but the trends data for these areas indicates the change will likely be less than a 3-4 ppb decrease for many monitors. To assess if TCEQ's modeling is potentially underestimating future year 2023 modeled DVs in these downwind areas, the EPA has compared 2018-2020 monitored DVs at many typically higher ozone monitors in these areas with TCEQ's projected 2023 DVs in Table 2.5, Table 2.6 and Table 2.7.

 Table 2.5 TCEQ Projected Future 2023 DVs Compared to 2018-2020 Monitored DVs
                              (IL, WI, & MI).
Chicago and Downwind areas in Wisconsin and Michigan

State
County
AQS ID
                    TCEQ 2023 Future Design Value-DV (ppb)
                           EPA Monitored 2018-2020 DV
                                     (ppb)
             Delta in 3 years 2018-2020 Monitor DV - TCEQ 2023 DV
                                     (ppb)
Illinois
Cook
                                                                      170310001
                                      60
                                      75
                                      -15
Illinois
Cook
                                                                      170310032
                                      68
                                      74
                                      -6
Illinois
Cook
                                                                      170310076
                                      61
                                      69
                                      -8
Illinois
Cook
                                                                      170311003
                                      60
                                      73
                                      -13
Illinois
Cook
                                                                      170311601
                                      61
                                      71
                                      -10
Illinois
Cook
                                                                      170314002
                                      61
                                      71
                                      -10
Illinois
Cook
                                                                      170314007
                                      57
                                      71
                                      -14
Illinois
Cook
                                                                      170314201
                                      64
                                      77
                                      -13
Illinois
Cook
                                                                      170317002
                                      66
                                      75
                                      -9
Michigan
Allegan
                                                                      260050003
                                      71
                                      73
                                      -2
Wisconsin
Door
                                                                      550290004
                                      64
                                      72
                                      -8
Wisconsin
Kenosha
                                                                      550590019
                                      67
                                      74
                                      -7
Wisconsin
Manitowoc
                                                                      550710007
                                      65
                                      70
                                      -5
Wisconsin
Milwaukee
                                                                      550790010
                                      58
                                      62
                                      -4
Wisconsin
Milwaukee
                                                                      550790085
                                      66
                                      70
                                      -4
Wisconsin
Sheboygan
                                                                      551170006
                                      70
                                      75
                                      -5

                                    Avg ALL
                                     -8.31

Avg if Current DV >= 74 ppb
                                     -9.17

Table 2.6 TCEQ Projected Future 2023 DVs Compared to 2018-2020 Monitored DVs (CO).
Denver and downwind areas in Colorado

State
County
AQS ID
                    TCEQ 2023 Future Design Value-DV (ppb)
                           EPA Monitored 2018-2020 DV
                                     (ppb)
             Delta in 3 years 2018-2020 Monitor DV - TCEQ 2023 DV
                                     (ppb)
Colorado
Adams
                                                                       80013001
                                      65
                                      69
                                      -4
Colorado
Arapahoe
                                                                       80050002
                                      70
                                      77
                                      -7
Colorado
Arapahoe
                                                                       80050006
                                      66
                                      71
                                      -5
Colorado
Boulder
                                                                       80130011
                                      67
                                      74
                                      -7
Colorado
Denver
                                                                       80310002
                                      57
                                      70
                                      -13
Colorado
Douglas
                                                                       80350004
                                      73
                                      81
                                      -8
Colorado
Jefferson
                                                                       80590005
                                      68
                                      71
                                      -3
Colorado
Jefferson
                                                                       80590006
                                      72
                                      79
                                      -7
Colorado
Jefferson
                                                                       80590011
                                      71
                                      80
                                      -9
Colorado
Larimer
                                                                       80690007
                                      69
                                      70
                                      -1
Colorado
Larimer
                                                                       80690011
                                      72
                                      75
                                      -3
Colorado
Larimer
                                                                       80691004
                                      65
                                      67
                                      -2
Colorado
Weld
                                                                       81230009
                                      70
                                      70
                                       0

                                    Avg ALL
                                     -5.31

Avg if Current DV >= 74 ppb
                                     -6.83

 Table 2.7 TCEQ Projected Future 2023 DVs Compared to 2018-2020 Monitored DVs
                                 (AZ & CA)
                     Arizona and Southern California areas
State
County
AQS ID
                    TCEQ 2023 Future Design Value-DV (ppb)
                           EPA Monitored 2018-2020 DV
              Delta in 3 years 2018-2020 Monitor  -  TCEQ 2023 DV
                                     (ppb)
Arizona
Yuma
                                                                       40278011
                                      72
                                      68
                                       4
California
Los Angeles
                                                                       60370002
                                      74
                                      97
                                      -23
California
Los Angeles
                                                                       60370016
                                      87
                                      107
                                      -20
California
Los Angeles
                                                                       60370113
                                      59
                                      70
                                      -11
California
Los Angeles
                                                                       60371103
                                      63
                                      76
                                      -13
California
Los Angeles
                                                                       60371201
                                      80
                                      92
                                      -12
California
Los Angeles
                                                                       60371302
                                      52
                                      64
                                      -12
California
Los Angeles
                                                                       60371602
                                      70
                                      78
                                      -8
California
Los Angeles
                                                                       60371701
                                      80
                                      88
                                      -8
California
Los Angeles
                                                                       60372005
                                      72
                                      93
                                      -21
California
Los Angeles
                                                                       60375005
                                      57
                                      62
                                      -5
California
Los Angeles
                                                                       60376012
                                      87
                                      101
                                      -14
California
Riverside
                                                                       60650012
                                      85
                                      99
                                      -14
California
Riverside
                                                                       60650016
                                      63
                                      78
                                      -15
California
Riverside
                                                                       60651016
                                      87
                                      99
                                      -12
California
Riverside
                                                                       60652002
                                      75
                                      84
                                      -9
California
Riverside
                                                                       60655001
                                      78
                                      88
                                      -10
California
Riverside
                                                                       60656001
                                      78
                                      94
                                      -16
California
Riverside
                                                                       60658001
                                      88
                                      96
                                      -8
California
Riverside
                                                                       60658005
                                      84
                                      98
                                      -14
California
Riverside
                                                                       60659001
                                      73
                                      87
                                      -14
California
San Bernardino
                                                                       60710001
                                      71
                                      81
                                      -10
California
San Bernardino
                                                                       60710005
                                      96
                                      90
                                       6
California
San Bernardino
                                                                       60710012
                                      83
                                      90
                                      -7
California
San Bernardino
                                                                       60710306
                                      76
                                      83
                                      -7
California
San Bernardino
                                                                       60711004
                                      91
                                      106
                                      -15
California
San Bernardino
                                                                       60712002
                                      94
                                      102
                                      -8
California
San Bernardino
                                                                       60714001
                                      82
                                      87
                                      -5
California
San Bernardino
                                                                       60714003
                                      94
                                      114
                                      -20
California
San Bernardino
                                                                       60719002
                                      79
                                      86
                                      -7
California
San Bernardino
                                                                       60719004
                                      88
                                      110
                                      -22

                                 Avg ALL CA
                                    -11.80

Avg Current DV >= 74 ppb
                                    -11.64

      As can be seen in Table 2.5, there are many monitors in Illinois, Wisconsin, and Michigan that would need a drop in ozone DVs over the next 3 years of  >= 5 ppb (values of -5 ppb or more in the far right column) to reach TCEQ's 2023 modeled projected levels. The average decrease to meet TCEQ's modeled DV projections is 8.31 ppb, which is more than the 0-4 ppb that would be typical for these areas. EPA also separately evaluated  the TCEQ modeled 2023 DVs for monitors with 2018-2020 DVs of 74 ppb or greater.  For these monitors, a 2023 monitored DV of  <=70 ppb  would be needed to reach attainment, implying a drop of at least 3 to 4 ppb.  However even if these monitors show attainment  in 2023, it is likely that their DVs would be close enough to the NAAQS to still be considered maintenance receptors. For this subset of monitors with 2018-2020 DVs of 74 ppb or more, the average amount of decrease in DVs needed to match TCEQ's 2023 projection is 9.17 ppb. In EPA's modeling experience, drops in 8-hour ozone DVs on average of 9.17 ppb in three years rarely occur unless there is a very large change in emissions or large change in meteorological conduciveness for ozone generation. TCEQ did not identify any large emission reductions to be implemented in the 2021-2023 timeframe. Based on this information TCEQ's modeling is underestimating future ozone levels at monitors/receptors in Illinois, Wisconsin, and Michigan and these underestimations likely prevented TCEQ from identifying nonattainment/maintenance receptors. Furthermore, as discussed above, TCEQ's modeled underestimation of future year ozone levels in Texas and production of ozone from Texas emissions can cause an underestimation of Texas's impacts on downwind State's ozone nonattainment and maintenance receptors had they been identified by Texas as receptors. EPA's modeling results included in the March 2018 memorandum and EPA's 2016v2 modeling both identified nonattainment/maintenance receptors in the Chicago area of Illinois and downwind areas in Wisconsin and Michigan that also had linkages to emissions from Texas (Texas' contribution was greater than 0.7 ppb).
      As can be seen in Table 2.6, there are many monitors in the Denver area and downwind of Denver that would need a drop in ozone DVs the next 3 years of > 5 ppb (values of -5 ppb or more in the far right column) to reach TCEQ's 2023 modeled projected levels. The average decrease to meet TCEQ's modeled DV projections is 5.31 ppb which is more than the 0-3 ppb that would be typical for this area.  EPA also evaluated just the 2023 DVs for monitors with 2018-2020 DVs of > 74 ppb.  For these monitors, a 2023 monitored DV of <= 70 ppb would be needed to monitor attainment which would imply a drop of at least 3-4 ppb.  However, even if the monitors monitor attainment in 2023, it is likely that they would be close to the NAAQS, and therefore, still be considered maintenance receptors. For this subset of monitors with 2018-2020 DVs of >74 ppb, the average amount of decrease in DVs needed to match TCEQ's 2023 projection is 6.83 ppb. In EPA's modeling experience drops in 8-hour ozone DVs on average of 6.83 ppb in three years do not typically occur unless there is a large change in emissions or large change in meteorological conduciveness for ozone generation. Based on this information, it does appear that TCEQ's modeling is underestimating future ozone levels at monitors/receptors in Denver and surrounding downwind areas of Colorado and these underestimations may have prevented TCEQ from identifying nonattainment/maintenance receptors. Furthermore, TCEQ's modeled underestimation of future year ozone levels in Texas and production of ozone from Texas emissions can also result in an underestimation of Texas's impacts on downwind ozone nonattainment and maintenance receptors in Colorado.  Even with these underestimation concerns, TCEQ identified several nonattainment/maintenance receptors in Colorado with linkages to Texas based on contributions of emissions from Texas equal to or greater than 0.7 ppb. EPA's modeling results included in the March 2018 memorandum also identified nonattainment/maintenance receptors in the greater Denver area of Colorado (Jefferson County) that also had linkages to emissions from Texas. (Texas' contribution was greater than 0.7 ppb).
      As can be seen in Table 2.7, there are many monitors in the Southern California area that would need a drop in ozone DVs the next 3 years of more than 5 ppb (values of -5 ppb or more in the far right column) to reach TCEQ's 2023 modeled projected levels. The average decrease to meet TCEQ's modeled DV projections is 11.80 ppb, which is more than the 0-3 ppb that would be typical for these areas of Southern California. EPA also evaluated a subset of these monitors, just the 2023 DVs for monitors with 2018-2020 DVs of 74 ppb or greater.  For these monitors, a 2023 monitored DV of <= 70 ppb would be needed to monitor attainment which would imply a drop of at least 3-4 ppb. However even if they monitor attainment in 2023, it is likely that they would be close to the NAAQS, and therefore, still be considered maintenance receptors. For this subset of monitors with 2018-2020 DVs of 74 ppb or more, the average amount of decrease in DVs needed to match TCEQ's 2023 projection is 11.64 ppb. In EPA's modeling experience drops in 8-hour ozone DVs on average of 11.6 ppb in three years rarely occur unless there is a large change in emissions or large change in meteorological conduciveness for ozone generation. Based on this information, TCEQ's modeling is underestimating future ozone levels at monitors/receptors in Southern California area and these underestimations may have prevented TCEQ from identifying nonattainment/maintenance receptors. Further, as discussed above, TCEQ's modeled underestimation of future year ozone levels in Texas and production of ozone from Texas emissions can also result in an underestimation of Texas's impacts on downwind ozone nonattainment and maintenance receptors in California.  Even with these underestimation concerns, TCEQ identified several nonattainment/maintenance receptors in California with linkages to Texas with contributions <= 0.7 ppb.
e.	Summary
      TCEQ's 2012 base case modeling underestimates ozone levels in Texas at high ozone monitors in the DFW and HGB areas and also at some of the high ozone monitors in areas downwind of Texas including the Chicago, IL area and downwind receptors in Wisconsin and Michigan, the greater Denver area of Colorado, and in Southern California. TCEQ's 2023 future year projections seem to be underestimating ozone levels in Texas at high ozone monitors in DFW and HGB and also underestimating downwind ozone levels at monitors in areas downwind of Texas including the Chicago area and downwind receptors in Wisconsin and Michigan, greater Denver area of Colorado, and in Southern California, as shown above by comparing 2018-2020 monitored DVs to the projected 2023 DVs and examining the amount of ozone reductions that would be necessary to meet those projections. These underestimations likely prevented TCEQ from identifying nonattainment/maintenance receptors in the Chicago area and downwind receptors in Wisconsin and Michigan.  Furthermore, as discussed above, TCEQ's modeled underestimation of future year ozone levels in Texas and production of ozone from Texas emissions can cause an underestimation of Texas's impacts on downwind State's ozone nonattainment and maintenance receptors had they been identified by TCEQ as receptors.  Despite these underprediction/underestimation biases, TCEQ's modeling did identify a number of receptors in Colorado and California that Texas emissions were linked (contribution equal or greater than 0.70 ppb).

3.	Comparison of TCEQ and EPA 2023 future modeling and identified maintenance and nonattainment receptors
   For the EPA's 2016 base year modeling, the EPA undertook a large collaborative multi-year effort with states (including Texas) and other stakeholder input in developing the 2016 emission inventories including 2016v2, so that the EPA's modeling would be based on the best data available. Using a 2016 base year also provides a more recent platform that shortens the number of years to project emission changes, reducing uncertainties in the 2023 projection compared to TCEQ's projection from a 2012 base to 2023 or the EPA's earlier 2011 base year modeling. Use of a more recent 2016 base year also allows for the use of monitored DVs from a more recent period. The combination of these and other issues result in less model uncertainty compared to TCEQ's 2012 base year modeling and has provided a better estimate of 2023 ozone levels; and therefore, we believe a more reliable tool for predicting which areas of the country will be nonattainment or have difficulty maintaining the standard as well as assessing contributions from upwind states.
   The EPA's modeling using both 2011 and 2016 base year periods identified that Texas was linked to nonattainment and/or maintenance receptors in 2023 in the Midwest Region (Illinois, Wisconsin, and Michigan), while TCEQ's modeling using a 2012 base year indicated only linkages to western receptors. As discussed above, the TCEQ's modeling is underestimating projected ozone levels in the Midwest Region for 2023. If TCEQ's 2023 modeled DVs were closer to recent observed monitoring data and anticipated 2023 monitored DVs, TCEQ would likely have also identified nonattainment and/or maintenance receptors in the Midwest Region.

      EPA's base case model performance is acceptable and the base case model performance analysis is included in the Air Quality Modeling TSD. As discussed in Air Quality Modeling TSD, the EPA's base case modeling model performance demonstrates the scientific credibility of EPA's 2016v2 modeling platform and provides confidence in the ability of the modeling platform to provide a reasonable projection of expected future year ozone concentrations and contributions.  In Tables 3.1 and 3.2, the EPA is evaluating TCEQ's identification of potential receptors using their 2023 model projections, including TCEQ's methodology for identifying maintenance receptors.  As discussed above, the EPA has significant concerns with the methodology TCEQ used to identify these maintenance receptors. In Tables 3.1 and 3.2, the EPA presents TCEQ's 2023 modeling projections and EPA's 2023 modeling projections (nonattainment and maintenance methodologies) and also preliminary 2019-2021 monitored DVs (subject to QA/QC by states and EPA).
In Table 3.1 EPA shows the TCEQ's list of potential receptors identified by TCEQ that are nonattainment and/or maintenance receptors with Texas linkages (Texas contributions of >= 0.7 ppb) in their SIP including the 2023 average DV (nonattainment receptor if >70 ppb), 2023 Maintenance DV using TCEQ's maintenance methodology (uses only the 2012-2014 base DV).  For these receptors, EPA also includes the EPA's 2023 modeling projections for both average DV and maintenance DV (using EPA's methodology) and the most recent monitor data available.

Table 3.1 Projected 2023 Nonattainment and Maintenance Receptors Identified by TCEQ Modeling Using a 2012 Base Year
                                  Receptor,
                                 (Monitor ID,
                                County, State)
                          TCEQ 2023 Average DV (ppb)
                        TCEQ 2023 Maintenance DV (ppb)*
                           Texas Contribution (ppb)
                        EPA 2023 Avg DV/Maintenance DV
                 2018-2020 DV/Preliminary 2019-2021 DV** (ppb)

                                   80350004,
                                  Douglas, CO
                                      73
                                      72
                                     1.42
                                     71/72
                                     81/83
                                  80590006,
                                 Jefferson, CO
                                      72
                                      73
                                     1.26
                                     72/73
                                     79/81
                                  80590011,
                                 Jefferson, CO
                                      71
                                      71
                                     1.26
                                     73/74
                                     80/83
                                  80690011,
                                  Larimer, CO
                                      72
                                      71
                                     1.22
                                     64/65
                                     75/77
                                  80050002,
                                 Arapahoe, CO
                                     70***
                                      71
                                     1.15
                                     68/68
                                     77/80
                                  40038001,
                                  Cochise, AZ
                                      71
                                    69****
                                     1.06
                                     70/72
                                     66/66
                                  60371201,
                                Los Angeles, CA
                                      80
                                      78
                                     0.76
                                     82/85
                                     92/87
                                  60371701,
                                Los Angeles, CA
                                      80
                                      82
                                     0.72
                                     85/88
                                     88/90
                                  60376012,
                                Los Angeles, CA
                                      87
                                      86
                                      0.9
                                     91/93
                                    101/101
                                  60658001,
                                 Riverside, CA
                                      88
                                      85
                                     0.73
                                     89/90
                                     96/95
                                  60658005,
                                 Riverside, CA
                                      84
                                      83
                                     0.71
                                     87/90
                                     98/97
                                  60710001,
                              San Bernardino, CA
                                      71
                                      72
                                     0.84
                                     74/75
                                     81/77
                                  60710306,
                              San Bernardino, CA
                                      76
                                      77
                                     0.81
                                     76/78
                                     83/83
                                   60711004,
                               San Bernardino, CA
                                      91
                                      90
                                     0.88
                                    97/100
                                    106/103
                                  60714001,
                              San Bernardino, CA
                                      82
                                      79
                                     0.86
                                     82/83
                                     87/87
                                  60714003,
                              San Bernardino, CA
                                      94
                                      91
                                     0.74
                                     95/98
                                    114/114
* Uses TCEQ's Maintenance receptor methodology that EPA has concerns (See section on maintenance receptor methodology discussion).
** 2021 monitoring data is preliminary and still has to undergo Quality Assurance/Quality Control analysis and be certified by the State of Texas, submitted to the EPA, and reviewed and concurred on by EPA.
*** From TCEQ spreadsheet of future 2023 DVs with state contributions.
**** TCEQ did not provide calculations for this receptor so EPA calculated from the Relative Response Factor in TCEQ spreadsheet of future 2023 DVs with state contributions and the monitor's 2012-2014 DV (0.983 X 71 ppb, truncation applied).