Modeling Emissions and Ozone Air Quality Impacts of Future

Article Cite This: Environ. Sci. Technol. 2019, 53, 7893−7902

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Modeling Emissions and Ozone Air Quality Impacts of Future Scenarios for Energy and Power Production in the Rocky Mountain States Rene Nsanzineza,† Shannon L. Capps,‡ and Jana B. Milford*,† †

Department of Mechanical Engineering, University of Colorado at Boulder, Boulder, Colorado 80309-0427, United States Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States

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‡

S Supporting Information *

ABSTRACT: This study examines air quality impacts of scenarios for energy production and use in 2030 across Colorado, northern New Mexico, Utah, and Wyoming. Scenarios feature contrasting levels of oil and gas production and shares of electricity from coal, natural gas, and renewables. Hourly emissions are resolved for individual power plants; oil and gas emissions are basin-specific. Ozone decreased from 2011 to the 2030 baseline, with median and 90th percentile reductions in maximum daily 8 h average (MDA8) ozone across the four-state domain of 3.5 and 7.1 ppb, respectively, resulting in 200 fewer premature deaths annually. Relative to the 2030 baseline, MDA8 ozone increased in the “cheap gas” scenario, with median and 90th percentile increases of 0.1 and 1.0 ppb, and declined in a scenario with greenhouse gas (GHG) emissions fees, with median and 90th percentile reductions of 0.2 and 1.5 ppb. Reduced coal generation lowered SO2 emissions in all future scenarios compared to 2011. GHG emissions from electricity and oil and gas production declined by 4% (CO2-equivalent) from 2011 to the 2030 baseline, increased by 10% from the 2030 baseline to the cheap gas scenario, and declined by 28% from the 2030 baseline to the GHG fees scenario.

1. INTRODUCTION Recent technological advances have opened up new resources for oil and gas production and lowered the costs of renewables in the Rocky Mountain (RM) region, affecting greenhouse gas (GHG) emissions, air quality, and associated health and environmental risks. In 2017, Colorado (CO), New Mexico (NM), Utah (UT), and Wyoming (WY) produced 46% of U.S. coal,1 17% of the country’s marketed natural gas,2 and 12% of its crude oil.3 Natural gas production in CO, UT, and WY increased sharply from 2005 to 2012 and then flattened out or declined. Oil production continued to increase through 2017, especially in CO and NM. In 2017, coal accounted for 72% of these four states’ total electricity generation, with shares ranging from 54% in CO to 93% in WY.4 Natural gas contributed 15% and wind 8% of these states’ electricity generation in 2017. These shares are expected to continue growing. CO, NM, and UT have renewable portfolio standards that require yet more wind and solar,5 while coal plants in all four states are being retired or repowered with natural gas. Many previous studies have examined the air quality effects of emissions from power plants,6−9 oil and gas production,10−14 or both.15,16 For example, Pasci et al. (2013; 2015)15,16 examined the effects on air quality of coupled changes in emissions from oil and gas production and © 2019 American Chemical Society

electricity generation in Texas, using dispatch models to estimate emissions from existing power plants for different natural gas price scenarios. With more abundant natural gas and lower prices, they found that the increased emissions from additional oil and gas production in the Barnett Shale basin would be counterbalanced by reduced emissions from electricity generation, leading to an overall ozone decrease.15 For a similar scenario but with the natural gas coming from the Eagle Ford Shale, they found ozone would be increased in the energy production area but reduced in northeastern Texas.16 Future trends in energy production, use, and associated emissions are difficult to predict, due to uncertain political and economic factors and changing technology. Nevertheless, examination of future scenarios can provide insight into the environmental costs and benefits of competing energy pathways. In this study, we link energy system and air quality models across decadal to hourly time scales to examine how changes in the RM energy landscape might affect emissions and air quality in the 2030 time frame. We consider four Received: Revised: Accepted: Published: 7893

January 16, 2019 June 4, 2019 June 10, 2019 June 10, 2019 DOI: 10.1021/acs.est.9b00356 Environ. Sci. Technol. 2019, 53, 7893−7902

Article

Environmental Science & Technology

RM region, we use sectoral scaling factors for emissions in 2030 taken directly from McLeod et al. (2014),18 as shown in Supporting Information (SI) Tables SI.1.1a−d. The SI also provides more detail on how the scaling factors were calculated and used. 2.2. Air Quality Model and Tools. We used the Comprehensive Air Quality Model with Extensions (CAMx) to examine the effects of the future emissions scenarios on ozone air quality in the RM region. Emissions were processed using the Sparse Matrix Operator Kernel Emission (SMOKE, https://www.cmascenter.org/smoke/), and human health costs and benefits of changes in ozone levels were estimated using the Benefits Mapping Analysis Program Community Edition (BenMAP-CE) version 1.3 (https://www.epa.gov/ benmap). Initial SMOKE and CAMx input data, software, and configurations were provided by the Intermountain West Data Warehouse (IWDW) from their 2011 version b air quality study.13 The IWDW study was undertaken to develop a state of the science oil and gas emissions inventory and an air quality modeling platform that would be available for use in scenario analysis.21 The 2011 version b platform is recommended primarily for studies of summer ozone and is considered less suitable for winter ozone and particulate matter due to model performance limitations. IWDW provided CAMx v6.10 (http://www.camx.com/) configured with the Carbon Bond 6 revision 2 (CB6r2) chemical mechanism for ozone modeling with the bimodal aerosol module.22 IWDW set up CAMx in a nested grid with an outer domain having a 36 km by 36 km horizontal grid resolution covering the Continental United States (CONUS) including parts of Canada and Mexico, a 12 km by 12 km domain focused on the western U.S, and a 4 km by 4 km domain covering CO, UT, northern NM, and WY. We ran the model for the 2011 base year for performance evaluation and benchmarking purposes. For the 2030 emissions scenarios, we modified the anthropogenic emissions and kept other inputs constant, including biogenic and fire emissions. We ran CAMx from March 21 to September 21 for 2011 and for the 2030 scenarios to evaluate the impacts of estimated emissions changes in the spring and summer seasons. A spin up period from March 21 to 31 was used to reduce the effects of initial conditions. We examined modeled ozone concentrations for the full modeling period (April to September) in all three CAMx domains, but for brevity, we present the results only for the 4 km CAMx domain in the summer when the highest ozone occurs. Because there was relatively little difference in total nitrogen oxides (NOx) and volatile organic compounds (VOC) emissions between the costly gas and GHG fees scenarios, we did not run the costly gas emissions scenario in CAMx. 2.3. 2011 CAMx Model Inputs. IWDW provided anthropogenic emissions data for 2011 based on EPA’s National Emissions Inventory (NEI), while natural emissions and fire emissions were from Ramboll-Environ and Air Sciences Inc.22 Meteorology inputs were developed using the Weather Research and Forecasting (WRF) model version 3.5.1.23 The IWDW study used initial and boundary conditions from the Model for Ozone and Related Chemical Tracers (MOZART) (https://www2.acom.ucar.edu/gcm/mozart). They also tested initial and boundary conditions from the Goddard Earth Observing System Chemistry (GEOS-Chem) model (http://acmg.seas.harvard.edu/geos/). We ran CAMx in 2011 without changing any input data to benchmark our

scenarios that represent contrasting assumptions about the future cost and abundance of natural gas, imposition of greenhouse gas emissions fees, and corresponding renewable energy capacity in the RM region. Within the region, we focus on CO, northern NM, UT, and WY, where air quality concerns have been raised by oil and gas production activity.14,17 We estimate emissions changes for GHG (CO2 and methane), SO2, nitrogen oxides (NOx), and volatile organic compounds (VOC). For air quality modeling and associated health effects, we focus on summertime ozone due to ongoing challenges in the region with meeting the National Ambient Air Quality Standards (NAAQS) for this pollutant. Our study is distinguished from prior work because we consider coupled changes in energy production and use for the RM region and because the time horizon for the study is long enough to allow for changes in the electricity generating fleet, including substantially more renewable energy in some scenarios.

2. MATERIALS AND METHODS 2.1. Scenarios. The future scenarios examined in this study were developed by McLeod et al. (2014)18 using the MARKAL (MARKet ALlocation) least-cost planning model with the U.S. Environmental Protection Agency (EPA)’s nineregion (US9R) energy system database.19 This model provides annual emissions by energy sector for nine U.S. regions at fiveyear intervals over a 50-year planning horizon, accounting for pollution control requirements and numerous other system constraints.19 The “2030 baseline” scenario assumes a natural gas price and supply based on Annual Energy Outlook (AEO) projections published in 2013.18 The “cheap gas” scenario assumes an abundant supply of natural gas at lower prices, corresponding to the 2013 AEO high resource scenario.18 The contrasting “costly gas” scenario assumes a limited supply of natural gas at higher prices, corresponding to the 2013 AEO low resource scenario.18 Natural gas prices in the western states ranged from $4.72 to $11.44 (2011 $) per MMBtu across the scenarios while coal prices were fixed across scenarios and ranged from $0.97 to $9.21 per MMBtu between states. The GHG fees scenario assumes natural gas supply similar to the baseline scenario but applies system-wide fees to emissions of CH4 and CO2. The fees applied were $55 (2011 $) per metric ton of CO2 and $1400 (2011 $) per metric ton of CH4 based on the Interagency Working Group’s (2013) social cost of carbon for a 3% discount rate.20 The MARKAL results yielded annual generation from wind in the western U.S. ranging from 83 TWh in the baseline scenario to 268 TWh in the GHG fees scenario.18 For the RM region, we used scaling factors taken directly from McLeod et al. (2014)18 for the industrial, commercial, residential, and transportation sectors, but we refined emissions from oil and gas production and electricity generation, as discussed below. For electricity generation, emissions changes for each scenario are estimated at the hourly time scale and at the location of individual facilities using dispatch modeling, with fuel prices, GHG fees, and renewable energy capacity taken from McLeod et al.18 For oil and gas emissions, total gas production in each scenario in the RM region is taken from McLeod et al.,18 with recent production forecasts used to allocate the changes by basin. The Sections Power Plant Emissions in the Western U.S and Rocky Mountain Oil and Gas Emissions below provide more information on how future power plant and oil and gas emissions were estimated. For portions of the U.S. outside the 7894

DOI: 10.1021/acs.est.9b00356 Environ. Sci. Technol. 2019, 53, 7893−7902

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individual basins within the RM region. For this purpose, the RM region included CO, MT, NM, ND, SD, UT, and WY. Emissions were projected out to 2030 for each scenario, starting from the 2011 oil and gas emission inventory developed by ENVIRON International Corporation through a survey of producers in each basin.32 Oil and gas emissions from states outside this expanded RM region were scaled uniformly based on results from McLeod et al. (2014)18 (SI Tables SI.1.1a−d). To allocate future scenario emissions from the oil and gas sector to basins in the RM region, we first estimated the percentage contribution of each basin to the total oil and gas production activity in the RM region, considering spuds, active wells, gas produced, and oil produced in each basin. The 2011 activity levels were obtained from each state’s natural resources agency and the Western Regional Air Partnership Phase III study.33−38 For the future scenarios, activity levels for basins in Colorado were based on the Colorado Air Resources Management Modeling Study (CARMMS),14 which projected spuds (initial drilling of an oil or gas well), active wells, and production from 2015 to 2025 in low and high production scenarios. We used the 2025 high production scenario for our baseline and cheap gas scenarios and the low production scenario for our costly gas and GHG fees scenarios. For the other states in the RM region, we used 2016 oil and gas activity data as the 2030 activity levels because production levels in the relevant areas in these states have flattened out in recent years.33−37,39 The next step in estimating 2030 oil and gas emissions for each scenario was to combine the fraction of activities occurring in each RM basin with total activity levels for the region. For natural gas, these were taken from McLeod et al. (2014).18 For oil, we used updated projections from the 2017 AEO,40 reflecting a sharp increase in projected production made after McLeod et al. (2014)18 completed their study. The corresponding scaling factors are shown for each scenario in Tables SI.1.3a−b. The total 2030 oil and gas production was allocated using the calculated percentage contributions from each basin. The number of spuds and active wells in 2030 were estimated by scaling their 2011 levels by the ratio of 2030 gas production to 2011 production in each basin for each scenario. Each specific emission source (e.g., storage tanks or compressor engines) identified in the inventory by its source classification code (SCC) was matched with a scaling factor for the appropriate activity level.41 This method of using a basin specific scaling factor for each activity level leads to spatial variation in emissions, as equipment and operating practices reflected in the inventory differ across basins. For natural gas production, methane emissions were calculated using an emission rate from Weber and Clavin (2012)42 and the estimated gas production in 2030 in each state of the RM region. For oil production, methane emissions were calculated using the ratio of the CH4 emissions associated with oil production in the U.S. GHG inventory43 to the oil production for 2011, multiplied by the estimated oil production in 2030. Methane emissions from oil and gas production were converted to CO2 equivalents (CO2-e) using a global warming potential of 28.44 Next, regulations to be implemented after 2011 were incorporated into the projections. These regulations included the U.S. EPA’s 201245,46 and 201647−49 new source performance standards for VOC and CH4, EPA’s spark ignition engine and nonroad diesel engine regulations for CO, NOx, and

results against the IWDW run to confirm correct model implementation. However, we ultimately chose to use boundary conditions from GEOS-Chem instead of MOZART because the former gave slightly better CAMx performance for most of the summer period.24 The CAMx/GEOS-Chem model performance is discussed in Model Performance Evaluation and Sensitivity Analysis and SI Section SI.2. 2.4. 2030 CAMx Model Inputs. 2.4.1. Power Plant Emissions in the Western U.S. For each future scenario, refined hourly electricity system emissions were estimated using the PLEXOS economic and electricity dispatch model (https://energyexemplar.com/products/plexos-simulationsoftware/) applied to the Western Electricity Coordinating Council (WECC) region.25 Arizona (AZ), California (CA), Colorado (CO), Idaho (ID), Montana (MT), Nevada (NV), New Mexico (NM), Oregon (OR), Utah (UT), Washington (WA), and Wyoming (WY) were included in the development of detailed power plant emissions inventories. For power plant emissions from states outside this region, we applied uniform scaling factors from McLeod et al. (2014).18 Time and location-specific electricity demand data input to the PLEXOS model were based on the Transmission Expansion Planning Policy Committee’s (TEPPC) 2024 WECC database and were the same for all scenarios.26 Electricity demand assumed for 2030 totaled 1100 TWh for the Western Interconnection. Fuel prices, greenhouse gas fees, and renewables capacities for each scenario were taken from McLeod et al.18 Before running PLEXOS, the WECC system was modified to reflect planned power plant retirements and fuel switching. In total, 27 units were retired while 4 units switched from coal to natural gas. In the end, 85 coal-fired plants and 690 natural gas power plants were included in PLEXOS in addition to other sources of electricity. The PLEXOS model provided hourly electricity generation from different units for the whole year, as determined by operating costs and constraints. NOx emissions from power plants in 2030 were estimated for each scenario by combining unit level emission rates from each plant with the corresponding start-up hours and electricity generation from the PLEXOS results25 2030 NOx Em = (NOx ER|start − up × SH + NOx ER|stabilized × 2030 EG + I ) × CF

(1)

In eq 1, Em stands for emissions, ER represents the emission rate, SH is the start-up hours, EG is the electricity generation, I represents the emissions when there is no generation, and CF is the emission control factor. Power plants’ individual emission rates were calculated using hourly NOx emissions in 2011 from the Continuous Emission Monitoring System (CEMS) database (ftp://ftp.epa.gov/dmdnload/emissions/ smoke/). For 16 coal-fired units in CO, NM, and WY, an emission control factor was applied to reflect on-the-books NOx emissions regulations.27−29 SO2 and CO2 emissions for the 2030 scenarios were estimated by multiplying the 2030 heat input from PLEXOS with emissions factors calculated from the ratio of 2015 emissions to 2015 heat input for SO2 and emission rates from the U.S. Energy Information Administration (EIA) for CO2.30,31 See SI Sections SI.1.2 and SI.4 and Nsanzineza et al. (2017)25 for additional details. We used SMOKE to process estimated hourly unit-specific emissions from power plants for input to the CAMx model. 2.4.2. Rocky Mountain Oil and Gas Emissions. Projected changes in oil and gas emissions were spatially allocated to 7895


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Environmental Science & Technology VOC,50,51 and Colorado’s storage tanks regulations.52 The regulations were applied as control factors representing the fraction of emissions left after accounting for their effect, considering the effectiveness of the emission control technology, expected compliance with the regulation, and the feasibility of installing the emission control devices. Overall, the regulations reduced oil and gas NOx, VOC, and CH4 emissions by 48%, 42%, and 42%, respectively, in 2030. Tables SI.1.4a−d and SI.1.5a−c present the full set of scaling and control factors for each scenario. SMOKE’s growth and control modules were used to scale the oil and gas emissions for input to CAMx. 2.5. Model Performance Evaluation and Sensitivity Analysis. The IWDW evaluated CAMx model performance for the 2011 base year for seven monitoring locations in CO, UT, and WY.53 Because we substituted GEOS-Chem boundary conditions for those from MOZART, we repeated the performance evaluation including comparisons for six additional monitoring sites. Overall, the CAMx-GEOS-Chem model tended to overestimate ozone in the summer, with a median fractional bias in the maximum daily 8 h average (MDA8) ozone of 1.4% and an average fractional bias of 4%. Model performance was relatively good at rural and suburban sites, including the National Renewable Energy Laboratory (NREL), Chatfield, and Rocky Flats sites that typically record the highest ozone in the Denver metropolitan area. The model overestimated MDA8 ozone at downtown sites in Denver and Fort Collins by 14.7% and 19.2%, respectively. This is likely due to ozone titration by NOx emissions on nearby roadways, which occurs at smaller scales than those resolved by the model. Overall, our performance evaluation agreed with the results found by IWDW.53 See SI Section SI.2 for more information. We explored three ozone sensitivity cases using the brute force method applied to 2011 and the 2030 baseline scenario for July to gain insights on the relationships between specific emissions sources and ozone. The first case reduced NOx emissions across the CONUS modeling domain by 10% to determine the impact of NOx emission perturbations. The next case doubled the 2011 and 2030 VOC emissions from oil and gas sources in the RM region to determine the effect of possible underestimation of VOC inventories suggested by several studies that measured VOC and/or methane concentrations.54−57 The last sensitivity case examined the effect of eliminating storage tanks from the 2030 emissions inventory at oil and gas production sites in the RM region. Although storage tanks are a significant source of VOC emissions, some operators are moving toward tankless operations. This sensitivity case provides a bounding estimate of the potential effect of this new practice. 2.6. BenMAP-CE. We used BenMAP-CE to evaluate human health effects of ozone changes associated with our future emissions scenarios.58 We modified default features in BenMAP-CE to add a new 4 km by 4 km grid covering CO, northern NM, UT, and WY and to add health impact functions from newer epidemiological studies used in EPA’s regulatory impact analysis for the 2015 ozone NAAQS revision.59 Key health impact functions included those from Zanobetti and Schwartz (2008)60 and Smith et al. (2009)61 for short-term premature mortality and Jerrett et al. (2009)62 for long-term mortality. We used MDA8 ozone to calculate mortality corresponding to short-term exposure and seasonal average maximum daily 1 h average (MDA1) ozone for mortality from

long-term exposure. Both short-term and long-term mortality were evaluated for ozone in June, July, and August. As explained further in SI Section SI.6, the three-month summer average of MDA1 ozone that we used overestimates the sixmonth MDA1 average referenced in Jerrett et al. (2009).62 Correspondingly, we adjusted our estimates of long-term premature mortality from ozone downward by 16% from 2011 to the 2030 baseline scenario, 19% from the 2030 baseline to the cheap gas scenario, and 3% from the 2030 baseline to the GHG fees scenario. The reference population data set in BenMAP-CE is based on the 2010 U.S. Census. To estimate the population in later years, BenMAP-CE used population projections from Woods and Poole (2012).63 We ran the PopGrid program in BenMAP-CE to allocate population to the grid cells of the 4 km domain. Table SI.3.1 lists the full set of epidemiological studies and effect estimates used to quantify ozone-related health impacts in our study.

3. RESULTS AND DISCUSSION 3.1. Electricity Generation, Oil and Gas Production, and Associated Emissions. Table 1 shows how summertime oil and gas production, electricity generation, and associated emissions change across the four 2030 scenarios for CO, northern NM, UT, and WY, corresponding to the 4 km domain used in CAMx modeling. Results in Table 1 are for Table 1. Changes in Electricity Generation, Oil and Gas Production, and Emissions Across Scenarios for the 4 km CAMx Domain for June, July, and August % Change from 2030 Baseline Item Coal Generation [TWh] NG Generation [TWh] Renewables Generation [TWh] Total Generation [TWh] Gas Production [PJ] Oil Production [PJ] Power Plant CO2 [106 metric tons] O and G Prod. CO2-e [106 metric tons] Power Plant SO2 [103 metric tons] O and G Prod. SO2 [103 metric tons] Power Plant NOx [103 metric tons] O and G Prod. NOx [103 metric tons] O and G Prod. VOC [103 metric tons]

% Change 2030−2011

Cheap Gas

Costly Gas

GHG Fees

47

−14

−5

−8

−20

5 5

−17 17

9 0

−55 200

−68 352

56

−12

−3

10

16

6121 1173 45

14 297 −4

20 45 −4

−9 −30 −20

−2 −30 −39

31

−3

29

−16

−12

35

−39

−4

−17

−36

2

35

32

−8

−3

64

−46

−6

−9

−21

33

−8

23

−20

−15

452

74

39

−27

−25

2011

summer only to match the air quality modeling results presented below. Annual results and selected hourly results are presented in SI Section SI.4. Note that while the least-cost energy system models (MARKAL and PLEXOS) were used to develop plausible, self-consistent scenarios for analysis, the results are not to be viewed as predictions of future trends. Furthermore, while the best available inventory is used for the 2011 oil and gas emissions, these emissions and the future 7896


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Figure 1. (a) Hourly ozone on July 7, 2011 at 3 pm MST. (b) Hourly ozone on July 7, 2030 at 3 pm MST. (c) Change in hourly ozone from the 2030 baseline scenario to the cheap gas scenario. (d) Change in hourly ozone from the 2030 baseline scenario to the GHG fees scenario.

respectively, due to expanded wind capacity displacing both coal and natural gas. For power plants, the relative emission changes across scenarios are smaller in the summer than they are for annual emissions. For example, annual NOx emissions in the GHG fees scenario are 40% lower than in the 2030 baseline (Figure SI.4.5), but summer NOx emissions are only 21% lower. Details of this trend are illustrated in Figure SI.4.1, which shows hour-by-hour NOx emissions from coal-fired power plants in the 2030 scenarios for the first week of July. On many days in the summer, electricity demand increases due to air conditioning loads and wind resources are relatively low. Therefore, any available coal and natural gas power plants are dispatched to meet the demand regardless of the scenario. On these days, emissions are similar across scenarios except for a few hours at night when NOx is reduced, especially in the GHG fees scenario. On 37 days in June, July, and August, there was less than a 10% difference in power plant NOx emissions in the 4 km domain between the GHG fees scenario and the

allocation of oil and gas production across basins are particularly uncertain. As shown in Table 1, from 2011 to the 2030 baseline scenario, electricity generation from coal and natural gas decreases while generation from renewables increases. In the cheap gas scenario compared to the 2030 baseline, generation from coal declines while generation from natural gas increases. In the GHG fees scenario, generation from coal and natural gas decrease while generation from renewables increases by more than a factor of 4, with most of the increase coming from wind (Figure SI.4.2). Total summertime NOx emissions from power plants in the 4 km domain decrease by about half from 2011 to 2030, driven by a combination of power plant retirements, fuel switching, and NOx control regulations. In the 2030 cheap gas scenario, increased generation from natural gas displaces coal, resulting in a further 6% decrease of NOx emissions from power plants. NOx emissions in the costly gas and GHG fees scenarios are reduced from the 2030 baseline scenario by 9 and 21%, 7897


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Figure 2. (a) Changes in MDA8 ozone from 2011 baseline to 2030 baseline for all summer hours and grid cells in the 4 km domain. (b) Changes in MDA8 ozone from the 2030 baseline scenario to the cheap gas scenario. (c) Changes in hourly ozone from the 2030 baseline scenario to the GHG fees scenario.

2030 baseline. Nsanzineza et al. (2017) provide more details on power plants’ hourly emissions variations throughout the year.25 As shown in Table 1, oil and gas production increase from 2011 to the 2030 baseline scenario, with nearly a factor of 4 increase in oil production. This increase continues a trend that has already started, as actual oil production in 2016 was about two times greater than that in 2011.39 Across scenarios in 2030, oil and gas production increases from the baseline scenario to the cheap gas scenario and decreases in the costly gas and GHG fees scenarios. See Figure SI.4.4 for oil and gas production trends in the RM region beyond the 4 km domain. Total NOx emissions from the oil and gas production sector decrease from 2011 to the 2030 baseline scenario, with the magnitude of reduction varying by state. The net reduction in NOx emissions reflects more stringent regulations that more than offset increases in oil and gas production, although the balance differs across basins. Compared to the 2030 baseline, oil and gas NOx emissions increase in the cheap gas scenario and decrease in the costly gas and GHG fees scenarios. These trends in the 4 km domain are mainly influenced by NOx emissions from Colorado. VOC emissions from oil and gas production are higher in all of the 2030 scenarios than in 2011. The increases in VOC emissions correspond to greater oil and gas production, which is only partly offset by stricter emissions regulations for new wells and other equipment. The increase in VOC emissions is driven by the projected dramatic increase in oil production. VOC emissions are higher in the cheap gas scenario and lower in the costly gas and GHG fees scenarios than in the 2030 baseline scenario. See SI Section SI.4 for more details, including maps of how the differences between scenarios vary by basin. SO2 emissions from power plants decrease from 2011 to 2030 because of reductions in coal generation. In 2030, SO2 emissions decrease from the baseline scenario as natural gas displaces coal generation in the cheap gas scenario and wind energy displaces coal in the costly gas and GHG fees scenarios. CO2 emissions from power plants decrease from 2011 to 2030 because of reductions in both coal and natural gas generation. Relative to the 2030 baseline scenario, power plant CO2

emissions decrease only modestly in the cheap gas scenario. In the costly gas and especially the GHG fees scenarios, CO2 emissions decrease more as wind energy displaces coal and natural gas generation. Despite an increase in production, methane emissions (shown in Table 1 as CO2-e) from oil and gas production decrease from 2011 to the 2030 baseline because of new emissions control regulations, with additional reductions in the costly gas and GHG fees scenarios. However, methane emissions from oil and gas in the 2030 cheap gas scenario are higher than those in 2011, as increased production offsets the effect of the new regulations. 3.4. Influence of Emissions Scenarios on Ozone. Figure 1a,b shows the hourly ozone concentrations in the 4 km domain at 3 pm MST for July 7, with 2011 and 2030 baseline emissions, respectively, while Figure 1c,d shows the change in ozone between scenarios on the same day and time. July 7 was selected as a representative day that had high ozone. This day also had moderate changes in power plant NOx emissions between scenarios, with a 12% reduction in the Cheap Gas scenario and a 48% reduction in the GHG fees scenario compared to the 2030 baseline. SI Section 5.1 shows representative results for other days, including some without significant changes in power plant NOx emissions. To provide a more complete picture, Figure 2a−c shows the distribution of changes in MDA8 ozone for all cells of the 4 km CAMx domain for all summer days. As discussed above, the 2011 CAMx model performance is acceptable for scenario analysis but exhibits some bias, especially for core-urban sites. Furthermore, the 2030 CAMx runs use 2011 meteorology and other fixed inputs from that year, which do not reflect potential for future climate change or year to year meteorological variations. As expected, modeled ozone levels in summer 2011 were highest in July and August, with significant day to day variation due to meteorology. Peak concentrations were somewhat localized, with high ozone concentrations modeled downwind of power plants, in oil and gas basins, and in urban areas such as Denver, CO and Salt Lake City, UT. High ozone was modeled in northern New Mexico in July because of emissions from a large fire that lasted for about 35 days, starting the last week of June 2011. 7898


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Table 2. Partial Social Benefits or Costs of Changes in Ozone and GHG Emissions between Scenarios for the 4 km CAMx Domain for June, July, and August Scenario 2030 Baseline-2011 Baseline

2030 Cheap gas-2030 Baseline

2030 GHG fees-2030 Baseline

End point

Effect

Economic Value (million 2015 $)

Ozone, short-term mortality,61a Ozone, long-term mortality62 GHG emissions reductions Ozone, short-term mortality,61a Ozone, long-term mortality62 GHG emissions reductions Ozone, short-term mortality,61a Ozone, long-term mortality62 GHG emissions reductions

−47 (−23 to −71)b premature deaths −140 (−49 to −240)b premature deaths −3 million tons CO2-e 2 (1 to 3)b premature deaths 6 (2 to 10)b premature deaths 7 million tons CO2-e −4 (−2 to −6)b premature deaths −16 (−6 to −27)b premature deaths −20 million tons CO2-e

−490 (−44 to −1,400) −1,500 (−120 to −4,500) −160 18 (2 to 52) 60 (5 to 180) 380 −45 (−4 to −130) −170 (−14 to −510) −1,100

a Results shown here are calculated using Smith et al. (2009)61 estimates for short-term mortality. Results obtained using estimates from Zanobetti and Schwartz (2008)60 are up to two times higher (Section SI.6). bValues given in parentheses represent 95th percentile confidence intervals. Population in 2030 for short-term mortality calculations was 14,927,515 (all ages), while that for long-term mortality was 8,813,247 adults over the age of 29. Negative values denote a beneficial change, i.e., a reduced social cost.

during the peak ozone time period as a result of NOx and VOC emissions increases from the nearby Denver-Julesburg basin. In contrast, ozone in Salt Lake City, which is generally upwind of Utah’s oil and gas basins, stayed the same from the baseline to the cheap gas scenario. The 2030 GHG fees scenario has lower NOx and VOC emissions in most areas than the 2030 baseline scenario. Correspondingly, ozone was reduced from the 2030 baseline to the 2030 GHG fees scenarios in the oil and gas basins. The median and 90th percentile of the MDA8 ozone reductions from the baseline scenario to the GHG fees scenario were 0.2 and 1.5 ppb, respectively. The average MDA1 ozone in the summer decreases by 0.3 ppb for the median and by 1.4 ppb for the 90th percentile from the baseline scenario to the GHG fees scenario. Similar to the cheap gas scenario, ozone was also reduced downwind of power plants on some days in the summer when wind resources improved (e.g., SI Figure SI.5.4a,b). Ozone in Denver was substantially reduced from the 2030 baseline to the GHG fees scenario because of lower oil and gas NOx and VOC emissions in the Denver-Julesburg basin. Ozone in Salt Lake City decreased by a smaller amount. Changes in NOx and VOC emissions from oil and gas basins were assumed to occur uniformly throughout the summer, so the influence of these changes on ozone is observed throughout the modeling period, particularly in the DenverJulesburg, Piceance, Uinta, and South San Juan basins. Changes between scenarios in NOx emissions from power plants are intermittent. As illustrated in Figures SI.4.1 and SI.5.6, the GHG fees case has lower NOx emissions during some hours on some days (e.g., July 5 and 6) when good wind resources allow displacement of coal and natural gas generation. Changes in ozone near power plants are apparent between scenarios on those days (Figure SI.5.6). However, at other times emissions changes between scenarios were relatively small. Figure SI.5.5a−b shows results for a representative day when the changes in power plant NOx emissions were too small to significantly influence ozone. In the sensitivity tests, doubling oil and gas VOC emissions increased the MDA8 ozone concentrations in the basins located in the 4 km CAMx domain by median values of 0.2 to 1.0 ppb in July 2011 and by 0.2 to 1.7 ppb in July 2030, with the largest changes occurring in the Denver-Julesburg basin. Doubling VOC emissions in both 2011 and 2030 altered the median differences between years in MDA8 ozone concentrations by −0.1 to 0.7 ppb. Eliminating storage tank emissions

Overall, there are significant reductions in ozone across the modeling domain from 2011 to the 2030 baseline scenario but with high ozone persisting on many days near Denver and to a lesser degree in the Four Corners region and Salt Lake City. In Figure 2a, the median and 90th percentile MDA8 ozone reductions from 2011 to 2030 were 3.5 and 7.1 ppb, respectively. The median and 90th percentile summer (June, July, and August) average MDA1 ozone reductions from 2011 to 2030 were 3.6 and 5.0 ppb, respectively. The overall reductions in ozone resulted from reductions in NOx emissions from the electricity generation, oil and gas production, and transportation sectors, and reductions in VOC emissions from the transportation sector, as shown in Table 1 and Tables SI.1.1a−b. However, some of these reductions were offset by increases in emissions of VOC from oil and gas production and NOx from the industrial sector. On some days in central Denver, hourly ozone was higher in the 2030 baseline than in 2011 due to reduced ozone titration with lower NOx emissions. Consistent with this result, the sensitivity tests with NOx emissions that were reduced by 10% across the CONUS domain confirmed that reducing NOx emissions generally reduces ozone, but disbenefits occur at some limited locations, especially in 2011. With NOx emissions uniformly reduced by 10%, the median changes in MDA8 ozone in July in the 4 km domain were 1.8 ppb in 2011 and 1.5 ppb in 2030 (SI Section SI.5.2). The 2030 cheap gas scenario has lower summertime NOx emissions from power plants but higher NOx and VOC emissions from oil and gas than the 2030 baseline scenario. NOx and VOC from other sources were relatively constant between these two scenarios. Correspondingly, ozone generally increased from the 2030 baseline scenario to the 2030 cheap gas scenario, with relatively large increases occurring in the Denver-Julesburg, Piceance, Uinta, and South San Juan oil and gas basins, as illustrated in Figure 1c. The median and 90th percentiles of MDA8 ozone increase from the baseline scenario to the cheap gas scenario by 0.1 and 1.0 ppb, respectively. Similarly, the median and 90th percentile of the summer average MDA1 ozone increase from the baseline scenario to the cheap gas scenario by 0.2 and 0.9 ppb, respectively. Reductions in hourly ozone from the 2030 baseline scenario to the cheap gas scenario occurred downwind of power plants on some days, as natural gas power plants displaced some generation from coal-fired plants (see SI Figure SI.5.3a,b). In Denver, hourly ozone increased in the cheap gas scenario 7899


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costs of the scenarios that were not examined in this study include implications for near-source exposures to hazardous air pollutants such as benzene from oil and gas operations,64 visibility and public health benefits/costs from changes in particulate matter concentrations as a result of NOx and SO2 emissions changes, and ecosystem benefits/costs from changes in ozone and nitrogen deposition. This study demonstrates the necessity of resolving future emissions changes temporally and spatially when exploring air quality implications. Using a dispatch model to provide hourly power plant emissions estimates provides insight into the challenge of reducing emissions when peak ozone conditions coincide with high electricity loads and low wind resources. Use of basin specific scaling factors for oil and gas emissions shows the influence of cross-basin differences in equipment and operating practices. This study also demonstrates that the emission disbenefits from increased oil and gas production could offset the benefits from reduced use of coal for electricity production in the RM region. An implementation of fees on GHG emissions could increase the share of renewable energy in the region and deliver substantial reductions in emissions from both the electricity generation sector and oil and gas production sectors.

in the 2030 oil and gas operations mainly affected ozone in the Denver-Julesburg and Uinta basins, with median MDA8 ozone reductions of 1.6 and 0.2 ppb, respectively, compared to the original 2030 baseline scenario. See SI Section SI.5.2 for detailed results. Our results for ozone are broadly consistent with those of Vijayaraghavan et al. (2017),14 who used CAMx to compare air quality impacts of low, medium, and high emissions scenarios from oil and gas development for 2025 in CO and northern NM. They found declining total emissions reduced the number of monitoring locations exceeding the 70 ppb ozone NAAQS from 26 sites in 2011 to 6−8 sites in 2025, with the 2025 range depending on the development scenario.14 Unlike this work, however, Vijayaraghavan et al. (2017)14 did not consider tradeoffs with power plant emissions but used the same estimates of 2025 power plant emissions in all scenarios. Our results for the RM region qualitatively agree with Pacsi et al.’s (2015)16 results for the Eagle Ford Shale in TX in finding that for a given year with other emissions fixed, increased oil and gas production increased ozone in that basin, while expanded use of natural gas reduced ozone downwind of power plants. 3.5. Impacts of Changes in Ozone on Health Effects. Table 2 presents the estimated premature mortality impacts of the changes in ozone across scenarios together with their associated economic value. Estimated monetary benefits from changes in GHG emissions are also shown for comparison. Confidence intervals shown in Table 2 for premature mortality end points reflect uncertainties in the epidemiological studies used to estimate health impact functions and uncertainty in the economic valuation. SI Section SI.6 addresses additional uncertainties, including alternative health impact functions and population projections, and provides estimates of ozone impacts on selected morbidity end points. The reduction in ozone from 2011 to the 2030 baseline scenario is estimated to reduce total mortalities (sum of shortterm and long-term mortalities) by about 200 annual deaths. Assuming the value of a statistical life to be $8.7 million (2015$),59 these health benefits are valued at about $2 billion. The estimated reduction in long-term mortality is more than three times greater than that for short-term mortality. The health benefits estimated from ozone reductions between 2011 and 2030 would be reduced by about a third if the population was held at the 2011 level (SI Section SI.6). Implementation of GHG fees further reduces the 2030 total mortalities due to ozone, with a corresponding benefit of more than $200 million. The cheap gas scenario has a higher number of premature deaths than the 2030 baseline, with an estimated social disbenefit of about $80 million. The main aim of adopting GHG fees is to reduce GHG emissions, so the reduced mortalities from ozone due to NOx and VOC reductions in that scenario can be viewed as cobenefits. Summertime CO2 emissions from power plants and CH4 emissions from oil and gas production decreased by 4% from 2011 to 2030 in the 4 km domain, generating an estimated social benefit of $160 million. CO2 and CH4 emissions increased by 10% from the 2030 baseline to the cheap gas scenario, causing an estimated disbenefit of $380 million. In contrast, in the GHG fees scenario, CO2 and CH4 emissions decreased by 28%, corresponding to a $1 billion benefit. Changes in GHG emissions across scenarios would occur year-round, not just in the summer, with annual benefits or disbenefits approximately four times greater than those estimated for the summer months. Other cobenefits or social

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b00356.

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Additional information on emissions adjustments for future scenarios; CAMx model performance; and inputs for ozone health effects estimates. Additional results for future electricity generation, oil and gas production and associated emissions; ozone modeling and sensitivity analyses; and ozone health effects (PDF)

AUTHOR INFORMATION

Corresponding Author

* Tel:(303) 492-5542. Fax: (303) 492-3498. E-mail: Jana. [email protected]. ORCID

Rene Nsanzineza: 0000-0002-8754-0186 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Science Foundation’s (NSF) AirWaterGas Sustainability Research Network, grant number CBET-1240584. We would like to thank the IWDW for providing the 2011 CAMx input data and software. We thank Jeff McLeod, Greg Brinkman, and Matthew O’Connell for supporting collaborations on scenario development and electricity dispatch modeling, and Yanko Davila for computing support. Findings, opinions, conclusions, or recommendations expressed in this paper are those of the authors and do not necessary reflect the views of the NSF or IWDW.

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