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Summary Emissions from activities in the Gulf of Mexico Region (GOMR)âincluding emissions associated with oil and gas exploration, development, and production on the Gulf watersâcan result in increased levels of air pollutants that contribute to a range of air quality impacts in the region. Carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter (PM), and sulfur dioxide, also known as âcriteria air pollutants,â are considered harmful to public health and the environment. In recognition of the potential adverse impacts, the Environmental Protection Agency (EPA) sets standards, called the National Ambient Air Quality Standards (NAAQS), for those pollutants. The Bureau of Ocean Energy Management (BOEM) manages the U.S. outer continental shelf (OCS) oil and gas resources and is required to help manage air quality in the GOMR to comply with the NAAQS. BOEM meets these requirements in part by conducting air pollution dispersion and photochemical modeling, and analyzing the results to estimate the individual and cumulative impacts of offshore oil and gas exploration, development, and production activities. Such analyses are used to set limits on emissions. If these analyses indicate that an offshore oil and gas facility adversely impacts the air quality of any state, then that OCS facility is subject to regulations and emissions controls to ensure compliance with the NAAQS. BOEMâs air quality analyses for the GOMR had not been updated in decades when in 2014, BOEM asked a team of contractors to conduct the Air Quality Modeling in the Gulf of Mexico Region Study (the âStudyâ). The Study will likely lead to regulatory changes, potentially including new emission exemption thresholds (EETs). EETs are a screening tool that take into account distance from shore and projected annual emission estimates in exploration, development, and production plans by potential lessees and operators to determine if more refined air quality modeling and emission controls are needed. The current EETs were developed in the 1980s and do not reflect more recently regulated pollutant (i.e., PM2.5 and PM10) and updated (i.e., 8-hour-average ozone, 1-hour-average NO2 and SO2) air quality standards, nor state-of-the-science dispersion and photochemical modeling approaches. BOEM designated the Study as âhighly influentialâ because of the potential impact on EETs and the associated methods, the development of a long-term meteorological dataset for future analysis, and the assessment of potential air quality impacts. As a result, BOEM asked the National Academies of Sciences, Engineering, and Medicine (the National Academies) to provide an independent technical review of the Study. The National Academies Board on Atmospheric Sciences and Climate appointed an ad hoc committee to conduct this review (see Chapter 1 for the full statement of task)1. COMMITTEEâS ASSESSMENT OF THE STUDY The committee is impressed by the amount of work and detail that was involved in carrying out the Study. The Study utilized a significant amount of scientifically appropriate tools for 1 Minor changes were made to the report and the Summary in January 2020. Details of the specific changes are explained with footnotes in the body of the report. 1
2 REVIEW OF THE BOEM âAIR QUALITY MODELING IN THE GOMRâ STUDY meteorological (Weather Research and Forecasting model [WRF]), emissions (using the National Emissions Inventory, plus additional study of specific sources), and air quality modeling (Comprehensive Air Quality Model with Extensions, American Meteorological Society/Environmental Protection Agency Regulatory Model [AERMOD], Offshore and Coastal Dispersion [OCD], and CALPUFF). The Study authors conducted a particularly detailed accounting of emissions in the GOMR, including the OCS. They evaluated the base case performance, tested a number of emissions scenarios using photochemical modeling, and compared multiple dispersion models. They also developed and tested a number of EET methods, including a new approach using Classification And Regression Tree (CART) analysis. The Study report is relatively detailed, documenting what was done and where the data originated. However, the Study report organization makes it difficult to review in detail and assess how well the Study meets its objectives. For example, the Study does not synthesize information from the report and associated appendices nor does it clearly identify its objectives. The Study objectives should appear in the Summary and in Chapter 1, and information should be added to guide readers to where material related to each of the Study objectives can be found. In conducting the review, the committee found that the Study addresses each of the objectives, although to varying degrees of accuracy and completeness. Furthermore, a number of overarching issues were found related to: (a) the assessment of the cumulative impact of uncertainties in the meteorological fields, OCS emissions, and photochemical modeling on air quality and deposition in the region; and (b) the development and analysis of EET approaches. Cumulative Air Quality Impacts Several atmospheric characteristics, including atmospheric stability, wind speed, direction, turbulence, and associated air-sea interactionsâthe latter of which is complicated by local topographyâinfluence the transport and dispersion of pollutants in the GOMR. Diurnal temperature differences between the land and the Gulf set in motion a circulation system that affects the direction of transport and can advect pollution from offshore sources toward coastal areas. Furthermore, extreme atmospheric conditions, such as cold air outbreaks, modify internal boundary layers and circulation and transport processes. Accurate representation of the diurnal cycle in land-sea breeze circulation is key to model development in the GOMR. The Studyâs WRF data evaluation does not include key meteorological metrics (e.g., planetary boundary layer height, time series of the wind conditions and temperature, diurnal patterns, errors in vertical profile, and averaging over hourly, daily, monthly time periods) that are important for modeling dispersion of emissions to onshore locations. Without a comprehensive analysis of such meteorological metrics, the committee is unable to determine whether the Studyâs meteorological modeling has any biases or errors that when propagated into the dispersion and chemical transport models that use the WRF data as input, significantly influence the analyses. In addition, the Studyâs evaluation of photochemical modeling does not focus on the meteorological conditions that are favorable for transfer of emissions from the OCS to areas where onshore monitors are located. This results in an evaluation that is inherently less sensitive to an accurate accounting of offshore emissions and the chemical and transport processes by which these emissions would likely have their greatest effect on air pollutant levels on land.
SUMMARY 3 Ozone levels, in particular, can become elevated over bodies of water, leading to high levels when such air masses move onshore. Finding: The Study does not fully evaluate the meteorological and air quality models for the various meteorological conditions that would favor outer continental shelf sources having the greatest air quality impact on vulnerable areas onshore in the Gulf of Mexico region. Meteorological conditions that would be expected to have the greatest impact on onshore air quality include: ï· predominantly onshore flows, ï· inter-season variations, ï· cold air outbreaks, and ï· periods of high temperatures, stagnation, and recirculation of pollutants due to time-varying sea breeze circulation and stationary fronts. Recommendation: The Study should conduct an enhanced analysis of the meteorological trends during the 5 years of the simulation and identify the key meteorological features and patterns during this time. This analysis should be used to identify conditions when the land-based measurements are most sensitive to offshore emissions, and how modeling biases and errors during those periods influence air quality modeling. The Study should also evaluate the results from the meteorological (and photochemical) modeling for conditions with onshore flows and other conditions when the land-based measurements are most sensitive to offshore emissions. In the Study, the WRF modeling has a high bias in the wind speeds and temperatures. This high wind speed bias could lead to over-dilution of the plume. This bias, coupled with the choice of 2012 as the base case year, impacts the cumulative modeling and the assessment of the impact of the current and future OCS activities on ozone, PM2, and visibility. The Studyâs analyses of temperature and precipitation in Texas ended in 2012 and the authors chose 2012 as the base case year because it is more meteorologically average, historically. However, choosing a year (such as 2011) that had conditions most conducive to ozone formation (i.e., a hot and dry year) may provide a different picture of how the OCS emissions contribute to nonattainment and visibility impairment (see Figure S.1). Furthermore, while 2011 may have been a meteorologically aberrant year, historically, the National Climate Assessment projects increasing temperatures for all Gulf States by the middle of the 21st century. The choice of 2012 as the base year also propagates into the modeling of the future year (2036), as the meteorological fields are kept the same in the Study. Therefore, the emissions of biogenic volatile organic compounds3 (VOCs) from vegetation and wildfire emissions are the same between the base year (2012) and the future year (2036). This may be less likely given the findings from the National Climate Assessment. Accounting for predicted changes in climatological conditions would have led to different biogenic and wildfire emissions and potentially higher impacts from the new oxides of nitrogen (NOX) emissions offshore. 2 Unless otherwise noted, this report refers to both primary and secondary PM. 3 VOCs are ozone precursors.
4 REVIEW OF THE BOEM âAIR QUALITY MODELING IN THE GOMRâ STUDY Finding: The choice of 2012 as the base case year might lead to biases in the impact assessment of current and future outer continental shelf emissions on levels of ozone, PM2.5, and PM10. Recommendation: The Study should also conduct the photochemical modeling for 2011, given that warm years are more conducive to ozone, PM2.5, and PM10 formation and are projected to become more prevalent in the future. Having results from two different years with different meteorological conditions would allow the Study authors to more comprehensively assess impacts of emissions now and in the future under climate change. The BOEM modeling of future impacts of OCS emissions includes the potential impacts of emissions from potential lease sales up to 2022. However, additional lease sales may occur after 2022, and those are currently not captured in the Study. The committee recognizes that such sales are uncertain, as are the associated emissions. However, just considering the specific scenario for lease sales appears to bias the results. Because the committeeâs review took place in 2019, and thus already halfway through the 2017-2022 period, actual lease sales could be Figure S.1 Average GOMR summer temperatures and ozone concentrations from 1990 to 2018. Meteorological conditions are an important factor in ozone formation, which is higher on warm, sunny days when the air is stagnant. Note that 2011 has, on average, higher ozone than 2010 or 2012 and later years, suggesting that 2011âs meteorological conditions contributed to more elevated ozone levels. The figure includes data from Florida, Louisiana, and Texas, which are the GOMR states that had ozone data available. The averaging time for ozone is the annual fourth-highest daily maximum 8-hour concentration. Generated with data from EPA4 and NOAA National Centers for Environmental Information5. See Appendix D for data. 4 See https://www.epa.gov/outdoor-air-quality-data. 5 See https://www.ncdc.noaa.gov/cag/.
SUMMARY 5 compared against assumed lease sales in the model. The committee notes that actual lease sales have been in deep water, while the scenario presented in the Study appears to have many lease sales in shallower waters (see Figure 3-9 of the Study). Finding: The Study uses an overly specific and perhaps biased scenario of lease sales; in particular, the scenario does not include emission-generating activities associated with potential future sales after 2022. Recommendation: A future update to the Study should evaluate the impacts from other scenarios for continued exploration, development, and production of oil and gas reserves on the outer continental shelf beyond the 2017-2022 lease-sale scenarios. Development and Testing of Emission Exemption Thresholds BOEM should be commended for its recognition that the EET methods need to be updated. The committeeâs understanding of the EET formulas as screening tools is that they should be conservative and not subject to any false negatives,6 while minimizing false positives7 to the extent possible. From its public discussions with both BOEM and industry representatives, the committeeâs understanding is that the typical lessee/operator response to an exceeded EET limit is to reduce planned oil and gas throughput (and thus projected emissions) rather than conduct refined modeling or implement emission controls. This means that less conservative threshold limits will likely lead to higher allowable emissions and associated air quality impacts. As noted by EPA in their Appendix W document, it is important that the worst-case atmospheric conditions are identified and assessed,8 which is particularly relevant for EET development. Conducting dispersion modeling using 5 years of meteorological data (2010-2014) appropriately captures a large range of conditions both conducive and less conducive to higher concentrations of primary pollutants9. However, average rather than worst-case meteorology (i.e., less dispersion of atmospheric pollutants) was selected for ozone, PM2.5, and PM10, with the result that potential contributions to air quality that would exceed the NAAQS can be underestimated (as discussed in the previous section). In addition, the approach for determining the new EETs results in false negatives for some pollutants. The existing EET methods do not generate false negatives for the annual average NAAQS for NO2, PM10, or SO2 when compared to the new modeling conducted under the Study, and only 1.2% false negatives for PM2.5. This implies that the existing EETs are generally protective of the long-term NAAQS onshore, although with many false positives for NO2 (41%) and SO2 (27%) (Table ES-2 in the Study). On the other hand, the existing EETs for the short-term NAAQS for CO, NO2, PM2.5, PM10, and SO2 exhibit false negatives ranging from 2% to 36% of the cases studied (Table ES-1 in the Study), depending on the pollutant, which suggests they 6 False negatives are situations in which the EET limit is not exceeded although refined air quality modeling shows a significant air quality impact as defined by the EPAâs Significant Impact Levels. 7 False positives are situations in which the EET is exceeded but refined modeling shows no significant air quality impact. 8 https://www3.epa.gov/ttn/scram/appendix_w-2016.htm. 9 Text modified January 2020 to more clearly acknowledge that the Study used 5 years of meteorological data for the primary pollutants.
6 REVIEW OF THE BOEM âAIR QUALITY MODELING IN THE GOMRâ STUDY could miss a number of cases where additional modeling is warranted. The committee agrees with this assessment and supports the choice of the Study authors to develop new exemption level thresholds for all NAAQS and their precursors. Using both air quality modeling and statistical methods, the Study developed several new versions of EETs, but none of these are shown to be fully protective of all NAAQS because of the continued existence of false negatives for several pollutants. It is more appropriate to eliminate false negatives, based on EPA guidance and the need to preserve air quality and public health. Furthermore, the EETs are mostly biased high because of choices made with regard to air quality inputs and the methodology used by BOEM to apply the EETs to specific exploration, development, and production plans by potential lessees and operators: 1. The meteorological modeling has a high bias in the wind speeds, which could lead to over- dilution of the plume and modeled pollution levels lower than what would be observed. 2. The year chosen as the base case (2012) is a more typical year meteorologically, but a year more conducive to elevated levels of ozone, PM2.5, and PM10 should have also been selected. The Study does not clearly demonstrate how 2012 satisfies current modeling guidance and the use of the results for developing EETs. 3. The CART analysis, which is used to develop a number of EETs, results in unrealistic, abrupt patterns. These include results where: o no emissions are allowed within a certain distance from shore (e.g., for 1-hour- average NO2), and o lower emissions are prohibited but higher emissions are allowed at the same distance from shore (e.g., for 24-hour-average PM10). Caution should be exercised with the use of any statistical or other technique not based on insights into the underlying physical and chemical processes, and where extrapolating beyond the conditions used to develop the techniques. It is not clear to the committee if these unrealistic, abrupt patterns are a result of a limited number of emission scenarios, large differences found by the Study between the models used within 50 km offshore (AERMOD) and beyond 50 km (CALPUFF), and/or problems with the application of the CART method itself. 4. The CART-based PM2.5 and PM10 EETs are based on direct emissions of PM, and do not include secondary formation of particle nitrates and sulfates. 5. BOEM allows potential lessees/operators to use annualized emissions, rather than daily maximum emissions, for the short-term NAAQS EETs. This contrasts with the appropriate use of maximum hourly emissions required by BOEM for subsequent air quality modeling of proposed projects that exceed one or more EETs. 6. BOEM only counts vessel emissions within 25 km of the platform and assigns those emissions to the distance from shore to the platform, despite the fact that the vessels dominate emissions (Figure 4-1 of the Study) and are primarily emitting at all distances between the platform and their ports.
SUMMARY 7 Some of the alternative approaches (e.g., using the maximum hourly emission rate for all calculations related to short-term NAAQS, estimating impacts based on comparable modeled sources) suggested by the Study should be considered by BOEM. The modeling system selected (WRF-MMIF-AERMOD) to derive new EETs for sources less than 50 km offshore appears to perform no better or worse than BOEMâs default regulatory model (OCD), and all models failed to reproduce near-shore (0.8 to 10 km) tracer studies of atmospheric dispersion to within a factor of two. Moreover, a modeling discontinuity, potentially several orders of magnitude in either direction, arises at offshore distances of 50 km, inside of which AERMOD is favored and beyond which CALPUFF is used. The Study did not conduct a model comparison beyond 40 km. Finding: The emission exemption thresholds (EETs) developed in the Study may not be protective of National Ambient Air Quality Standards (NAAQS) because they are not conservative and may miss instances where more refined air quality modeling or emission controls are needed. The EETs may also not be as accurate when used as a screening tool for deep-water operations farther offshore. Recommendation: The Study should develop EET formulas and methods based on meteorological conditions that are most conducive to elevated levels of ozone, PM2.5, and PM10, and the EET methods developed for the criteria pollutants should eliminate false negatives. Finding: The cumulative effect of biases and uncertainties across the meteorological and emission datasets, and from the air quality models themselves, could be quite large and result in EET estimates that are higher than they should be, or lower than they need to be. Recommendation: Future updates to the Study should conduct a formal uncertainty and error analysis that takes into account cumulative uncertainties from meteorological and emission inputs to the air quality modeling, and the three pollutant dispersion models should be compared at offshore distances of 50 km and beyond. CONCLUDING THOUGHTS BOEM and its contractors conducted extensive emissions, meteorological, and air quality modeling to better understand the impact of current and potential future emissions from the OCS of the GOMR on air quality and to test EET approaches. In general, the committee found that the air quality modeling tools used were scientifically appropriate and well documented. However, certain aspects of the Study were found to lead to potential underestimates of the impacts of GOMR emissions on air quality and EETs that would not identify all cases where additional air quality modeling or emission controls are warranted. In particular, the Study chose a base year (2012) for ozone, PM2.5, and PM10 with more historically average or typical conditions, rather than focusing on those conditions that are most conducive to pollution exceedances (2011). Furthermore, the EET development was not conservative and allowed for false negatives. Specifically, ï· the meteorological analyses and photochemical modeling have not been evaluated for their performance of conditions typical of when offshore emissions would have their largest impact on air quality on land and during the most critical times,
8 REVIEW OF THE BOEM âAIR QUALITY MODELING IN THE GOMRâ STUDY ï· the choice of base year does not account for increasing temperatures resulting from climate change, ï· future emissions are only included through 2022, meaning that potential emissions from 2023-2036 are not considered in the future case, and ï· the CART approach for the EETs allows for false negatives and, in some cases, has physically unrealistic results. As such, the Studyâs current results have the potential to underestimate the current and future impacts of OCS emissions on air quality, visibility, and deposition. Furthermore, the EET methods developed are not fully protective of future emissions, leading to increased high pollutant levels and potential exceedances of the NAAQS. The overall utility of the Study could be improved if the Study authors build off of the extensive modeling and analyses that were already conducted and address the shortcomings outlined in this reportâs findings and recommendations. The Study authors are in a unique position to further advance understanding of how OCS sources impact air quality in the GOMR and develop robust and protective EET approaches.