Environ. Sci. Technol. 2002, 36, 3465-3473
Influence of Population Density and Temporal Variations in Emissions on the Air Quality Benefits of NOx Emission Trading CAROLYN E. NOBEL, ELENA C. MCDONALD-BULLER, YOSUKE KIMURA, KATHERINE E. LUMBLEY, AND DAVID T. ALLEN* Center for Energy and Environmental Resources, The University of Texas at Austin, M/S R7100, 10100 Burnet Road, Austin, Texas 78758
Ozone formation is a complex function of local hydrocarbon and nitrogen oxide emissions. Therefore, trading of NOx emissions among geographically distributed facilities can lead to more or less ozone formation than across-the-board reductions. Monte Carlo simulations of trading scenarios involving 51 large NOx point sources in eastern Texas were used in a previous study by the authors to assess the effects of trading on air quality benefits, as measured by changes in ozone concentrations. The results indicated that 12% of trading scenarios would lead to greater than a 25% variation from conventional across-the-board reductions when air quality benefits are based only on changes in ozone concentration. The current study found that when benefits are based on a metric related to population exposure to ozone, two-thirds of the trading scenarios lead to changes in air quality benefits of approximately 25%. Variability in air quality benefits is not as strongly dependent on the temporal distribution of NOx emissions.
Introduction Interest in and use of economic instruments for environmental protection has gained momentum in the past decade. In particular, the use of cap-and-trade programs for NOx emissions has been planned or implemented in several ozone nonattainment areas that have identified the need for severe NOx reductions. In cap-and-trade programs, each facility is allocated allowances; facilities that reduce their emissions below the number of allowances they hold can trade allowances or sell them to other facilities on the open market. The area-wide cap on allowances is reduced over time, thus lowering the net amount of pollutants emitted in the region (1). Currently, regional NOx cap-and-trade programs are or will soon be in effect in California (RECLAIM) (2), along the East Coast (NOx Budget Program)(1), and in Texas (3, 4). Few studies have addressed the environmental impacts of NOx emission trading (5-9). Because ozone formation is a strong, nonlinear function of the local concentrations of volatile organic compounds (VOC) and NOx and since local concentrations of VOCs can vary greatly among facilities participating in a trading program, ozone formation is strongly influenced by both where and when precursor * Corresponding author e-mail:
[email protected]; phone: (512)471-0049; fax: (512)471-1720. 10.1021/es0110168 CCC: $22.00 Published on Web 06/27/2002
2002 American Chemical Society
emissions occur. "Wrong-way trades” (e.g., where allocations are sold from a facility with low ozone productivity to a facility with high ozone productivity) have the potential to significantly reduce or eliminate the benefits of NOx emission trading (5). In a previous paper (9), we compared the potential air quality benefits of a NOx emission trading program to the air quality benefits of across-the-board NOx reductions in eastern Texas. The ozone productivity of NOx emissions was assessed for a set of over 50 facilities in eastern Texas using a regional photochemical air quality model, the Comprehensive Air Quality with Extensions (CAMx) (10). The air quality model provided data on ozone productivity of each facility as a function of time and location. These data were converted into a series of impact indices based on the net change in ozone mixing ratios for each facility, and these ozone impact indices were used to evaluate emission trading scenarios in eastern Texas. This study builds on that previous analysis of NOx emission trading. The objectives of the work reported herein are (i) to assess the impacts of trading NOx emissions that are held constant with time for NOx emissions that vary with time and (ii) to determine if the evaluation of impact indices based on human population densities leads to different results than evaluating trading based on the net change in ozone concentration. This work specifically examines: (i) Changes in ozone impact indices, the spatial distribution of impacts, and peak ozone concentrations for potential trades between facilities in eastern Texas with constant emissions and facilities with time-varying emissions. (ii) An impact index based on the net change in ozone concentrations only in areas with ozone levels above a threshold mixing ratio. Effects of changing the threshold ozone concentration on trading benefits were examined for eastern Texas. (iii) Differences in potential trading strategies based on the 24-h average net change in ozone concentration versus the 24-h net change in population exposure.
Methodology The computational framework for evaluating ozone productivity of stationary source NOx emissions has been described by ref 9 and is briefly summarized here. CAMx was applied to determine the ozone productivity of each facility in eastern Texas. Three different episodes representing 11 modeling days (to account for a range of meteorological variability) were modeled. The modeling parameters and episodes were selected to mirror those used in state regulatory analysis (11). Indices that quantify the relative air quality impacts of emissions reductions over space and time are produced for the facilities using a Visual Basic Application tool with Geographic Information Systems (GIS)-based data. Two types of indices were used in the previous study: an index based on the net change in ozone in a region and an index based on the net change in ozone over a threshold concentration. The net change in ozone index is a measure of the net ozone change that would occur in an area if a point source decreased its emissions by 1 ton of NOx per day:
net ozone impacth ) [
∑
all grid cells in region
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TABLE 1. Description of Four Facilities Used in the Temporal Analysis
facility id LTMc42r48 HGAc36r29 VICc28r23 CENc29r32
FIGURE 1. Locations of four facilities used in temporal emissions analysis. The index based on the net change in ozone over a threshold concentration was calculated using the change in ozone concentration only in those regions initially over 85 ppbv:
threshold ozone impacth )
∑
[
(base ozone concnh -
all grid cells >85 ppb threshold in region
ozone concn from 50% NOx reduction at facilityh)]/ [no. of grid cells in region × (ton of NOx/day)reduction] (2) The values of these indices depend on the size (i.e., the number of grid cells) of the region of interest. The eastern Texas region shown in Figure 1 was used for the previous and current studies. Ozone indices were calculated for every hour of the day. To produce single impact index values for each facility, impact index values were averaged either over 24 h (daily) or over 8 h (1100-1900). The 8-h average encompassed those times of the day when ozone levels are typically high. Additionally, the index values were averaged over the 11 modeling days to produce an aggregate index averaged over time and space. The combination of the two time periods and two types of impact indices led to four impact indices: (1) 24-h average net change in ozone (24 net) (2) 8-h average net change in ozone (8 net) (3) 24-h average net change in ozone over an 85 ppbv threshold (24 thresh) (4) 8-h average net change in ozone over an 85 ppbv threshold (8 thresh) Nobel et al. (9) applied a Monte Carlo analysis to calculate the probability distribution of potential trading impacts (i.e., the likelihood of good or bad trades) with the constraints that total emissions in the region must equal 50% of the original emissions and that emissions reductions from any facility could not exceed the original emission rate. The purpose of the Monte Carlo analysis was to examine the variability in air quality benefits of trading scenarios as 3466
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24 net impact NOx emissions index (per (ton/d) ton of NOx) 63.7 66.2 24.0 44.5
0.006207 0.003286 0.001931 0.004797
description rural, high biogenic emissions urban, high biogenic emissions urban, low biogenic emissions rural, low biogenic emissions
compared to across-the-board reductions. The study of Nobel et al. (9) considered only ozone reduction benefits. However, benefits associated with other factors such as control and production costs or, as in the current study, human exposure could also be incorporated into the analysis. The Monte Carlo analysis was conducted by assigning each facility a random number and sorting facilities based on the assignment. Emissions were reduced by 100% at each facility in descending order until the total reduction was equal to 50% of the original emissions for the region. Net benefits were calculated by summing the emissions reductions times the facility impact index over all facilities. A total of 5000 simulations were conducted for each modeling day and impact index. The results were used to calculate the probability of trading impacts for each index as compared to 50% across-the-board reductions at all facilities. The current study builds on the methodology of Nobel et al. (9) by examining: (i) Changes in the above indices when emissions are varied temporally instead of held constant throughout the day. (ii) Changes in the 24 thresh and 8 thresh indices, respectively, when the threshold ozone concentration is different from 85 ppbv. (iii) Population-based indices for children, the elderly, and all age groups, respectively. The methodology for each of these analyses is described below. Time-Varying Emissions. Four facilities were selected to examine the impact of time-varying emissions. The facility locations are shown in Figure 1. The facilities were selected in order to examine a range of scenarios, including a mix of rural and urban locations, high and low biogenic emissions (12), and relatively high and low impact indices based on emissions that were constant throughout the day (9). Table 1 describes the facility modeling id, daily emissions, and general location of each of the four facilities. The original CAMx modeling emissions inventory input files for the study conducted by Nobel et al. (9) specified constant emission rates for most elevated sources. Therefore, the hourly emissions of the facilities listed in Table 1 were equal throughout the day, with the daily total corresponding to the amount listed in the NOx emissions column. Nobel et al. (9) then calculated the 24 net, 8 net, 24 thresh, and 8 thresh indices described above for each facility. New sensitivity analyses using CAMx are conducted here for the four point sources in the current study to examine the effects of temporal variations in emissions. A modified emissions profile is applied to the targeted facilities to model the effect of temporal variations in emissions on ozone productivity. This temporal emission profile more accurately represented some facilities (e.g., peaking load power plants) whose production and, consequently, emissions varied throughout the day. Table 2 shows the hourly emission profile and associated emission factors that are applied to the four facilities. The temporal emission profile used for this analysis is based on power plant summer demand estimates. The same emission profile is used for all four facilities. A more detailed analysis of a specific source with a different temporal
TABLE 2. Emission Factors Used for Temporal Analysisa hour
emission factor
hour
emission factor
hour
emission factor
1 2 3 4 5 6 7 8
0.666667 0.666667 0.666667 0.666667 0.666667 0.666667 0.761905 0.857143
9 10 11 12 13 14 15 16
0.952381 1.047619 1.142857 1.238095 1.333333 1.333333 1.333333 1.333333
17 18 19 20 21 22 23 24
1.333333 1.333333 1.238095 1.142857 1.047619 0.952381 0.857143 0.761905
a
Three new indices are developed for the change in ozone exposure of the total population, children, and the elderly, respectively:
population impacth ) {
∑
all grid cells in region
[(base ozone concnh ozone concn from 50% NOx reduction at facilityh) × population group in grid cell]}/ [no. of grid cells in region × (ton of NOx/day)reduction] (3)
Emissions/24-h average.
emission profile could also be conducted using different factors and the same modeling methodology. After the modified, time-varying emission input files are created, they are input into the CAMx model to produce a new base case (with no emission reductions). The new base case includes the same net emissions as the time invariant emission base case reported by Nobel et al. (9) but yields different predictions in ozone concentrations because of the difference in the temporal profile of NOx emissions. Sensitivity analyses are then conducted to calculate the ozone productivity of each of the four facilities with the modified emission profiles. In each sensitivity analysis, emissions from a single facility are reduced by 50%, and a constant percentage reduction was employed at all times of the day. The CAMx model output is used to calculate 24 net, 8 net, 24 thresh, and 8 thresh impact indices for the four facilities with timevarying emissions, and these are compared to the indices calculated by Nobel et al. (9) based on constant emissions at the facilities. Threshold Ozone Concentration. In the previous study by Nobel et al. (9), the 24 thresh and 8 thresh indices were based on the change in ozone only in those regions initially with ozone concentration over 85 ppbv. The current study examines the impacts of changing the threshold ozone concentration on trading benefits. Impact indices for threshold concentrations from 80 to 120 ppbv in 5 ppbv increments, respectively, are generated for each facility. Impact indices are averaged over 24 (daily) and 8 h (1100-1900), respectively, to generate a single facility index for each threshold mixing ratio and averaging period. The Monte Carlo analysis described above is then applied to compare the trading impacts for each threshold index with the impacts from 50% across-the-board reductions at all facilities. Population-Based Indices. The ultimate objective of our national air quality standards is to protect public health. Because ozone formation is strongly influenced by both where and when precursor emissions occur, NOx emissions trading programs have the potential to increase or decrease human exposure. Although individual susceptibility to ozone varies, asthmatics and other individuals with respiratory illnesses, athletes, adults who work outdoors, and children (who typically spend more time outdoors and have higher ventilation rates than adults) are groups at risk (13). To examine how consideration of human exposure influences trading strategies, new impact indices that incorporate population densities are developed for the constant emission base case. County-level total population density estimates were obtained for eastern Texas for 1997 (14). Population data for children (ages 5 through 17 years) and the elderly (ages 65 years and older) were also obtained for the region from the 1990 U.S. Census. Population densities and facilities in eastern Texas are shown in Figure 2.
where population group in grid cell ) total, children, or the elderly, respectively. Impact indices are averaged over 24 h (daily) to generate a single facility index for each population group. Populationbased indices are compared with the 24 net ozone index for each facility to determine if rankings (i.e., designation as high or low impact) change based on the selected index. A Monte Carlo analysis is applied according to the methodology described above and used to evaluate potential trading impacts. Trading benefits are compared to benefits from 50% across-the-board reductions at all facilities. Differences in trading benefits between population-based indices and the 24 net ozone index are compared in the context of implications for trading program strategies.
Results and Discussion Time-Varying Emissions. This section presents a comparison of the ozone productivity of constant and time-varying emissions at four point sources in eastern Texas. Impact indices from constant and time-varying emissions at each point source are compared. Figure 3 shows the four ozone impact indices calculated for each facility for both the constant and time-varying emission profiles averaged over the 11 modeling days. Time-varying emission indices are slightly but consistently lower than the constant emission indices. Differences between constant and time-varying emissions for each of the individual facilities are small as compared to differences between facilities. The results presented in Figure 3 show that the impacts of varying the temporal profiles of emissions are small relative to the spatial variability between facilities based on ozone impact indices averaged over space and time. However, when facilities emit different amounts of NOx during different hours of the day, one would expect that the spatial distribution of ozone would be different than when facilities’ emissions are constant throughout the day because of variations in meteorological conditions and emissions by neighboring sources. Figure 4 shows the maximum and minimum differences between ozone concentrations in the constant emission base case and ozone concentrations in the time-varying emission base case over the 11 modeling days. Because the only difference between the constant emission and time-varying base cases is the modified temporal distributions of emissions at the four facilities (total NOx emissions are equal), the changes in predicted ozone between the two scenarios are due to the change in when the emissions occurred. Figure 4 demonstrates that for equal net emissions, constant emissions result in predicted ozone levels that ranged from 10 ppbv higher to 9 ppbv lower over the 11 modeling days than predicted ozone levels due to time-varying emissions. Both lower and higher predicted ozone levels occur in the same regions (in the vicinity of the modified point sources), but predicted ozone levels for constant emissions are higher VOL. 36, NO. 16, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Densities of (a) total population, (b) children (ages 5-17 yr); and (c) the elderly (ages 65+) in eastern Texas. NOx emissions of facilities are also shown.
FIGURE 3. Impact indices averaged temporally over 11 modeling days and spatially over eastern Texas for four sources with constant and time-varying (labeled as temp) emission profiles, respectively. Four ozone impact indices (24 net, 24 thresh, 8 net, 8 thresh) are shown. and more widespread. The affected region is large, in part due to the fact that Figure 4 reflects the changes seen during different meteorological conditions on the 11 modeling days (i.e., not all regions are affected on each day). Note that some areas show high values for both maximum and minimum values since at certain times of day the constant emissions base case leads to higher values while at other times of day leads to lower values than the time-varying emission base case. The data shown in Figure 4 are an important complement to the data shown in Figure 3. Figure 3 shows that the 3468
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differences in ozone impacts between constant and timevarying emissions are small when averaged over time and space when compared to other sources of variability (meteorology and difference in location of facilities). In contrast, Figure 4 shows that difference between constant and timevarying emissions at a single location may be large. These data demonstrate that changing a facility’s emissions from constant to time-varying can have a large impact on ozone concentrations locally. Although impact indices integrated over time and space do not depend strongly on the temporal profile of the emissions, policy-makers should be aware of
become more sensitive to the local availability of VOC emissions and meteorological conditions. For example, the percentage of trading scenarios that result in greater than a 10% or 25% variation from across-the-board impacts under an 80 ppbv threshold are 66% and 20%, respectively. Under a 120 ppbv threshold, the percentages became 79% and 43%, respectively. The results of the current study and the previous study by Nobel et al. (9) indicate that the air quality benefits of NOx trading programs can be variable because ozone formation is a strong, nonlinear function of local precursor emissions. The variability in trading benefits relative to acrossthe-board reductions increases considerably when unrestricted trading programs are evaluated in the context of reducing very localized areas of high ozone versus concentrations more typical of background levels in a region such as eastern Texas. Population-Based Indices. Population-based metrics can be used to evaluate whether emission reduction strategies effectively reduce both peak ozone concentrations and population exposure. Although they do not account for an individual’s personal exposure, population-based impact indices indicate where the exposure potential is high. These indices have important implications for emissions trading programs because benefits, and hence exposure, may be nonuniform across a region.
FIGURE 4. Maximum positive and negative (minimum) differences in ozone mixing ratios between the constant emission base case and the time-varying emission base case over the 11 modeling days. The maximum change in ozone shows where constant emissions result in higher ozone levels than time-varying emissions; the minimum change shows where constant emissions result in lower ozone mixing ratios than time-varying emissions. the potential for localized areas of high ozone if trades occur between facilities with different temporal emissions patterns. While these results could have been expected without the detailed modeling presented in this work, this modeling is the first attempt to confirm and quantify these effects. Threshold Ozone Concentration. Threshold indices coupled with Monte Carlo analysis can provide a measure of the relative benefits of trading versus conventional acrossthe-board reductions in areas that experience high ozone concentrations. Figure 5 shows the average of daily peak ozone concentrations over the 11 modeling days across eastern Texas and indicates areas that experience high ozone concentrations. Figure 6 shows the distribution of trading impacts based on systematically varying threshold ozone concentrations. The results are presented for the 24 net index, but the same trends are also observed for the 8 net index. As the threshold ozone concentration increases, the likelihood that the impacts of trading deviated by 10% or 25% from the impacts of across-the-board reductions also increase. With increasing threshold concentrations, impacts
Variability in Regional Ranking between Indices. To facilitate comparison between population-based indices and the 24 net ozone index, results are evaluated in the form of rankings, e.g., the facility with the highest impact for a given metric is assigned a rank of 1, the second highest a rank of 2, etc. Variability among the facilities ranked in the top 10 for any given index is shown in Figure 7. Variability in impacts among population subgroups is lowest between the total population and children and slightly higher between the total population and the elderly. The relative rankings between population-based indices reflected population distribution patterns in the state. The ranking of facilities in central Texas and Austin tend to drop between the total population and the elderly population indices, reflecting the concentration of university students and young adults and families. In contrast, the relative impacts on the elderly population tend to increase in the Longview/Tyler/Marshall area in east Texas. Differences between rankings for population subgroups are much smaller than differences between population-based indices and the 24 net ozone index. In general, facilities with a high impact based on the 24 net ozone index also have a high impact based on the total population index, i.e., the difference in ranking between the two indices is at most 2. However, approximately 10 facilities ranked in the top 20 of either index changed their rankings by at least five places. Several of the state’s large facilities are located near the Longview/Tyler/Marshall area in east Texas. Although NOx emissions from these facilities coupled with the high biogenic VOC emissions in the region lead to high ozone productivities, the area is not as densely populated as the major metropolitan areas of Houston, Dallas, Austin, or San Antonio. Consequently, many of these facilities that were ranked as “high impact” according to the 24 net ozone index are ranked lower according to the total population index. Certain facilities in more densely populated areas, such as Dallas, Houston, and central Texas but with lower ozone productivities, are ranked higher based on the total population index. The results indicate that changing metrics can make a considerable difference in how facility impacts are evaluated in the contexts of improving air quality and reducing exposure. Perhaps more significantly, facilities that may be considered high impact based on one type of metric may not be considered high impact based on another type of metric. Differences between metrics for population subgroups are VOL. 36, NO. 16, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 5. Average peak ozone concentrations over the 11 modeling days across eastern Texas. Several urban areas that are classified as either ozone nonattainment or near-nonattainment areas are indicated for reference purposes.
FIGURE 6. Comparison of trading benefit distributions for the 24 thresh index with different threshold ozone concentrations. less than differences between population-based metrics and an ozone metric. Variation in Trading Benefits between Indices. The likelihood that population-based impacts from trading differ from the impacts of across-the-board reductions is examined using Monte Carlo analysis. Analyses are conducted for the total population-based index and compared with the results of Nobel et al. (9) for the 24 net ozone index. A probability distribution of impacts is constructed for each index by accumulating the Monte Carlo simulation results over the 11 modeling days, i.e., 5000 per day or 55 000 total scenarios. To allow direct comparison of benefits between the two different indices, trading benefits are normalized by the average across-the-board reduction benefit for each index. Figure 8 shows the trading benefits for each index relative to across-the-board reductions. Table 3 presents the likelihood that trades yield 3470
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greater than a given percent change from across-the-board reductions. The direction of trading impacts (positive or negative) based on the ozone index tends to agree with the direction of trading impacts based on the total population density. The correlation coefficient between trading benefits for the two indices is 0.82. However, the magnitude of the variability in benefits based on population is larger than that based on ozone concentration only. For example, there is at least a 40% chance that trading impacts based on total population will deviate from the impacts of across-the-board reductions by at least 50%. In contrast, there is less than a 1% chance that trading impacts based on ozone concentration only will deviate from the impacts of across-the-board reductions by at least 50%.
FIGURE 7. Differences in rankings between the 24 net ozone index and the population-based indices for facilities ranked in the top 10 for any given index in eastern Texas over the 11 modeling days.
FIGURE 8. Comparison of the variations of benefits due to trading using the total population index and 24 net ozone index.
TABLE 3. Percentage of Trading Scenarios That Deviated from Daily Across-the-Board Impacts for Total Population Index and 24 Net Ozone Index % greater than across-the-board benefit index
10
25
50
100
24 net total population
52.9 87.0
12.9 67.9
0.3 39.5
0 6.7
The primary reason for the increased variability of trading based on total population exposure can be ascertained from Figure 9. Population-based indices are highly dependent on the proximity of a facility’s impact to densely populated areas. For example, certain sources in urban areas such as Dallas or Houston have a negligible impact based on the ozone index when averaged over the region. However, these same sources have a large negative
impact based on the total population index because their impacts occur within a densely population region that experiences NOx disbenefits. An example of this behavior is shown in Figure 10 for a facility in Houston. Reducing NOx emissions by 50% at HGAc38r31 leads to NOx disbenefits in certain grid cells. Because these NOx disbenefits are located within a densely populated area (refer to Figure 2a), the overall impacts from this facility using a population-based index are negative while the impacts on ozone concentration are slightly positive. Implications for Trading Strategies. The results of the current study as well as the study of Nobel et al. (9) indicate that the potential air quality and public health benefits of emission trading programs can be variable depending upon local concentrations of ozone precursors and meteorological conditions. A primary challenge of such programs is to facilitate improvements in air quality and public health that VOL. 36, NO. 16, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 9. Comparison of impacts based on total population densities with impacts based on the 24 net ozone index for 51 facilities in subregions of eastern Texas.
FIGURE 10. Predicted differences in ozone concentrations when NOx emissions from HGAc38r31 are reduced by 50% relative to the base case with no emissions reduction on July 3, 1996 [O3(base case) - O3(50% reduction of the facility)]. are perceived to be equitable among stakeholders within thetrading region. Using different impact indices allows factors such ozone reduction benefit, population exposure potential, and even economics to be incorporated into the analysis of the benefits of emission trading. The two studies demonstrate that different air quality benefit metrics based on ozone concentration exhibit very similar behaviors, and likewise, different air quality benefit metrics based on population densities have similar behaviors. In contrast, the population and concentration-based metrics exhibit distinctly different behaviors. For example, Nobel et al. (9) found that variability in trading impacts from acrossthe-board impacts were independent of the index used to calculate the ozone reduction benefit. Similarly, the current study found little variability in facility rankings based on different population subgroups. In contrast, Figures 8 and 9 show that the variability both in facility rankings by impact index and in trading are significantly different when the analysis is done using ozone concentration alone versus population densities. Many facilities that have a high ranking or impact based on the ozone concentration metric also have a high ranking based on the population metrics but not all. Analyzing trading programs in the context of multiple types 3472
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of impact indices and over different ozone episodes is needed to ensure that trades do not create or exacerbate concentration and exposure “hot spots”.
Literature Cited (1) Farrell, A.; Carter, R.; Raufer, R. Res. Energy Econ. 1999. 21, 103124. (2) Lents, J.; Leyden, P. J. Air Waste Manage. Assoc. 1996, 46, 195206. (3) Texas Natural Resources Conservation Commission.Control of Air Pollution from Nitrogen Compounds. Chapter 117; Rule Log No. 2000-011H-117-AI; 2000; http://www.tnrcc.state.tx.us/ oprd/rule•lib/proposals/pc00011h.pdf. (4) 76th Legislature of the State of Texas. Senate Bill No. 7, 1999, http://www.capitol.state.tx.us. (5) Burtraw, D.; Mansur, E. Environ. Sci. Technol. 1999, 33, 34893494. (6) Nichols, A. Accounting for Location in NOx Emission Trading Programs: Empirical Results from the OTAG Region. Prepared for the Electric Power Research Institutes, Cambridge, MA; National Economic Research Associates, Inc: February 1998; EPRI TR-110266. (7) ICF-Kaiser. OTAG Trading analysis with EPA/IPM. EPA: April 1996.
(8) Krupnick, A.; McConnell, V. Cost-effective NOx controls in the eastern US. Resources for the Future: April 2000 (www.rff.org/ disc•papers/abstracts/0018.htm). (9) Nobel, C. E.; McDonald-Buller, E. C.; Kimura, Y.; Allen, D. T. Environ. Sci. Technol. 2001, 35, 4397-4407. (10) ENVIRON International Corporation. Comprehensive Air Quality Model with Extensions (CAMx) Version 2.00 User’s Guide; December 1998 (www.camx.com). (11) McDonald-Buller, E. C.; Eusebi, A. A.; Russell, M. M.; Quigley, C.; Hill, A. N.; Allen, D. T. Impacts of Regional Control Strategies on Air Quality in Eastern and Central Texas. Vol. 2: Regional Photochemical Modeling and Sensitivity Studies; Prepared for the Texas Natural Resources Conservation Commission, August 1999.
(12) Wiedinmyer, C.; Strange, W.; Estes, M.; Yarwood, G.; Allen, D. Atmos. Environ. 2000, 34, 3419-3435. (13) Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society. Am. J. Respir. Crit. Care Med. 1996, 153, 3-50. (14) Environmental Systems Research Institute, Inc. ArcView GIS 3.2a; 1992-2000.
Received for review May 29, 2001. Revised manuscript received May 22, 2002. Accepted May 22, 2002. ES0110168
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