Environ. Sci. Technol. 2004, 38, 570-580
Geographic Sensitivity of Fine Particle Mass to Emissions of SO2 and NOx STEPHEN F. MUELLER,* ELIZABETH M. BAILEY, AND JIMMIE J. KELSOE Tennessee Valley Authority, P.O. Box 1010, Muscle Shoals, Alabama 35662-1010
An air quality model, URM-1ATM, was used to investigate tendencies in fine particle (PM2.5) species in response to changes in SO2 and NOx emissions in the eastern United States. The model employed the decoupled direct method (DDM) to estimate sensitivities without the need for multiple model runs for different emissions species and geographic regions. The baseline for sensitivities was emissions projected to 2010. Principal geographic regions investigated were east of the Mississippi River, although the contribution of a region to the immediate west of the river was also included in the study. Sensitivities to emissions changes from point sources (SO2 and NOx) and low-level sources (NOx) were computed. PM2.5 species examined were sulfate, organic carbon, and nitrate as well as total fine mass. Results for the midwest, mid-Atlantic, and southeast regions indicated that those regions affect their own aerosol levels the most. Aerosols in the northeast were most strongly linked to emissions from the midwest and mid-Atlantic regions. In general, midwest emissions had the most influence of any region on other regions. In addition, the southeast was relatively isolated, having the least influence outside itself and being least affected by neighboring regions. Sulfate was the species most sensitive to emission changes. Finally, the largest potential relative sensitivities of sulfate and organic aerosols, along with PM2.5 mass, to emissions changes were usually modeled to occur outside those areas computed to experience the highest aerosol levels.
Introduction Aerosols are under scrutiny for their role in human health and visibility. They originate from a variety of anthropogenic sources, either as primary emissions or as secondary aerosols formed from precursor gases. The objective of this study was to estimate the large-scale response of fine particle mass to changes in trace gases that are emitted predominantly by coal-fired electric-generating stations. The emissions of interest were SO2 and NOx. However, because the quantity of NOx emitted from other sources is equal to or greater than that emitted from power plants, impacts from other so-called “low-level” sources were also examined to put the particulate impacts of power plant NOx in perspective. The modeling results presented here are unique in that they represent a fairly large scale look at interregional impacts using an innovative technique applied to aerosols. In addition, this work takes the unique perspective of looking to the future * Corresponding author phone: (256)386-3643; fax: (256)3862499; e-mail:
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(2010). By that year, large point sources will have complied with the emission reduction requirements of Title IV of the Clean Air Act and the regional NOx emission reductions mandated by the U.S. Environmental Protection Agency (EPA) to reduce ozone levels in the eastern United States. Finally, the results are timely because of the growing interest in the effects of fine particles on health and regional haze. Fine particle (PM2.5) mass in the eastern United States is composed primarily of sulfate and carbonaceous particles (both black carbon and organic), with lesser amounts of soil minerals and nitrates (1, 2). Sulfates are predominantly of secondary origin formed from SO2. Black carbon is derived from combustion. Organic carbon (OC) can be of primary or secondary origin. In urban areas the OC portion of PM2.5 may equal or exceed that of sulfate. A detailed list of OC aerosols found in the environment is provided by Zheng et al. (3). Primary OC aerosols are emitted from sources such as diesel and aircraft engines, painting operations, and the cooking of meat. Secondary OC aerosols are formed from a variety of reactive organic gases such as terpenes (emitted by trees) and aromatic hydrocarbons, especially toluene and the xylene isomers, emitted by automobiles. Before methods to reduce ambient PM2.5 are considered, it is important to understand the gross characteristics of its behavior in response to emissions changes. This can only be done using atmospheric models that simulate the emission, transport, formation, and removal of PM2.5 species and their precursors. Several models have been developed to treat the atmosphere in a holistic sense by considering all trace gas and aerosol constituents simultaneously. The EPA has sponsored development of the Models-3 modeling system that includes the Community Multiscale Air Quality (CMAQ) model (4). Other similar models have also been developed and testing is ongoing (5). Traditionally, sensitivity modeling has been done by making multiple model simulations with different levels of emissions to identify the effects of emissions changes in different regions. This approach is called the “brute force” method. Other methods have been developed that offer alternative means for estimating such sensitivities. These include the coupled direct method (6), the Green function method (7), and the decoupled direct method (8). Other novel approaches, such as the stochastic response surface method (9), have been introduced and may be applied to complex air quality models in the future. The decoupled direct method (DDM) offers attractive advantages. It significantly reduces the number of model simulations, level of effort, and computer resources compared to the brute force approach. The DDM technique was described by Dunker (8) and implemented by Yang et al. (10) for ozone. Seefeld and Stockwell (11) used DDM to investigate the sensitivity of ozone and peroxyacetyl nitrate (PAN) to different chemical reaction rate parameterizations. The technique was recently implemented for aerosols (12). Efforts are underway to implement DDM in CMAQ (Talat Odman, personal communication). DDM is the approach used in the modeling reported here. The model, URM-1ATM, was recently updated by Boylan et al. (13) for an integrated air quality study performed for the Southern Appalachian Mountains Initiative (SAMI).
Modeling System System Components. The SAMI assessment focused on analyzing air quality-related impacts on sensitive ecosystems in the southern Appalachian Mountains of the southeastern United States. SAMI selected a system of models for simulating episodic levels of atmospheric pollutants. This 10.1021/es021016n Not subject to U.S. copyright. Publ. 2004 Am. Chem.Soc. Published on Web 12/06/2003
system was composed of the EMS-95 emissions modeling system (14), the RAMS meteorological model (15), and the URM-1ATM air quality model. The URM-1ATM model was updated to provide information on acidic deposition and visibility in addition to ozone. The original photochemical model was described by Odman and Russell (16), Kumar et al. (17), and Kumar and Russell (18). Boylan et al. (13) describe the current version of the model and its application to the SAMI air quality assessment. This assessment included an evaluation of model performance for ozone (urban and rural sites), aerosols, and wet deposition. Model performance was judged to be sufficient for the SAMI assessment, although performance was generally better for ozone than for aerosols. URM-1ATM simulates the formation of secondary aerosol species as well as the removal of species through dry and wet deposition processes. Gasphase reaction kinetics are simulated in URM-1ATM using the updated SAPRC chemical mechanism (19, 20). Inorganic aerosol thermodynamics are simulated on the basis of the ISORROPIA model (21). As described below, growth of organic particles is treated following the method of Pandis et al. (22, 23). Scavenging of gases and aerosols by clouds and precipitation is simulated using the Reactive Scavenging Module described by Berkowitz et al. (24). Each of the aerosoland wet-scavenging components selected to be in URM1ATM has been tested in other models, and no new modeling techniques are involved. URM-1ATM partitions aerosols into four size classes. Three classes include particles of -0.005.
Finally, several metrics were defined for this analysis representing each aerosol species to be defined. Metrics were designed to measure the sensitivity relationship between emissions and 24-h average concentrations of particulate species. The chosen metrics varied somewhat by species as summarized in Table 1. Nitrate sensitivities could not be treated the same way as the other species. Nitrate levels are usually so low that normalizing sensitivity by the concentration often results in enormous relative changes. Thus, only nitrate metrics were not normalized. Nitrate results are extremely sensitive to the presence of ammonia. Unfortunately, ammonia emissions and ambient ammonia levels are not well-known. Therefore, nitrate sensitivities are, at best, qualitative. The average relative sensitivities, ij∆ h kl , for PM2.5, PMOC, and PMSO4 were computed across all grid cells (with the exception of those cells excluded from the analysis as described in Supporting Information S1) within an impact region and provide a regional estimate of the general magnitude of the sensitivity to a particular emissions species. The extreme relative 24-h sensitivities, ij∆ ˆ kl , represent one grid cell in each impact region on any given day. These metrics identify the regions having the most sensitive grid cells to emissions changes. The average relative sensitivities for grid cells exceeding threshold values, ij∆ ˇ kl , provide insight into the extent to which sensitive regions occur in areas experiencing high computed levels of aerosols. Thresholds used to determine the so-called threshold sensitivities were 10 µg m-3 for PMSO4, 5 µg m-3 for PMOC, and 35 µg m-3 for PM2.5. These values were chosen because they were near the computed 75th percentile concentration values for the corresponding species. Only average and maximum nonrelative sensitivities, ijδ h kl and ijδˆ kl , were computed for PMNO3.
Results The general approach of this analysis involved computing a response metric for each combination of aerosol species, impact region, source region, and source type. Metrics were computed for all simulation days of the four episodes examined. Daily averaged values of ij∆ ˆ kl and ijδˆ kl for each source region are summarized in Table 3. This table presents 574
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FIGURE 3. Frequency distributions of computed sensitivities of PMSO4 in each impact region (designated by different bar patterns) responding to a 10% reduction in SO2 emissions from source regions MW (top) and SE (bottom). Results are a composite of all modeled days. results from the perspective of the source regions and their impacts on sensitivities anywhere within the four impact regions. For example, Table 3a shows that when examined across all four episodes, there is a predicted, domain-wide average maximum 24-h reduction in grid cell PMSO4 of 5.8% for a 10% reduction in SO2 emitted from region MW. Likewise, in Table 3b, a 10% reduction in LL-NOx in region SE was predicted to produce an average, domain-wide maximum reduction of 1% in PMOC and a corresponding average maximum reduction in PMNO3 of 0.10 µg m-3. Sensitivities in Table 3, parts b and c, are often quite different, and they are usually much smaller for PS-NOx sources. Source region SE appears to have the greatest potential for influencing PMSO4 and PM2.5. However, as is shown later, each source region tends to have the greatest influence on itself. PMOC is most influenced by LL-NOx. PMSO4 and PM2.5 appear to be most sensitive to SO2 emitted from source region SE. PMNO3 increases in response to SO2 reductions and decreases in response to LL-NOx and PS-NOx reductions. Nominally, PMNO3 in 2010 is expected to be similar to values listed in Table S1, or generally -1%. No sensitivities >0 were computed. Thus, PMSO4 shows a universal negative response to a small reduction in SO2. Results were quite different for source region SE. Impact regions MW, MA, and NE all had most sensitivities >-1% to SO2 h SO , which was near zero. This contrasts sharply with SE SE∆ 4 SO2 most frequently -2 to -3%. The differences in MW ∆ h SO and j 4 SE SO2 ∆ h frequency distributions indicate that the MW source j SO4 region has large impacts on all regions except SE, whereas,
conversely, source region SE has little impact outside itself. This result may be indicated more strongly here than otherwise because of the set of episodes modeled. Three of four episodes were in summer, a season often characterized by airmass stagnation over the southeast (27). Average sulfate sensitivities of all SO2 source regions are compared in Figure 4. Region NE primarily affects itself because of its predominant downwind location relative to the other impact regions. Region MA primarily affects itself and region NE. The pervasive influence of region MW sources contrasts sharply with the apparent isolation of sources in region SE. Region W sources primarily affect the two impact regions to the immediate east, but the sulfate sensitivities are all 35 µg m-3) source region
MW
MA
SE
NE
MW MA SE NE
-0.3 -0.0 -0.2 -0.0
-0.9 -1.8 -0.2 -0.1
-0.5 -0.3 -2.4 -0.5
-0.5 -1.1 -0.1 -0.0
a Signs of values are shown even though round-off indicates zero magnitude.
the fact that modeled sulfate levels were lower than in summer. More sensitivity of PMNO3 to NOx emissions is expected in winter when greatly reduced sulfate levels free up ammonia and lower temperatures favor the formation of NH4NO3. From this discussion it is evident that modeled sensitivity of total fine mass to SO2 reductions is almost totally dependent on the sensitivity of PMSO4. Computed sensitivities of threshold values of PM2.5 are summarized in Table 5. Only three combinations of source and impact regions were computed to have average threshold sensitivities of at least 1%. If the model results have any validity, it is evident that, by 2010, areas having trouble meeting a National Ambient Air Quality Standard for PM2.5 will not expect much benefit from SO2 emission reductions beyond those already required under Title IV of the Clean Air Act. Sensitivities to NOx Emissions. Emissions of NOx are not predominantly attributable to one source type as are emissions of SO2. NOx emissions are more evenly distributed among elevated point sources, motor vehicles, and numerous small stationary sources collectively referred to as “area” sources. Elevated point sources emit into the higher levels of the atmospheric boundary layer. The other sources emit into the lowest portion of the boundary layer. Consequently, PS-NOx and LL-NOx sources have different characteristics that may lead to differences in the way they affect aerosol formation. However, before sensitivity results can be examined, it is necessary to examine the influence of boundary conditions on NOx emissions sensitivities. This is because, as previously mentioned, there is considerable uncertainty in BCs for NOx and PAN. A test run of URM-1ATM was made with a 10% reduction in NOx BCs for the episode (April-May 1995) producing the highest levels of PMNO3. Aerosol sensitivities were compared with those from the original sensitivity run. Aerosol sensitivities to the boundary NOx were far less than those associated with a 10% change in emissions. PAN thermally decomposes, especially above 25 °C, and one of the products is NO2. Thus, PAN can serve as a source of additional NOx imported across the model boundary into the domain. The original SAMI modeling on which the current work was based derived NOx BCs by spatially interpolating the scarce, primarily urban, NOx observations that were available for each episode. Unfortunately, PAN data were not available for this interpolation. Inspection of the SAMI URM-1ATM values for PAN along the western domain boundary revealed that they were generally in the range of 3-4 ppb. By comparison, a CMAQ (4, 29) simulation of the entire continental United States for a July 1999 episode produced maximum afternoon boundary layer PAN values of ∼0.4 ppb for the same geographic region. These latter results are consistent with observations made in suburban Atlanta, GA, outside the influence of the primary urban plume (30). Therefore, the SAMI BC PAN values appear to be too high by nearly a factor of 10, and a second experimental model simulation was made with PAN BCs reduced by 90%. VOL. 38, NO. 2, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 8. Average (circles) and range (vertical lines) of the maximum sensitivity metric for PMOC responding to a 10% reduction in NOx from LL-NOx and PS-NOx. Impact regions are labeled above the bottom axis. Averages and ranges were determined across all source regions. Positive PMOC sensitivities occurred only 5% of the time for LL-NOx sources and
