The primary classifying variable was the observed daily cumulative ozone exposure using the W126 ... Only the days during the ozone season (April-October) were considered. Visibility ...... Without additional action. (e.g. B1 or B3 emission ...
Response of Ozone, PM2.5, and Acid Deposition in the Southern Appalachian Mountains to Future Year Emission Scenarios James W. Boylan, Mehmet T. Odman, James G. Wilkinson and Armistead G. Russell School of Civil and Environmental Engineering, Georgia Institute of Technology, 200 Bobby Dodd Way, Atlanta GA 30332-0512 Stephen F. Mueller and Robert E. Imhoff Tennessee Valley Authority, PO Box 1010, Muscle Shoals AL 35662 Patricia F. Brewer Southern Appalachian Mountains Initiative, 59 Woodfin Place, Asheville NC 28801 Abstract -- As part of the Southern Appalachians Mountains Initiative (SAMI), a comprehensive air quality modeling system has been developed to assess the impact of three different emission scenarios on air quality for the years 2010 and 2040. Results have been aggregated over nine characteristic episodes, representing 69 days, to find the expected response of seasonal ozone, annual average PM2.5 and annual average wet and dry deposition. These levels provide the basis for SAMI's regional effects modeling assessment. It was found that ozone can be reduced with nitrogen oxide (NOx) controls. Sulfate aerosols and sulfur deposition decrease significantly in the Class I areas in response to sulfur dioxide (SO2) emission controls. However, an increase in nitrate aerosol levels may result due to an increase in free ammonia becoming available in response to reductions in SO2 and increases in NH3 emissions. Also, changes in total nitrogen deposition were minimal, except when ammonia emissions are controlled. Keywords: air quality, ozone, aerosols, wet deposition, regional modeling, SAMI, control strategies, emission projections INTRODUCTION Studies that have been conducted in national parks, forests and wilderness areas of the Southern Appalachian Mountains have documented adverse air pollution effects on visibility, streams, soil, and vegetation (Sisler and Malm, 2000; Heck et al., 1998; Cowling, 1989). Although it is known that current air pollution levels at parks and wilderness areas are due to emissions from a variety of sources (large and small, mobile and stationary, near and distant), the relative contribution of each source type to specific environmental resources in the region is not well quantified. The 1990 Clean Air Act Amendments (CAAA) require major emissions reductions for primary airborne pollutants, including SO2, NOx, and volatile organic compounds (VOCs). The reductions are expected to produce air quality improvements; however, it is uncertain whether the results will be enough to protect and preserve the ecosystems and natural resources of the Southern Appalachians, especially in Class I areas. The Southern Appalachian Mountains Initiative (SAMI) is currently assessing the impacts of emission controls on air quality and air quality related values through atmospheric modeling.
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The SAMI atmospheric modeling system consists of the Urban-to-Regional Multiscale One Atmosphere (URM-1ATM) model (Boylan et al., 2002a) for the air quality modeling, the Regional Atmospheric Modeling System (RAMS) (Pielke et al., 1992) for meteorology, and the Emission Modeling System (EMS-95) (Wilkinson et al., 1994) for emissions processing. Figure 1 shows the domain and grid configuration used by the URM-1ATM model in the SAMI assessment. The grid cell dimensions are 192, 96, 48, 24, and 12 km with the finest resolution (12 km) cells roughly following the Southern Appalachian Mountains. In the vertical, the domain extends from the surface to a height of 12,867 m and is divided into seven layers with thickness of 19 m, 43 m, 432 m, 999 m, 1779 m, 3588 m, and 6007 m, respectively. A more detailed discussion on the URM-1ATM model and its setup can be found in Boylan et al. (2002a).
Figure 1: Map of SAMI modeling domain and grid structure. The 12-km grid is indicated by the tightly hatched region. SEASONAL AND ANNUAL AIR QUALITY METRICS In contrast to many air quality modeling efforts, SAMI’s primary interest is in assessing the impact of emission control scenarios on visibility and the ecosystems (aquatic and terrestrial) in the Southern Appalachian Mountains. Specific focus is placed on the air quality and air quality values in the national parks and wilderness areas designated as Class I. This requires consideration of longer term air quality metrics, such as the responses of seasonal cumulative ozone, annual average PM2.5 (by species), and annual average acid deposition to emission
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controls. Given the choice of modeling several continuous years (one year likely will not be representative), or selecting a series of episodes specifically chosen to characterize longer term meteorology and air quality, SAMI opted for the latter as being more tractable. Data classification and optimization techniques (Deuel and Douglas, 1998) were used to select nine episodes, each 6 to 9 days long (plus 2 ramp-up days), between the years 1991 and 1995. As part of the episode selection process, each day (or week in the case of acid deposition) is assigned a statistically-defined category or class depending on the observed pollutant levels. For SAMI, the classes were defined at two national parks, Great Smoky Mountains (GRSM) in Tennessee/North Carolina and Shenandoah (SHEN) in Virginia. SAMI categorized individual days into one of four ozone classes. The primary classifying variable was the observed daily cumulative ozone exposure using the W126 metric (Lefohn and Runeckles, 1987) to calculate a weighted sum of hourly ozone concentrations. Ozone W126 was selected because it can be used to evaluate the ozone effects on forests and vegetation. Only the days during the ozone season (April-October) were considered. Visibility class (1 through 5) was defined as one of five levels representing the measured daily total fine aerosol mass (sulfate, nitrate, organics, and soils). Wet deposition class (1 through 4) was defined as one of four levels representing the observed sum of selected cations (calcium and magnesium) and anions (sulfate and nitrate) in weekly precipitation. Dry deposition classes were assigned after the episodes were selected. Wet and dry deposition classes were based on weekly monitoring data because daily measurements were not available. In each case, class number increased with the severity of pollutant levels with Class 1 days being the least polluted and class 4 or 5 days being the most polluted. Table 1 shows the percentage of days that are represented by each class over a season or year. Table 1: Percent of days falling into each class based on the severity of pollutant levels (Deuel and Douglas, 1998). Species Class 1 Class 2 Class 3 Class 4 Class 5 Ozone 70% 20% 7% 3% N/A Wet Deposition 70% 20% 7% 3% N/A Dry Deposition 70% 20% 7% 3% N/A Aerosols 20% 30% 30% 17% 3% These classes were then used to select a set of multi-day episodes to represent the full spectrum of ozone, deposition and visibility conditions for modeling. The nine episodes listed in Table 2 were chosen for detailed modeling. A general description of the severity of the ozone, aerosols, and acid deposition levels is also included for each episode that will be used to calculate the corresponding seasonal and annual air quality metrics. This modeling approach was designed to provide insight into the atmospheric response to different emission strategies under a variety of conditions. It was also meant to provide a framework for scaling episodic model results up to seasonal and annual impacts.
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Table 2: SAMI episodes and the severity of the pollutant levels used to develop seasonal and annual air quality metrics. Episode February 8-13, 1994 March 23-31, 1993 April 26-May 3,1995 May 11-17,1993 May 24-29, 1995 June 24-29, 1992 July 23-31, 1991 July 11-19, 1995 August 3-11, 1993
Ozone N/A N/A Low Low to Moderate Moderate Moderate Low to Moderate High Low
Aerosols Low Low Low to Moderate Moderate Moderate Moderate to High High High Moderate
Acid Deposition Moderate Moderate Low to Moderate Moderate to High Low to Moderate Low to Moderate High Low Low to Moderate
Table 3: Ozone and aerosol classes and their contribution (weight) to the seasonal cumulative ozone (W126) and annual average visibility metrics at Great Smoky Mountains (GRSM) and Shenandoah (SHEN) National Parks. Ozone Date GRSM SHEN (MM/DD/YY) Class Weight (%) Class Weight (%) 07/23/91 – – 3 0.17 07/24/91 – – – – 07/25/91 – – – – 07/26/91 3 1.46 2 1.19 07/27/91 2 0.77 – – 07/28/91 1 4.00 2 1.19 07/29/91 – – – – 07/30/91 – – 1 5.85 07/31/91 2 0.77 2 1.19 06/24/92 4 1.13 2 1.19 06/25/92 – – 1 6.10 06/26/92 1 2.87 2 1.19 06/27/92 – – – – 06/28/92 3 1.27 2 1.19 06/29/92 – – 3 2.30 03/23/93 – – – – 03/24/93 – – – – 03/25/93 – – – – 03/26/93 – – – – 03/27/93 – – – – 03/28/93 – – – – 03/29/93 – – – – 03/30/93 – – – – 03/31/93 – – – – 05/11/93 2 0.84 3 0.17 05/12/93 – – 3 2.02 05/13/93 1 10.66 1 5.85 05/14/93 – – – – 05/15/93 2 3.07 – –
Visibility GRSM SHEN Class Weight (%) Class Weight (%) – – – – – – 4 2.10 – – – – – – – – 5 1.39 – – – – – – – – – – – – – – 5 0.38 4 2.19 4 4.03 4 0.79 – – – – – – – – – – 4 0.79 – – – – – – – – – – – – 2 26.41 2 1.37 – – – – – – – – 1 10.39 – – – – – – – – – – – – – – – – 1 17.90 – – – – – – 4 2.55 – – – – – – – – 3 6.99 3 4.50
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05/16/93 05/17/93 08/03/93 08/04/93 08/05/93 08/06/93 08/07/93 08/08/93 08/09/93 08/10/93 08/11/93 02/08/94 02/09/94 02/10/94 02/11/94 02/12/94 02/13/94 04/26/95 04/27/95 04/28/95 04/29/95 04/30/95 05/01/95 05/02/95 05/03/95 05/24/95 05/25/95 05/26/95 05/27/95 05/28/95 05/29/95 07/11/95 07/12/95 07/13/95 07/14/95 07/15/95 07/16/95 07/17/95 07/18/95 07/19/95
2 2 – – 2 1 1 2 2 – 2 – – – – – – 2 – – 3 – 1 1 2 3 3 3 2 – 1 3 3 3 4 3 – 3 – 4
0.84 0.52 – – 3.07 10.66 4.00 1.58 0.77 – 3.07 – – – – – – 3.07 – – 0.64 – 10.66 10.66 1.58 1.27 0.64 0.37 1.58 – 10.66 0.52 1.27 1.27 1.57 0.37 – 0.92 – 1.57
– – – – 1 1 – – 2 2 2 – – – – – – – 4 1 2 – 1 1 1 3 2 – – 1 1 3 3 4 4 4 – 2 2 2
– – – – 6.10 5.85 – – 1.19 1.19 1.19 – – – – – – – 0.36 5.45 3.77 – 5.85 5.85 5.85 2.02 1.19 – – 5.85 5.85 2.02 0.67 0.62 1.76 1.01 – 2.00 1.19 3.62
– – – 3 – – 3 – – – 4 – 1 – – – – 2 – – 3 – – – – – – – 4 – – – 4 – – 5 – – – –
– – – 4.60 – – 4.60 – – – 4.03 – 14.98 – – – – 3.59 – – 9.16 – – – – – – – 4.03 – – – 4.03 – – 1.39 – – – –
– – – 3 – – 4 – – – 4 – 1 – – – – 2 – – 2 – – – 2 4 – – 3 – – – 5 – – 5 – – – 3
– – – 2.95 – – 2.55 – – – 1.58 – 17.90 – – – – 10.14 – – 10.14 – – – 10.14 2.10 – – 3.26 – – – 2.05 – – 2.05 – – – 2.95
Table 3 lists the ozone and visibility classes for modeled days and gives the percentage contribution (weight) to the seasonal ozone and annual visibility metrics at the Great Smoky Mountain (GRSM) and Shenandoah (SHEN) National Parks. Table 4 lists the weeklong periods for which classes were assigned and the percentage contribution to the annual wet and dry deposition metrics at GRSM and SHEN. Dry deposition was not optimized during the episode selection process. Instead it was classified after the fact leading to episodes that under represent the amount of dry deposition contributing to the annual metric.
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Table 4: Wet and dry deposition classes and their contribution (weight) to the annual metrics at Great Smoky Mountains (GRSM) and Shenandoah (SHEN) National Parks. Wet Deposition Dry Deposition Period GRSM SHEN GRSM SHEN (MM/DD/YY) Class Weight (%) Class Weight (%) Class Weight (%) Class Weight (%) 07/23/91 – 07/30/91 4 3.81 4 2.00 1 14.70 1 13.97 06/23/92 – 06/30/92 2 18.36 1 30.79 – – 1 13.97 03/23/93 – 03/30/93 2 9.13 3 13.88 1 14.67 1 8.89 05/11/93 – 05/18/93 3 4.46 4 2.00 – – 2 6.00 08/03/93 – 08/10/93 3 4.46 – – 1 14.67 1 13.90 02/08/94 – 02/15/94 2 9.13 2 4.75 1 11.86 1 8.94 04/25/95 – 05/01/95 1 14.61 2 11.05 1 14.67 – – 05/23/95 – 05/30/95 1 36.04 2 4.75 1 14.72 1 13.95 07/11/95 – 07/18/95 – – 1 30.79 1 14.70 2 20.37
To calculate a seasonal or annual average pollutant level at each site, the following relation was used: N
Caverage = ∑ i =1
wi Ci 100
(1)
where, Caverage is the modeled seasonal or annual average pollutant value, N is the number of weighted periods (days or weeks) contributing to the metric, wi is the percent contribution of period i to the metric, and Ci is the modeled daily or weekly pollutant value. Ozone W126 is used to represent the cumulative seasonal exposure to ozone. The W126 exposure index was selected to characterize ozone trends and relate vegetation yield reduction to ozone exposure. The daily cumulative ozone W126 is calculated using Equation 2 and the sigmoidally weighted function (fi) given in Equation 3 (Lefohn and Runeckles, 1987): 24
W 126 = ∑ ci f i i =1
fi =
(2)
1 1 + Me − Aci
(3)
where, ci is the hourly ozone concentration in ppm, fi is the weighting factor, M is 4403, and A is 126 ppm-1. The W126 index focuses on the higher hourly average concentrations, while retaining the mid- and lower-level values. Equation 2 was used to reconstruct the cumulative seasonal ozone W126 using model predicted ozone concentrations for each weighted day at GRSM and SHEN. Multiplying the daily average cumulative reconstructed ozone W126 values by the number of days in the ozone season (214) results in the seasonal cumulative reconstructed ozone W126. The modeled and observed seasonal cumulative reconstructed ozone W126 are
6
Ozone W126 (ppm-hrs)
compared at GRSM (Look Rock - elevation of 793 m) and SHEN (Big Meadows – elevation of 1073 m) in Figure 2. The ozone observations were taken from the Aerometric Information Retrieval System (AIRS) database (USEPA, 2001a). The model under predicts the seasonal W126 at GRSM and SHEN by 39% and 50%, respectively. This is due to the underestimation of the ozone peaks that carry the greatest weight in calculating the seasonal ozone W126. Figure 3 shows a comparison of the annual average observations and model simulated concentrations of fine sulfate (SO4), nitrate (NO3), ammonium (NH4), organics (OC), elemental carbon (EC), and soils at GRSM (Look Rock) and SHEN (Big Meadows) by using Equation 1 to weight each episode day. Speciated daily average observations were extracted from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network (NPS, 2000). Annual average sulfate and ammonium are under predicted by approximately 17% at GRSM, but show little bias at SHEN. Nitrates and soils are overestimated at both sites. Organic and elemental carbon model predictions are within 7% of the observed values at GRSM and SHEN.
80.0
Seasonal Ozone W126
60.0 40.0 20.0 0.0
Great Smoky Mountains Observed
Shenandoah Modeled
Figure 2: Comparison of modeled and observed seasonal ozone W126 at Great Smoky Mountains (Look Rock) and Shenandoah (Big Meadows) National Parks.
7
14.0
3
Concentration (µ g/m )
Annual Average Aerosol Concentrations 12.0
GRSM
SHEN
10.0
Soils EC
8.0
OC
6.0
NH4 NO3
4.0
SO4
2.0 0.0 MODEL
IMPROVE
MODEL
IMPROVE
Figure 3: Comparison of modeled and observed annual averaged fine aerosol concentrations at Great Smoky Mountains (GRSM – Look Rock) and Shenandoah (SHEN – Big Meadows) National Parks. . A comparison of the annual averaged wet deposition using weekly observations from the National Atmospheric Deposition Program (NADP) and model predicted mass fluxes for sulfate (SO4), nitrate (NO3), ammonium (NH4), calcium (Ca), and magnesium (Mg) at GRSM (Elkmont – elevation of 640 m) and SHEN (Big Meadows – elevation of 1074 m) are shown in Figure 4. Sulfate and nitrate wet deposition are overpredicted at GRSM by 86% and 95%, respectively. However, sulfate and nitrate wet deposition is under predicted at SHEN by 5% and 35%, respectively. Ammonium is over predicted by approximately 600% at GRSM and 140% at SHEN. Calcium and magnesium are over estimated at both sites, but the deposition mass fluxes are small compared to the other species. A comprehensive set of performance statistics for all SAMI atmospheric modeling (ozone, aerosols, wet and dry deposition) can be found in Boylan et al. (2002b).
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Deposition Flux (mg/m2)
Annual Average Wet Deposition Mass Fluxes 200.0
GRSM
SHEN
150.0 Mg Ca
100.0
NH4 NO3 SO4
50.0
0.0
MODEL
NADP
MODEL
NADP
Figure 4: Comparison of modeled and observed annual averaged wet deposition mass fluxes at Great Smoky Mountains (GRSM – Elkmont) and Shenandoah (SHEN – Big Meadows) National Parks. FUTURE YEAR EMISSION SCENARIOS Three emission control scenarios were developed by SAMI’s Policy Committee to reflect their assumptions about future growth in the demand for goods and services and the implementation of regulations and incentives (SAMI, 2001). Each strategy proposes progressively more stringent emission controls in each of five major source categories: utility, industrial, highway vehicle, non-road engines, and area sources for the years 2010 and 2040. From least stringent to most stringent, the three strategies are dubbed “A2”, “B1”, and “B3”. Strategy results for 2040 are highly dependent on the assumptions about the future decisions that businesses, government, and individuals may make. The purpose of the strategies is to approximate both the upper and lower boundaries of likely future emissions under SAMI’s assumptions. Each strategy used in this modeling exercise is not necessarily being considered as an option for policy recommendation. A brief description of the emission control assumptions for each strategy follows. For a more complete description, refer to Pechan (2001). The A2 strategy assumes reductions of VOCs and oxides of nitrogen (NOx) emissions from the laws and regulations mandated by the Clean Air Act (CAA) as amended in 1977 and 1990 to comply with the 1-hour ozone standard; reductions of SO2 and NOx from utility sources under Title V of the 1990 CAA amendments; and reductions of NOx and VOCs from mobile sources under Tier I tailpipe standards and fuel rules. In addition, this strategy assumes emission reductions from several recently promulgated regulations: regional NOx reductions which will be included in “State Implementation Plans” to reduce ozone (USEPA, 1998); NOx and VOC reductions resulting from implementation of Tier II and low sulfur rules (USEPA, 2001b); and
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VOC reductions resulting from Maximum Achievable Control Technology (MACT) standards (USEPA, 1990). A2 does not include the emissions reductions that might be required for the 8hour ozone National Ambient Air Quality Standards (NAAQS), the new PM2.5 NAAQS, or the regional haze rule. A2 is applied for all the eastern United States and is the reference strategy against which the two other strategies will be evaluated. The B1 strategy simulates emission reductions from the A2 reference strategy’s inventory, plus state-of-the-art emission controls applied to all sources as soon as technologically feasible, and “off the shelf” controls for 2010 and existing prototypes for 2040. It also includes logistical constraints to the implementation of emissions controls. B3 assumes emission reductions from the A2 reference strategy’s inventory, plus the most advanced existing and evolving technologies applied to all sources for 2010 and 2040. This strategy is intended to approximate an upper bound of emission reductions (given the current assumptions) without consideration of economic or technical feasibility. B1 and B3 emissions reductions are applied only within the eight SAMI states; emissions in the rest of the eastern U.S. are assumed to remain the same as the A2 strategy. The SAMI emission inventories include ammonia (NH3), carbon monoxide (CO), SO2, NOx, VOCs , and speciated particles less than 10 and less than 2.5 micrometers in diameter (PM10 and PM2.5). Annual inventories were developed for each of the SAMI emission reduction strategies for 1990, 2010 and 2040, as well as the nine air quality modeling episodes in 19911995. All three emission reduction strategies project that SO2 and NOx emissions will decrease in 2010 and 2040; however, VOCs, fine particles, and NH3 show both increases and decreases in emissions depending on the specific strategy and year (Figure 5). A more detailed discussion of SO2 and NOx emissions follows. Annual SO2 emission rates in the eight SAMI states in 1990 and projected annual SO2 emission rates in 2010 and 2040 under the three SAMI strategies are illustrated in Figure 6. In the SAMI states, annual SO2 emissions are projected to decrease by 23% for the 2010 A2 strategy compared to the 1990 levels. Annual SO2 emissions in 2010 would be reduced by 49% and 86% under the B1 and B3 strategies, respectively. The majority of the projected SO2 reductions in 2010 are attributed to the addition of scrubbers to coal-fired generating units. Utility SO2 emissions reductions in 2040 assume retirement and replacement or repowering with cleaner technologies, or adding emissions reduction equipment for most of the existing coal-fired power plants.
10
Million Tons/Year
Annual Emissions in the SAMI States 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0
SO2 1990
2010-A2
NOx 2010-B1
VOC 2010-B3
PM2.5
2040-A2
2040-B1
NH3 2040-B3
Figure 5: Emissions from the eight SAMI states in 1990 and projected to 2010 and 2040 for the A2, B1, and B3 strategies. VOC emissions reported here are anthropogenic and do not include biogenic emissions.
Million Tons/Year
Annual SO2 Emissions in the SAMI States 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0
2010
1990
Base Industrial
A2
Utility
B1 Nonroad
2040
B3
A2
B1
Highway Vehicle
B3 Area
Figure 6: SO2 emissions in 1990 and projected to 2010 and 2040 for the three SAMI emissions reduction strategies.
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Annual NOx emissions in the eight SAMI states are projected to be 20% lower in 2010 than in 1990 under the A2 reference strategy (Figure 7). Emissions from utilities and highway vehicles will be reduced in response to federal regulations, while emissions from non-road engines and area sources are projected to increase slightly. Under the A2 strategy, annual NOx emissions in the eight SAMI states are projected to be 3.3 million tons in 2010. Annual NOx emissions would be reduced by 45% and 72% under B1 and B3 strategies, respectively. These reductions would be accomplished by a combination of advanced controls on utility and industrial sources, as well as cleaner fuels and improved engines in the highway vehicle, nonroad engine, and area source sectors.
Million Tons/Year
Annual NOx Emissions in the SAMI States 5.0 4.0
2010
1990
2040
3.0 2.0 1.0 0.0 Base
Industrial
A2
Utility
B1 Nonroad
B3
A2
B1
Highway Vehicle
B3 Area
Figure 7: NOx emissions in 1990 and projected to 2010 and 2040 for the three SAMI emissions reduction strategies. Figure 8 compares NOx emissions from the 8 SAMI states to the total NOx emissions in the modeling domain. Domain wide annual NOx emissions are projected to decrease between 24% (2010-A2) and 48% (2040-B3) from the base year (1990). NOx emissions from the SAMI states contribute between 23% (1990, 2010-A2, and 2040-A2) and 7% (2040-B3) to the domain wide NOx emissions; the majority of the NOx emissions are from outside the SAMI states. Figure 9 contains total emissions of nitrogen for each of the SAMI control strategies. Total annual nitrogen emissions are calculated by summing the nitrogen contribution from the NOx and NH3 emissions. The NOx emissions are assumed to consist of 90% NO and 10% NO2. The majority of the nitrogen emissions are from outside the SAMI states; the SAMI states contribute between 20% (1990, 2010-A2, and 2040-A2) and 5% (2040-B3) to the domain wide nitrogen emission totals. Note, the domain wide annual nitrogen emissions do not decrease as much as the domain wide annual NOx emissions due to increased NH3 emissions outside the SAMI states
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Million Tons/Year
for all emission control strategies and inside the SAMI states for the A2 and B1 strategies. The contribution of ammonia emissions to the total domain wide emissions of nitrogen are approximately 25% for the base year (1990), 40% for the 2010 strategies, and 50% for the 2040 strategies. The reductions in domain wide annual nitrogen emissions from the base year are between 6% (2040-A2) and 20% (2010-B3 and 2040-B3).
24.0 20.0
Annual NOx Emissions 1990
2010
2040
16.0 12.0 8.0 4.0 0.0
Base
A2
Domain Wide
B1
B3
A2
30 Non-SAMI States
B1
B3
SAMI States
Figure 8: NOx emissions in 1990 and projected to 2010 and 2040 for the three SAMI emissions reduction strategies.
13
Million Tons/Year
14.0 12.0
Annual Nitrogen Emissions 1990
2010
2040
10.0 8.0 6.0 4.0 2.0 0.0
Base
A2
Domain Wide
B1
B3
A2
30 Non-SAMI States
B1
B3
SAMI States
Figure 9: Nitrogen emissions in 1990 and projected to 2010 and 2040 for the three SAMI emissions reduction strategies. FUTURE YEAR STRATEGY RESULTS Using the basecase meteorology for each episode and the emission projections (A2, B1, and B3) described in the previous section for 2010 and 2040, the URM-1ATM model was applied to the nine SAMI episodes. Future pollutant levels were simulated for cumulative seasonal ozone (May-October), annual average speciated fine PM, and annual average wet and dry deposition. The initial conditions of SO2, sulfate aerosols, and ammonium aerosols were scaled proportionally to the SO2 emission reductions and the initial conditions of NOx and were scaled proportionally to the NOx emission reductions. Because the controls cited in the previous section would have little or no effect outside the modeling domain, the boundary conditions were not changed. For the purpose of this paper, the following discussion will focus on pollutant responses at Great Smoky Mountains (GRSM) and Shenandoah (SHEN) National Parks. Other Class I areas were examined, but will not be presented here. In summary, Class I sites located in Georgia and North Carolina showed similar responses to those at GRSM, and sites located in West Virginia and Virginia showed similar responses to those at SHEN. Seasonal Ozone Results One way to examine the effects of the different emission reduction strategies on ozone is to look at hourly plots of ozone at different sites for each episode. The example in Figure 10 compares the hourly ozone concentrations resulting from the three emission control strategies for 2010 and 2040 to the basecase for the July 11-19, 1995 episode at GRSM (Look Rock).
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Great Smoky Mountains (TN) - July 1995 Ozone (ppm)
0.090 0.080 0.070 0.060 0.050 0.040 0.030
11
12 Base
13 2010-A2
14 2040-A2
15
16 2010-B1
17 2040-B1
18 2010-B3
19
20 2040-B3
Figure 10: Diurnal variations of ozone using A2, B1, and B3 emission control strategies in the years 2010 and 2040 for the July 11-19, 1995 episode at GRSM. The 2010-A2, 2010-B1, and 2040-A2 emission control strategies each show similar reductions in peak ozone ranging from approximately 10 to 15 ppb. The 2040 B3 strategy shows peak ozone reductions between 10 and 25 ppb. The 2040-B1 and 2010-B3 strategies fall somewhere between the 2010-A2 and 2040-B3 results. All three 2010 and 2040 strategies also show reductions in the nighttime ozone; however, there is little change in the magnitudes of the nighttime reductions for different strategies. Figure 11 contains the modeled growing season cumulative reconstructed ozone W126 for the A2, B1, and B3 emission control strategies for 2010 and 2040 and compares them to the basecase at GRSM and SHEN. Significant reductions in ozone W126 are projected to occur at both GRSM (40%) and SHEN (48%) due to the emissions reductions under the 2010-A2 strategy. Additional reductions for 2010-B1 are minimal, but there is approximately a 30% reduction in ozone W126 from the 2010-A2 strategy to the 2010-B3 strategy. The ozone W126 for 2040-A2 is very similar to that for 2010-A2 at both GRSM and SHEN. Both B1 and B3 strategies yield incremental benefits at GRSM (15% for B1 and 35% for B3) and SHEN (20% for B1 and 35% for B3) in the year 2040.
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Ozone W126 (ppm-hrs)
Seasonal Ozone W126
50.0 40.0 30.0 20.0 10.0 0.0 Basecase
2010-A2
2010-B1
2010-B3
Great Smoky Mountains
2040-A2
2040-B1
2040-B3
Shenandoah
Figure 11: Estimated seasonal cumulative ozone W126 for 2010 and 2040 for the three emission reduction strategies. Annual Aerosol Results Daily averaged speciated fine aerosol concentrations were examined for each day contributing to the annual average for the basecase and three emission control strategies for 2010 and 2040. Figure 12 shows the daily averaged sulfate concentrations for the basecase and the A2, B1, and B3 control startaegies for 2010 at GRSM. Low sulfate days (Class 1 and 2) show minimal changes and can even show an increase in sulfate concentrations for some control strategies. The increase in sulfate is thought to be due to either an increase in SO2 emissions or an increases in heterogenous sulfate chemistry. Although SO2 emissions decrease in the SAMI states, there are some areas that show local increases in SO2 emissions. These increases may be due to new sources coming on-line in 2010, increased SO2 production by an existing source, or a discrepency in the way the basecase and future emission inventories were developed. In the future scenarios, utility boilers are assumed to be operating all hours. However, in the basecase runs, actual hourly emissions (some being zero for non-operating units) were used. An increase in heterogenous sulfate chemistry could result from (1) an increase in hydrogen peroxide resulting from a decrease in NOx emissions and/or (2) an increase in the pH of the liquid droplets due to increased ammonia emissions, leading to greater oxidation by ozone. The days with higher sulfate concentrations (Class 4 and 5) show significant reductions for each of the three control strategies, except for May 27, 1995 which shows an increase in sulfate under the 2010A2 possibly due to the reasons mentioned above. Although not shown in this paper, nitrate concentrations change slightly for Class 1 and 2 days, but increase significantly for Class 4 and 5 days. The increase in nitrate is a result of more free ammonia being available to convert gasphase nitric acid to the nitrate aerosol form due to a decrease in sulfate aerosols and an increase in ammonia emissions. Ammonium aerosols tend to increase due to an increase in ammonia emissions for the A2 and B1 strategies, but show a significant decrease for the B3 strategy.
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Organics show little change because the biogenic emissions were assumed to remain the same in the future year control strategies. For specific details on how each of these species are affected by the different emission control strategies refer to the Georgia Tech web site (GIT, 2001).
Class 1
Class 2
03/24/93
20.0
03/27/93
Conc. (µ g/m3 )
Great Smoky Mountains (TN) - Average Sulfate Class 3
Class 4
Class 5
15.0 10.0 5.0
Basecase
2010-A2
2010-B1
07/15/95
07/31/91
07/27/91
07/12/95
05/27/95
08/11/93
06/24/92
04/29/95
08/07/93
08/04/93
05/15/93
04/26/95
02/09/94
0.0
2010-B3
Figure 12: Modeled sulfate concentrations at Great Smoky Mountains for the basecase and the A2, B1, and B3 emission control strategies in 2010. Figures 13 and 14 demonstrate how various components of the annual PM2.5 at GRSM and SHEN respond to the changes in emissions under the A2, B1, and B3 strategies. The relative changes are dependent on the starting point for the changes (i.e., basecase levels). The reductions cannot reach 100% even if all the emissions were eliminated because the boundary conditions for the future scenarios are the same as the basecase. As emissions are reduced, the benefits get smaller because of the relative increase in the contribution of transport into the domain. Dailyaveraged sulfate concentrations decrease by 6% (2010-A2) to 60% (2040-B3) at GRSM and 16% (2010-A2) to 58% (2040-B3) at SHEN. Nitrate increases for the A2 and B1 strategies at GRSM, but decreases slightly for the B3 strategy. On the other hand, nitrate decreases for all the strategies at SHEN, except for 2040-A2 where nitrate increases slightly. This difference in the responses of the two sites is probably due to the proximity of SHEN to SO2 and NOx sources that are reduced and to the site’s relatively larger distance from the NH3 sources that are predicted to increase in 2010 and 2040. Ammonium concentrations increase slightly at GRSM for the 2010A2 strategy, but decreased by 8% (2010-B1) to 53% (2040-B3) for the other strategies. Ammonium decreased for all strategies at SHEN by 10% (2010-A2) to 47% (2040-B3). The change in organic aerosols is small at both sites, except the B3 strategies that show approximately a 15% decrease for both 2010 and 2040. Elemental carbon was reduced under all control strategies with decreases up to 50% (2040-B3) at both sites. Soil concentrations increased for the A2 and B1 strategies, but decrease for the B3 strategies at GRSM. Large reductions in soils (up to 45%) were predicted at SHEN under the B3 strategy.
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Concentration (µ g/m3)
Annual Average Fine PM at Great Smoky Mountains 12.0 10.0 Soils EC ORG NH4 NO3 SO4
8.0 6.0 4.0 2.0 0.0 Basecase
2010-A2
2010-B1
2010-B3
2040-A2
2040-B1
2040-B3
Figure 13: Model estimates for annual average PM2.5 concentrations at Great Smoky Mountains National Park for the basecase and A2, B1, and B3 emission control strategies for 2010 and 2040.
3
Concentration (µ g/m )
Annual Average Fine PM at Shenandoah 12.0 10.0 Soils
8.0
EC ORG
6.0
NH4
4.0
NO3 SO4
2.0 0.0 Basecase
2010-A2
2010-B1
2010-B3
2040-A2
2040-B1
2040-B3
Figure 14: Model estimates for annual average PM2.5 concentrations at Shenandoah National Park for the basecase and A2, B1, and B3 emission control strategies for 2010 and 2040. Annual Acid Deposition Results Weekly cumulative wet and dry deposition mass fluxes by species were modeled and examined for each week contributing to the annual average for the basecase and three emission control strategies for 2010 and 2040. Deposited species examined for acid deposition assessment include:
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Dry:
SO2 (gas), NO2 (gas), NO (gas), NH3 (gas), HNO3 (gas), HONO (gas), N2O5 (gas), SO4 (aerosol), NO3 (aerosol), NH4 (aerosol), Mg (aerosol), Ca (aerosol) Wet: SO2 (very small), SO4, NO3, NH4, H+ (hydrogen ion), Mg, Ca The wet and dry deposition fluxes of sulfur, oxidized nitrogen, reduced nitrogen, and total nitrogen were computed from the above, as shown in Table 5. Table 5: Equations used to evaluate the sulfur and nitrogen wet and dry deposition. Species Equation used to determine deposition dry 32 32 S SO dry SO dry 2 + 4 96 64 14 14 14 28 14 14 N dry oxidized NO dry + NO dry NO 3dry + N 2 O 5dry + HONO dry + HNO 3dry 2 + 30 46 62 108 47 63 14 14 N dry reduced NH 3dry + NH dry 4 17 18 dry N dry N dry oxidized + N reduced
S wet wet N oxidized wet N reduced
N wet
32 32 SO 2wet + SO 4wet 64 96 14 NO 3wet 62 14 NH 4wet 18 wet wet N oxidized + N reduced
Figure 15 shows weekly cumulative wet deposition of sulfate at GRSM for the basecase and A2, B1, and B3 control startaegies for 2010 and 2040. Each strategy shows decreases in sulfate from the basecase levels except for the 2010-A2 strategy for the May 1995 and February 1994 episodes. Significant decreases in wet sulfate deposition are seen for all other strategies. Although not shown here, nitrate, calcium, and magnesium wet deposition fluxes show minimal fluctuation from the basecase for all episodes and emission control strategies. Ammonium deposition typically shows either a slight increase or decrease from the basecase for the A2 and B1 strategies, while the B3 strategy always shows a significant decrease from the basecase. Hydrogen ion deposition decreases with each strategy due to the reduction in sulfate. Again, specific details for these species are not presented here, but can be found on the Georgia Tech web site (GIT, 2001).
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2
Sulfate Flux (mg/m )
Sulfate Wet Deposition at Great Smoky Mountains (Elkmont) 120.0
Class 1
Class 2
Class 3
Class 4
90.0 60.0 30.0 0.0 April 1995
May 1995
Basecase
June 1992 March 1993 Febr 1994
2010-A2
2010-B1
2010-B3
2040-A2
May 1993 August 1993 July 1991
2040-B1
2010-B3
Figure 15: Modeled weekly cumulative sulfate wet deposition fluxes at Great Smoky Mountains for the basecase and the A2, B1, and B3 emission control strategies in 2010 and 2040. Figure 16 shows nitric acid dry deposition for the basecase and for the A2, B1, and B3 control startaegies for 2010 and 2040 at SHEN. The gas phase nitric acid dry deposition decreased from the basecase for all three emission control strategies, not only at SHEN but GRSM as well (not shown). Recall that the aerosol nitrate concentrations, hence aerosol nitrate dry deposition, typically increased due to the conversion of gaseous nitric acid to aerosol nitrate as a result of increased free ammonia gas. The free ammonia increases were due to decreased sulfate formation and increased ammonia emissions. This is also the reason that ammonia gas dry deposition increased significantly for the A2 and B1 strategies. Ammonium dry deposition did not change much from the basecase except under the B3 strategy which resulted in larger decreases because of significant reductions in ammonia emissions. The other nitrogen containing species (NO, NO2, HONO, and N2O5) all showed decreased concentrations and dry deposition fluxes in all the future year strategies. Gas phase SO2 and aerosol phase SO4 both showed significant decreases in dry deposition for all control strategies.
Class 1
20.0
Class 2
2
HNO3 Flux (mg/m )
Nitric Acid Dry Deposition at Shenandoah (Big Meadows)
15.0 10.0 5.0 0.0 July 1991
June 1992 March 1993 August 1993
Basecase
2010-A2
2010-B1
2010-B3
Feb 1994
2040-A2
May 1995
2040-B1
May 1993
July 1995
2040-B3
Figure 16: Modeled weekly cumulative nitric acid dry deposition fluxes at Shenandoah for the basecase and the A2, B1, and B3 emission control strategies in 2010 and 2040.
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Figures 17 and 18 show the annual average wet and dry deposition mass fluxes of sulfate at GRSM and SHEN for the A2, B1, and B3 strategies for the years 2010 and 2040. The modeled wet deposition of sulfur is significantly higher than the dry deposition. However, it should be noted that the episodes in Table 2 did not consider dry deposition during the selection process. Therefore, there may be a bias towards the wet episodes in the annual average deposition fluxes. Both wet and dry sulfate deposition show significant reductions for each of the emission control strategies. At GRSM, wet sulfate deposition decreases by 5% (2010-A2) to 59% (2040-B3), while dry deposition decreases by 36% (2010-A2) to 78% (2040-B3). Wet sulfate deposition decreases by 24% (2010-A2) to 73% (2040-B3) at SHEN, while dry deposition decreases by 41% (2010-A2) to 88% (2040-B3). These reductions are similar to the reductions in SO2 emissions for each strategy (Figure 6).
Mass Flux (mg/m2)
Sulfur Deposition at Great Smoky Mountains 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Basecase
2010-A2
2010-B1
2010-B3
Wet Sulfur
2040-A2
2040-B1
2040-B3
Dry Sulfur
Figure 17: Annual average wet and dry deposition mass fluxes of sulfur at Great Smoky Mountains for the basecase and the three control strategies in 2010 and 2040.
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Mass Flux (mg/m2)
Sulfur Deposition at Shenandoah 25.0 20.0 15.0 10.0 5.0 0.0 Basecase
2010-A2
2010-B1
2010-B3
Wet Sulfur
2040-A2
2040-B1
2040-B3
Dry Sulfur
Figure 18: Annual average wet and dry deposition mass fluxes of sulfur at Shenandoah for the basecase and the three control strategies in 2010 and 2040. Annual average wet and dry deposition mass fluxes of oxidized and reduced nitrogen at GRSM and SHEN are shown in Figures 19 and 20. Modeled wet deposition of nitrogen is significantly greater than the dry deposition of nitrogen, especially at GRSM. Although there were large reductions in NOx emissions in the SAMI states for each of the three control strategies, the wet deposition of the oxidized nitrogen does not change significantly at either site. The low response in nitrate wet deposition is likely due to NOx that is transported to the Appalachian Mountains from outside the SAMI region. Recall that the boundary conditions were not changed for the future year control strategies and that NOx emissions were not reduced outside the SAMI states for the B1 and B3 control strategies. Furthermore, NH3 emissions in the 30 non-SAMI states (accounting for 85% - 97% of the domain wide NH3 emissions) increase by 38% in 2010 and by 84% in 2040. This, in addition to SO2 reductions, results in more free ammonia becoming available to convert gas-phase nitric acid to the aerosol nitrate form. The dry deposition rates of aerosol nitrate are much lower than for gas-phase nitric acid. Therefore, oxidized nitrogen in the form of aerosol nitrate can be transported greater distances and in greater concentration before being deposited to the surface. The wet deposition module can scavenge mass from all layers in the model, especially the upper layers where the clouds are formed. The pollutant levels in these upper layers are influenced by long-range transport from regions outside the SAMI states and the boundary, so reductions in local NOx emissions may not significantly change the amount of nitrate wet deposition. Also, the wet deposition of the reduced nitrogen does not change significantly with either the A2 or B1 strategies, but does show decreases between 10% and 15% for the B3 strategies. The dry deposition of oxidized nitrogen at GRSM decreases for all three strategies with reductions ranging from 8% - 10% (2010-A2 and 2040-A2) to 37% (2040-B3), while reductions ranging from 25% (2010-A2 and 2040-A2) to 66% (2040-B3) were simulated at SHEN. For
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ground-level sources, local emission reductions typically show a greater influence on the pollutant levels in the first layer than pollutant levels in the upper layers. Therefore, the reductions in oxidized nitrogen dry deposition are much greater than those for wet deposition (on a percentage basis) because dry deposition fluxes are a function of pollutant levels in the first layer of the model, whereas wet deposition fluxes are affected by pollutant levels in all layers of the model (especially the upper layers). The reduced nitrogen dry deposition increases for all three strategies at GRSM; these increases range from 34% (2010-B3) to 123% (2040-A2). At SHEN, the reduced nitrogen dry deposition shows increases for the A2 and B1 strategies between 48% (2010-A2) and 143% (2040-A2), but shows reductions of 11% and 6% for the 2010-B3 and 2040-B3 strategies, respectively. The increase in reduced nitrogen deposition was a result of an increase in ammonia emissions for the A2 and B1 strategies.
Mass Flux (mg/m2)
Nitrogen Deposition at Great Smoky Mountains 50.0 40.0 30.0 20.0 10.0 0.0 Basecase
2010-A2
Wet Oxidized N
2010-B1
2010-B3
Wet Reduced N
2040-A2
Dry Oxidized N
2040-B1
2040-B3
Dry Reduced N
Figure 19: Annual average wet and dry deposition mass fluxes of oxidized and reduced nitrogen at Great Smoky Mountains for the basecase and the three control strategies in 2010 and 2040. .
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Mass Flux (mg/m2)
Nitrogen Deposition at Shenandoah 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Basecase
2010-A2
Wet Oxidized N
2010-B1
2010-B3
Wet Reduced N
2040-A2
Dry Oxidized N
2040-B1
2040-B3
Dry Reduced N
Figure 20: Annual average wet and dry deposition mass fluxes of oxidized and reduced nitrogen at Shenandoah for the basecase and the three control strategies in 2010 and 2040. Typical annual observations indicate that the mass of sulfur and nitrogen removed from the atmosphere by wet deposition processes is approximately equal to the amount removed by dry deposition processes. Recall, that the modeled annual average wet deposition of ammonium (reduced nitrogen) was overpredicted by approximately 600% at GRSM and 140% at SHEN. Decreasing the modeled reduced nitrogen by these percentages would make the ratio of wet to dry deposition much closer to values that are typically observed. Although the modeled absolute mass fluxes may differ from those typically observed, the percentage change in fluxes for each control scenario is still thought to be reliable. CONCLUSIONS
The URM-1ATM/RAMS/EMS-95 atmospheric modeling system was used to assess how emissions controls will affect ozone, particulate matter and acid deposition in the Southern Appalachian Mountains. The system's ability to estimate the levels of ozone, PM2.5, and wet deposition were evaluated by using measurements taken in the Southern Appalachians. Three future year emission control strategies were simulated and evaluated for the years 2010 and 2040 using historic meteorological data from nine episodes that were selected to characterize a typical season or year between 1991-1995. The responses to future year emission strategies were analyzed at selected receptors within the SAMI region. Responses were different in magnitude and sometimes in direction depending on the location of the receptor. During the July 1995 episode, the daily maximum ozone at Great Smokey Mountains National Park (GRSM) is estimated to decrease by 10-15 ppb in 2010 and 10-25 ppb in 2040 from the baseyear levels, depending on the strategy. The B3 strategy leads to the largest decreases followed by the B1 and A2 strategies. Significant decreases in cumulative ozone W126 at GRSM and
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Shenandoah National Park (SHEN) are estimated to occur by 2010 (A2 strategy) due to expected NOx emissions reductions from the basecase period. Without additional action (e.g. B1 or B3 emission control strategies), ozone W126 is estimated to remain nearly unchanged from 2010 to 2040. Fine particulate sulfate is estimated to decrease in 2010 and drop further in 2040. As the stringency of the strategy increases so does the reduction in sulfate levels at GRSM and SHEN. Nitrate levels, on the other hand, tend to increase due to additional ammonia becoming available in response to reductions in SO2 and increases in NH3 emissions. The model estimated an increase in annual average PM2.5 at GRSM in 2010 under the A2 strategy; other strategies are estimated to lead to decreases. In 2040, the model estimated decreases between 8% (A2 strategy) and 40% (B3 strategy). At SHEN future PM2.5 levels are estimated to decrease between 10% (2010-A2) to 44% (2040-A2). The episode selection process considered wet deposition but not the dry deposition. For this reason, the ratio of estimated annual average dry deposition to wet deposition may be biased low. Nevertheless, both wet and dry sulfate deposition estimates show significant reductions for each of the emission strategies. The relative magnitudes of these reductions between different strategies are similar to the relative reductions in SO2 emissions for each strategy. The model estimated an increase in the dry deposition of reduced nitrogen with all future year strategies. This is probably due to the increase in ammonia emissions. Wet deposition of reduced nitrogen is also estimated to increase under the 2010-A2, 2010-B1 and 2040-A2 strategies. Dry deposition of oxidized nitrogen is estimated to decrease but the wet deposition of oxidized nitrogen is estimated to remain unchanged. The latter was a surprising result given the level of NOx reductions in the SAMI states. However, NOx reductions are not as extensive outside the SAMI states and the increases in NH3 emissions are probably compensating for the NOx reductions. This result also suggests that the source of wet oxidized nitrogen deposition at Class I areas of the SAMI region is outside the SAMI states. The increases in reduced nitrogen deposition may compensate or even exceed the decreases in oxidized nitrogen resulting from NOx emission reductions. Acknowledgements – This research was sponsored by the Southern Appalachian Mountain Initiative (SAMI) under grants from the U.S. Environmental Protection Agency and the member states. REFERENCES
Boylan J.W., Odman M.T., Wilkinson J.G., Russell A.G., Doty K.G., Norris W.B. and McNider R.T. (2002a) Development of a comprehensive, multiscale “one atmosphere” modeling system: application to the Southern Appalachian Mountains. Atmos. Environ., in press. Boylan J.W., Odman M.T., Wilkinson J.W., Russell A.G., Doty K.G., Norris W.B., McNider R.T., Mueller S.F., and Imhoff R.E. (2002b) Performance evaluation of the URM-1ATM modeling system in the Southern Appalachian Mountains. Submitted to J. Air Waste Manage Assoc.
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Cowling E.B. (1998) Recent changes in chemical climate and related effects on forests in North America and Europe. AMBIO 18, 167-171. Deuel H.P. and Douglas, S.G. (1998) Episode selection for the integrated analysis of ozone, visibility and acid deposition for the Southern Appalachian Mountains; Systems Applications International, Inc.: San Rafael, CA. SYSAPP-98/07r1. GIT (2001) The SAMI Atmospheric Modeling Home Page at Georgia Institute of Technology. http://environmental.gatech.edu/SAMI Heck W.W., Furiness C.S., Cowling E.B. and Sims C.K. (1998) Effects of ozone on crop, forest, and natural ecosystems: assessment of research needs, EM. October, 11-22. Lefohn A.S. and Runeckles V.C. (1987) Establishing a standard to protect vegetation - ozone exposure/dose considerations. Atmospheric Environment 21, 561-568. NPS (2000) National Park Service Air Quality Research Division Fort Collins. Anonymous ftp at ftp://alta_vista.cira.colostate.edu in /data/improve. Pechan (2001) Pechan /Avanti Group. Southern Appalachian Mountains Initiative (SAMI) Emissions Projections to 2010 and 2040: Growth and Control Data and Emission Estimation Methodologies. Draft Final Report # 01.07.002/9405.000. Pielke R.A., Cotton W.R., Walko R.L., Tremback C.J., Lyons W.A., Grasso L.D., Nicholls M.E., Moran M.D., Wesley D.A., Lee T.J. and Copeland J.H. (1992) A comprehensive meteorological modeling system - RAMS. Meteor. Atmos. Phys. 49, 69-91. Sisler J.F. and Malm W.C. (2000) Interpretation of Trends of PM2.5 and Reconstructed Visibility from the IMPROVE Network. J. Air & Waste Manage. Assoc. 50, 775-789. USEPA (1990) Section 112 of the Clean Air Act Amendments. http://www.epa.gov/ttn/atw/mactfnl.html. USEPA (1998) 40 CFR Parts 51, 72, 75, and 96. Federal Register Vol. 63, No. 207, 5735657538. USEPA (2001a) EPA AIRS Data. U.S. Environmental Protection Agency, Office of Air Quality Planning & Standards, Information Transfer & Program Integration Division, Information Transfer Group. www.epa.gov/airsdata. USEPA (2001b) 40 CFR Parts 80 and 86. Federal Register Vol. 66, No. 72, 19295-19311. Wilkinson J.G., Loomis C.F., McNally D.E., Emigh R.A. and Tesche T.W. (1994) Technical Formulation Document: SARMAP/LMOS Emissions Modeling System (EMS-95). AG-90/TS26 & AG-90/TS27. Alpine Geophysics, Pittsburgh, PA.
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