Plume-in-grid modeling of atmospheric mercury

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2008jd010580, 2008 Plume-in-grid modeling of atmospheric mercury Krish Vijayaraghavan, 1 Prakash Karamchandani, 1 Christian Seigneur, 1 Rochelle Balmori, 1 and Shu-Yun Chen 1 Received 10 June 2008; revised 17 September 2008; accepted 21 October 2008; published 30 December [1] An existing plume-in-grid model for ozone and particulate matter, which provides an explicit treatment of stack plumes embedded within a three-dimensional grid-based Eulerian air quality model, is extended to include a comprehensive treatment of mercury (Hg) processes. The model is applied to the continental United States to investigate the subgrid-scale effects associated with Hg emissions from large elevated point sources on atmospheric Hg concentrations and deposition. The top thirty Hg-emitting power plants in the U.S. were selected for explicit plume-in-grid treatment. Two new processes are included in the Hg chemical mechanism: the gas-phase adsorption of reactive gaseous mercury (RGM) on atmospheric particulate matter and the reduction of RGM to elemental Hg by sulfur dioxide. The plume-in-grid treatment results in improved performance for Hg wet deposition over a purely Eulerian grid-based model, partial correction of overpredictions of wet deposition downwind of coal-fired power plants in the northeastern U.S., and decreases of approximately 10% in simulated dry and wet deposition over large parts of the eastern U.S., with larger decreases near the plants selected for plume-in-grid treatment. On average, 23% of ambient RGM is modeled to adsorb on atmospheric particulate matter. Citation: Vijayaraghavan, K., P. Karamchandani, C. Seigneur, R. Balmori, and S.-Y. Chen (2008), Plume-in-grid modeling of atmospheric mercury, J. Geophys. Res., 113,, doi: /2008jd Introduction [2] The tendency of atmospheric mercury (Hg) emitted from anthropogenic and natural sources to deposit locally, regionally or globally depends on its chemical and physical form, its interactions with other atmospheric constituents, and prevailing atmospheric conditions [Lindberg et al., 2007]. The different forms of inorganic atmospheric Hg (elemental gaseous mercury (Hg 0 ), divalent reactive gaseous mercury (RGM) and particle-bound mercury (Hg p )) exhibit different deposition characteristics [Bullock et al., 2008]; for example, removal of RGM from the atmosphere occurs more rapidly than that of Hg 0 because RGM species are soluble and adsorb readily on most surfaces. The transport and transformations of the different components of Hg play a critical role in their eventual removal by wet and dry deposition. These transport and transformation processes are different in the plumes from elevated point sources than in the background atmosphere as discussed below. It is important, therefore, to represent these processes accurately in any modeling of atmospheric Hg deposition. [3] Several three-dimensional (3-D) Eulerian grid-based models have been applied to Hg deposition in North America [Pai et al., 1997; Xu et al., 2000; Bullock and Brehme, 2002; Lin and Tao, 2003; Seigneur et al., 2004; Lin 1 Air Quality Division, Atmospheric and Environmental Research, Inc., San Ramon, California, USA. Copyright 2008 by the American Geophysical Union /08/2008JD et al., 2006; Gbor et al., 2007; Sillman et al., 2007; Vijayaraghavan et al., 2007; Bullock et al., 2008; Selin and Jacob, 2008]. However, these traditional Eulerian gridbased models have several limitations when applied to the simulation of the fate of Hg and other emissions from elevated point sources such as power plant stacks. The artificial dilution of stack emissions in the entire volume of a grid cell leads to (1) lower concentrations of plume material, (2) unrealistically elevated concentrations upwind of the stack, (3) incorrect chemical reaction rates due to the misrepresentation of the plume chemical concentrations and turbulent diffusion, and (4) incorrect representation of the transport of the emitted chemicals [Pai et al., 2000a; Karamchandani et al., 2002]. Several plume-in-grid (PinG) models that treat the plume material at a subgrid-scale have been developed and applied over the years [e.g., Seigneur et al., 1983; Gillani, 1986; Sillman et al., 1990; Kumar and Russell, 1996; Godowitch, 2004], thereby eliminating some of the errors associated with the 3-D grid representation. However, they fail to represent the complex dispersion processes associated with the plume mixing into the background air because the plume dimensions are represented by simple geometric functions (columns, grids, ellipses, or Gaussian distributions). Physical phenomena such as the effect of wind shear on plume dispersion, the effect of plume overlaps (e.g., under conditions of reversal flow or merging of adjacent plumes), and the effect of atmospheric turbulence on chemical kinetics are not (or poorly) represented by such models. We have previously presented an improved PinG model that includes an advanced reactive 1of12

2 plume model embedded in a 3-D Eulerian gridded model to address the limitations discussed above with respect to ozone (O 3 ) and particulate matter (PM) formation from emissions of nitrogen oxides (NO x ), sulfur dioxide (SO 2 ) and PM from point sources [Karamchandani et al., 2002, 2006; Vijayaraghavan et al., 2006]. Wehavealsodescribed earlier the development and application of a multi-pollutant Eulerian grid-based model for Hg deposition [Vijayaraghavan et al., 2007]. We now combine these existing approaches and present here the development and application of a PinG model of atmospheric Hg. Use of a PinG formulation for Hg processes is expected to provide a more realistic representation of the behavior of reactive plumes in the atmosphere compared to a purely grid-based model. The objective of this work is to examine the impact of PinG treatment of some of the largest Hg emitting power plants on modeled atmospheric Hg deposition in the United States. [4] First, we discuss the relevance of explicit plume modeling to atmospheric Hg and the development of the PinG Hg model. We then apply the PinG model to assess the effect of explicit plume treatment for the thirty largest U.S. power plants (in terms of RGM emissions) on atmospheric Hg concentrations and deposition. 2. Plume-in-Grid Model Description 2.1. Relevance of Plume-in-Grid Modeling to Hg [5] There are two aspects of atmospheric processes that are relevant to an explicit plume treatment of major coalfired power plant plumes via PinG modeling: atmospheric dispersion of elevated stack plumes and the evolution of Hg speciation in power plant plumes. [6] Seigneur et al. [2006a] investigated the effect of the formulation of atmospheric dispersion in models of the atmospheric fate and transport of Hg. They concluded that a grid-based model tends to overestimate Hg deposition to areas that are commensurate with the grid spacing of the model compared with a plume model, because the gridbased model tends to overestimate the vertical dispersion of the elevated Hg emissions. In contrast, the plume model correctly retains the elevated Hg plume aloft, thereby limiting Hg dry deposition to downwind areas after plume touchdown. Implementation of a PinG model should provide a better representation of mercury deposition near emission sources, particularly for coarse modeling grids (e.g., those with 36 km horizontal resolution) [Lin et al., 2006]. Increasing the grid resolution (i.e., smaller grid spacing) can also help resolve point source emissions better because of less dilution of emitted material but will not address the problem of artificially enhanced vertical dispersion. Moreover, since point source plumes are of the order of tens to hundreds of meters while horizontal resolutions in grid-based models are of the order of kilometers, using an explicit plume model (or one embedded in a grid model) would provide a more accurate representation of these plumes rather than just decreasing the grid spacing. Removal of Hg by wet deposition, which is dominated by that of RGM, is influenced by whether the plume material comes into contact with clouds and/or precipitation and, thus, simulated wet deposition may increase or decrease with PinG treatment. [7] Hg in power plant plumes is a combination of Hg 0, RGM and Hg p ; the relative fractions depend on the type of coal burned and Hg control equipment at the plant. Hg 0 has the potential for long-range transport because of its low solubility and slow oxidation rate while RGM is widely accepted as the form of Hg most readily deposited from the atmosphere [Lindberg et al., 2007; Bullock et al., 2008]. However, there is some evidence that the RGM fraction in some power plant plumes is greater in the stack than in downwind ambient measurements [Edgerton et al., 2006; Prestbo et al., 2004]. This effect is not due to plume dilution because the analyses were done on the plume incremental concentrations above background levels. Two hypotheses have been proposed to explain the discrepancy in the Hg speciation: (1) stack measurements overestimate the RGM fraction and (2) some RGM reduction to Hg 0 occurs in the plume, possibly through gas-phase heterogeneous reduction by SO 2 [Lohman et al., 2006]. Seigneur et al. [2003] have investigated the effect of a lower RGM fraction in coal-fired power plant emissions and concluded that the overprediction of Hg wet deposition downwind of the Ohio Valley was reduced if a lower RGM fraction was used in the emissions. We investigate here the other hypothesis, namely, the effect of the proposed RGM reduction in power plant plumes. [8] In general, the use of PinG treatment will reduce simulated RGM dry deposition near the stack as the plume material will remain longer aloft and differences in Hg speciation due to RGM reduction will affect both dry and wet deposition of Hg because RGM is removed significantly faster than Hg 0 by both dry and wet processes Description of the Modeling System [9] The multi-pollutant PinG model applied in this study is the Advanced Modeling System for Transport, Emissions, Reactions and Deposition of Atmospheric Matter (AMSTERDAM) (see Appendix A for a list of model-related acronyms). AMSTERDAM is derived from a series of air quality models as described below. [10] The transport and gas-phase chemistry processes in AMSTERDAM are based on those in the U.S. Environmental Protection Agency s (EPA) Community Multiscale Air Quality model (CMAQ), a 3-D air quality model that simulates O 3 and other photochemical oxidants, PM and the deposition of pollutants such as acidic and nitrogenous compounds [Byun and Schere, 2006]. CMAQ-MADRID refers to a version of CMAQ where the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) [Zhang et al., 2004] is used instead of the standard CMAQ PM module to simulate the formation of PM. CMAQ- MADRID has been extended to include a comprehensive treatment of mercury processes [Vijayaraghavan et al., 2007]. [11] The primary PinG component is a reactive plume model, the Second-order Closure Integrated puff model with Chemistry (SCICHEM) [Karamchandani et al., 2000] embedded into the host model, CMAQ-MADRID, to provide advanced plume treatment (APT) for selected point sources. We refer to the resulting model as AMSTERDAM; the model can be applied with or without APT, i.e., in its standard gridbased formulation or with the PinG formulation. [12] In SCICHEM, plume transport and dispersion are simulated with a second-order closure approach to solve the turbulent diffusion equations [Sykes et al., 1993; Sykes and Henn, 1995]. The plume is represented by a myriad of 3-D 2of12

3 Figure 1. Modeling domain with the locations of the 30 largest (in terms of RGM emissions) coal-fired power plants in the USA (selected for explicit plume treatment). puffs that are advected and dispersed according to the local meteorological characteristics. Each puff has a Gaussian representation of the concentrations of emitted inert species. The overall plume, however, can have any spatial distribution of these concentrations, since it consists of a multitude of puffs. The effect of wind shear is addressed as individual puffs evolve according to their respective locations in an inhomogeneous velocity field. Puff contents are transferred to the 3-D grid system and the puff is no longer tracked when the horizontal size of the puff is comparable to the horizontal grid resolution. [13] The formulation of nonlinear chemical kinetics within the puff framework is described by Karamchandani et al. [2000] and extended now to include Hg chemistry. Chemical species concentrations in the puffs are treated as perturbations from the background concentrations. In this study, the Carbon-Bond Mechanism version IV (CBM-IV) of Gery et al. [1989] and the Euler Backward Iterative (EBI) solver of Hertel et al. [1993] are used to simulate gas-phase chemistry of NO x, volatile organic compounds (VOC), SO 2 and carbon monoxide (CO) in both SCICHEM and the host grid model for consistency. The Hg chemistry of Seigneur et al. [2006b] was augmented in this study with the gas-phase heterogeneous reduction of RGM by SO 2 and the adsorption of RGM to PM as a function of PM composition and size as described below. [14] Data collected at a ground-level monitoring site impacted by power plant plumes showed consistently lower RGM fractions than estimated for the stack emissions [Edgerton et al., 2006]. Lohman et al. [2006] used a reactive plume model [Seigneur et al., 1997] to simulate nine plume events among those identified by Edgerton et al. [2006] at the Yorkville monitoring site in Georgia, USA. Incorporating a reaction between RGM and SO 2 improved model performance in terms of speciated Hg concentrations at the Yorkville site. Scott et al. [2003] suggested that HgO could be reduced heterogeneously by SO 2. Though HgCl 2 is more likely to be the predominant RGM species in power plant plumes, rather than HgO [Senior et al., 2000], a similar reduction pathway involving HgCl 2 could be considered to occur heterogeneously in power plant plumes to explain the observed reduction of RGM to Hg 0. Lohman et al. derived an empirical average second-order reaction rate of molecule 1 cm 3 s 1 for the reduction of RGM by SO 2. We incorporated this reaction in AMSTERDAM (in both the host model, CMAQ-MADRID, and the plume model, SCICHEM) as a surrogate reaction for the potential RGM reduction in power plant plumes. [15] Currently, Hg models typically treat mercury adsorption only in the aqueous phase using an adsorption coefficient derived from available experimental data [Seigneur et al., 1998; Ryaboshapko et al., 2002]. It is essential to also consider the adsorption of gaseous Hg species to PM because gas/particle conversion also affects Hg deposition [Lindberg et al., 2007]. Rutter and Schauer [2007a, 2007b] reported results of laboratory work measuring the 3of12

4 Figure 2. (a, top left) Mean daily wet deposition flux of mercury in August September 2001 from the base (ng m 2 d 1 ) and (b, top right) APT simulations (ng m 2 d 1 ), (c, bottom left) change from the base to APT simulations [(APT Base)] (ng m 2 d 1 ), and (d, bottom right) relative change in wet deposition from the base to APT simulations [(APT Base)/Base * 100] (%). adsorption of RGM to atmospheric and synthetic particles as a function of temperature. Their experimental results suggest that surface area rather than PM mass controls the partitioning process. They reported three surfacearea adsorption coefficients for urban PM (collected in Milwaukee, WI, and Riverside, CA), ammonium sulfate and adipic acid, respectively. They expressed the surfacearea adsorption coefficient (K sa ) as follows: K sa ¼ Hg p;ads = RGM * A sp *PM where K sa is in m 3 m 2,Hg p,ads and RGM are in pg m 3, A sp is the specific surface area of ambient PM in m 2 mg 1 and PM is the ambient urban PM concentration in mg m 3. Rutter and Schauer [2007a, 2007b] also found that the K sa obtained for urban PM falls between that of ammonium sulfate (more RGM adsorption) and adipic acid (less RGM adsorption). Their laboratory experiments lead to the following value for K sa as a function of temperature (in K): ð1þ K sa ¼ 10 ð4250=t 10Þ ð2þ In the current study, we use equations (1) and (2) for the adsorption coefficient for urban PM to simulate RGM adsorption to PM in continental areas; for simplicity, we treat all non-sea-salt PM as urban PM. Thus we calculated the partitioning between the gas phase and the particulate phase as a function of non-sea salt PM and temperature in AMSTERDAM using equation (3) below. Here, Hg p,ads refers only to the adsorbed RGM, i.e., it does not include non-volatile primary Hg p. Hg p;ads =RGM ¼ 10 ð4250=t 10Þ *A sp *PM ð3þ Rutter and Schauer s experimental results also show a tenfold increase in adsorption of RGM to sodium chloride compared to ammonium sulfate and organic particulate compounds (a larger increase was observed for sodium nitrate); however, sea-salt emissions were not included in the current study and all RGM adsorption occurred on continental PM. [16] Inorganic non-hg PM simulated in AMSTERDAM includes sulfate, nitrate, ammonium, sodium, chloride and water. The model also tracks elemental carbon and primary organic aerosol. Secondary organic aerosol formation is simulated from the oxidation of both anthropogenic and biogenic precursors [Pun et al., 2005]. The particle size distribution is represented by a sectional distribution. Two size sections (fine and coarse) are used for this application. Particulate Hg consists of both primary (emitted) Hg p and that formed by the aqueous-phase and gas-phase adsorption of RGM on PM as discussed above. [17] The transport algorithms used in the host grid model in this study are the same as those used in our earlier CMAQ-MADRID application [Vijayaraghavan et al., 2007] with one exception: the vertical diffusion scheme is supple- 4of12

5 The mechanism of Walcek and Taylor [1986] is used to simulate aqueous-phase chemistry in clouds. Wet deposition includes rainout and washout that are calculated as described earlier [Karamchandani et al., 2006; Vijayaraghavan et al., 2007]. Figure 3. Mean daily precipitation (cm d 1 ) in August September 2001 from MM5. mented with the asymmetric convective model (ACM) for the convective boundary layer [Pleim and Chang, 1992]. [18] Dry deposition is simulated using a resistance transfer approach for gases and particles [Pleim et al., 1997]. Dry deposition characteristics of RGM are assumed to be similar to those of nitric acid because both gases have similar solubilities in water. The dry deposition of Hg 0 is not treated explicitly but is assumed to be balanced by the background emissions of mercury (i.e., natural emissions and emissions of previously deposited mercury) over North America. This common assumption [e.g., Seigneur et al., 2001; Vijayaraghavan et al., 2007] is justified by the fact that the atmospheric lifetime of Hg 0 (several months) is much larger than its residence time (a few days) within the modeling domain, therefore, its concentration within the domain is not very sensitive to its removal rate. For particles, the formulation of Venkatram and Pleim [1999] is used to account for gravitational settling. The dry deposition of Hg p and Hg p,ads is treated according to particle size. 3. Model Application and Impact of Plume-in-Grid Treatment 3.1. Model Application [19] AMSTERDAM was applied to the continental U.S. for August September The host grid model was based on version 4.6 of CMAQ. The modeling domain, shown in Figure 1, covers the continental U.S. and portions of southern Canada and northern Mexico. The grid has a horizontal spacing of 36 km, while the vertical grid structure consists of 14 layers from the surface to the tropopause with finer resolution near the surface (e.g., the surface layer is 35 m deep). The choice of horizontal grid spacing was influenced by the availability of model inputs at this resolution. [20] The meteorological fields, emissions inventory, and initial and boundary conditions for the air quality modeling simulations were provided by EPA. The meteorological fields were driven by the non-hydrostatic meteorological model, MM5 (the Mesoscale Modeling System Generation 5; Grell et al. [1995]), with four-dimensional data assimilation. Emissions files for non-hg species were obtained from EPA and based on the inventory used earlier in modeling in support of the Clean Air Interstate Rule [EPA, 2005a]. PM emissions were mapped from three size modes of CMAQ to two size sections in CMAQ-MADRID. The anthropogenic Hg emissions were obtained from EPA and represent EPA s 2001 Clean Air Mercury Rule (CAMR) inventory [EPA, 2005b], which was derived from the 1999 National Emissions Inventory (NEI) with revisions for waste incinerators. Hg p emissions were included only in the fine PM size section. As discussed above, natural Figure 4. Comparison of observed and simulated Hg wet deposition fluxes (ng m 2 ) in August September 2001 at (left) all MDN sites and (right) MDN sites in Pennsylvania (corresponding performance statistics are provided in Table 1). 5of12

6 Figure 5. (top) MDN measurement sites in Pennsylvania and (bottom) observed and simulated wet deposition flux of mercury at the sites active in August September 2001 (ng m 2 ) (source for MDN site map: note: PA19, PA47, and PA52 were not operational in 2001). emissions of Hg were not included because Hg 0 dry deposition is assumed to be balanced by background Hg emissions. Hg emissions from Mexico were not considered because of a lack of information in the CAMR inventory. Hg emissions in northern Mexico could be up to 60 Mg a 1 [Pai et al., 2000b]; ignoring Mexican emissions results in some underestimation of simulated mercury deposition in the southern U.S. The initial and lateral boundary concentrations were derived from simulation results from the global Goddard Earth Observing System - Chemistry (GEOS-Chem) model for non-hg [Park et al., 2004] and Hg species [Selin et al., 2007]. A spin-up period of 10 days was used to minimize the influence of the initial conditions. A spin-up time of 7 days is typically sufficient to eliminate the impact of initial conditions in this modeling domain [Pongprueksa et al., 2007]. [21] Thirty coal-fired power plants with the highest RGM emission rates in the continental U.S. were selected for advanced plume treatment in the AMSTERDAM simulation. Figure 1 shows the location of these plants in the modeling domain. The plant selection criterion was based on RGM because this form of Hg deposits more rapidly than Hg 0 and Hg p. The 30 plants selected have total Hg emissions of 12 Mg a 1 (57% of which is, on average, RGM, 39% Hg 0, and 4% Hg p ) in the 2001 CAMR inventory; these emissions represent approximately 27% of U.S. coal-fired power plant Hg emissions and approximately 10% of the total anthropogenic Hg inventory in the U.S. and Canada. The number of sources selected for PinG treatment represents a balance between scientific accuracy and computational efficiency. Multiple stacks in each plant treated with APT were combined into one stack each with an effective stack diameter, stack temperature, stack velocity, and stack height using conservation of flow, momentum and buoyancy principles. All other emission sources in the domain were treated with regular gridded modeling. In addition to the APT simulation described above, we also conducted a base simulation with just the grid model to determine the effect of using the PinG treatment. In this base simulation, the 30 point sources referred to above were treated like the remaining point sources (i.e., without APT); thus, regular gridded treatment was used for all sources Impact of Plume-in-Grid Treatment Mercury Deposition [22] Figure 2 presents the mean daily Hg wet deposition flux in August September 2001 from the base and APT simulations and the change and relative change in this flux 6of12

7 Table 1. Model Performance of Hg Wet Deposition in August September 2001 a Monitoring Locations MDN Hg Wet Deposition Flux (ng m 2 ) Simulated Hg Wet Deposition Flux (ng m 2 ) Normalized Mean Error (%) Normalized Mean Bias (%) Coefficient of Determination r 2 All MDN sites , , 47 2, , 0.43 MDN sites in PA , , 46 54, , 0.36 a Each pair has values in the following order: Base, APT. from the base to the APT simulations. The Hg wet deposition flux in the base simulation ranges typically from ng m 2 d 1 over the U.S. and Canada with isolated areas experiencing higher deposition. High fluxes in the spatial distribution of Hg wet deposition are driven by high precipitation (such as in Florida) and/or high local RGM emissions and high summertime oxidant concentrations (such as in the Northeast). Measured Hg concentrations in rain and corresponding wet deposition fluxes across the U.S. are higher in summer than in other seasons [Selin and Jacob, 2008] because of a combination of higher precipitation [Guentzel et al., 2001], enhanced oxidation of Hg 0 to RGM [Mason et al., 2000] and less efficient scavenging of mercury by snow than by rain [Glass and Sorensen, 1999]. Likewise, the simulated wet deposition fluxes shown in Figure 2 for August September are not representative of the entire year. The simulated wet deposition flux in August September 2001 was dominated by that in August because of much greater precipitation in that month than in September (not shown). The high wet deposition fluxes simulated over the Gulf of Mexico and the Atlantic are caused by greater precipitation over these areas. Figure 3 depicts the spatial distribution of mean daily precipitation input to the model in August September The precipitation fields simulated by MM5 are very high over the Gulf of Mexico and the Atlantic, and have spatial distributions over these waters similar to those shown by the wet deposition fluxes (see Figure 2). High wet deposition fluxes over the Gulf of Mexico and the Atlantic have also been simulated in other modeling studies [e.g., Pongprueksa et al., 2007; Bullock et al., 2008]. We are not aware of wet deposition measurements available over these waters to verify this phenomenon. [23] The base simulation shows high wet deposition fluxes over the Ohio River Valley and parts of Pennsylvania. Measured sulfate wet deposition is high in Pennsylvania, downwind of large SO 2 sources in the Ohio River Valley; however, measured Hg wet deposition fluxes and concentrations in precipitation do not exhibit this feature [National Atmospheric Deposition Program, 2008]. Simulated Hg wet Figure 6. (a, top left) Mean daily dry deposition flux of mercury in August September 2001 from the base (ng m 2 d 1 ) and (b, top right) APT simulations (ng m 2 d 1 ), (c, bottom left) change from the base to APT simulations [(APT Base)] (ng m 2 d 1 ), and (d, bottom right) relative change in dry deposition from the base to APT simulations [(APT Base)/Base * 100] (%). 7of12

8 Figure 7. (a, top left) Mean daily total deposition flux of mercury in August September 2001 from the base (ng m 2 d 1 ) and (b, top right) APT simulations (ng m 2 d 1 ), (c, bottom left) change from the base to APT simulations [(APT Base)] (ng m 2 d 1 ), and (d, bottom right) relative change in total deposition from the base to APT simulations [(APT Base)/Base * 100] (%). deposition fluxes are overestimated downwind of the large Hg sources in the Ohio River Valley. This Pennsylvania anomaly [Seigneur et al., 2003; Vijayaraghavan et al., 2007] is potentially due to one or more of the following reasons: a misrepresentation of the mercury reductionoxidation cycle, uncertainties in the dry deposition of RGM, and/or incorrect speciation of mercury emissions. We examine below whether the advanced plume treatment of major power plant emissions, which leads to greater RGM reduction (i.e., the first of the potential reasons listed above), leads to a resolution of this Pennsylvania anomaly. [24] As shown in Figure 2, the application of APT results in decreases in wet deposition of 1 4 ng m 2 d 1 (2 10%) in large parts of the eastern U.S. In the vicinity of the plants selected for APT, wet deposition decreases by 4 20 ng m 2 d 1 (10 30%) and by up to 45% in a few areas. These decreases are a consequence of the differing nature of Hg chemistry and dispersion in power plant plumes compared to the background air as discussed above. In those plumes, the enhanced reduction of RGM to Hg 0 results in lower RGM concentrations and, consequently, lower Hg wet deposition. The small increases of up to 2% in wet deposition in several parts of North America and increases of 2 7% in isolated areas in the APT simulation result mostly from the greater Hg 0 concentrations that slowly get oxidized to RGM farther downwind of the APT sources. [25] AMSTERDAM was evaluated with Hg wet deposition data from the Mercury Deposition Network (MDN) [National Atmospheric Deposition Program, 2008]. Predicted Hg wet deposition fluxes from the simulations with and without APT were compared with Hg wet deposition measurements during August September 2001 at 58 MDN monitoring stations in the U.S. and Canada. Figure 4 presents scatter plots of observed and simulated Hg wet deposition fluxes at all MDN sites and MDN sites in Pennsylvania for the August September 2001 time period. Table 1 shows the corresponding performance statistics. When considering MDN sites over the entire model domain, both the base and APT simulations exhibit comparable performance with an error of 47 49%, negligible bias and a coefficient of determination (r 2 ) of The overall similarity in model performance is primarily because the differences between the two simulations are minor at the majority of the monitoring stations in the modeling domain, because these stations are not influenced by the emissions from the 30 APT sources. However, the application of APT results in greater improvement in performance at the MDN sites in Pennsylvania (NMB improves from 54% to 36% and r 2 from 0.29 to 0.36). Figure 5 shows the locations of the MDN sites in PA and the observed and simulated wet deposition fluxes at those sites. Improvement in model performance with APT is seen particularly at the sites PA13 (in Cresson, Cambria County) and PA37 (in Holbrook, Greene County) which are directly downwind of some of the plants selected for PinG treatment. Thus APT results in a better representation of the fate of Hg in the power plant plumes and in a partial resolution of the Pennsylvania anomaly. 8of12

9 because of the application of APT are similar to those seen for wet deposition; however, the effect of APT is seen over a larger area in the case of dry deposition because it is not restricted to areas with precipitation. Dry deposition decreases by 1 4 ng m 2 d 1 (2 10%) in most of the eastern U.S. and by up to 41% in the immediate vicinity of the plants selected for APT because of enhanced reduction of RGM and the transport of Hg aloft in the plumes of those power plants. In parts of the U.S., Canada and Mexico farther from these plants, dry deposition increases by up to 2%; these increases are due to the same reasons as outlined in the case of wet deposition above (i.e., oxidation of greater concentrations of Hg 0 to RGM some of which is adsorbed Figure 8. Mean hourly surface concentrations of mercury in August September 2001 from the base simulation: (top) Hg 0 (ng m 3 ), (middle) RGM (pg m 3 ), and (bottom) Hg p (pg m 3 ). [26] Figure 6 shows the mean daily Hg dry deposition flux in August September 2001 from the base and APT simulations and the change and relative change in this flux from the base to the APT simulations. A similar set of plots is shown for the total (i.e., wet + dry) deposition flux in Figure 7. The simulated dry deposition flux in the base simulation ranges from 20 to 100 ng m 2 d 1 over most of the modeling domain with scattered areas experiencing higher dry deposition. In particular, dry deposition exceeds 140 ng m 2 d 1 in southern California; this is caused by the enhanced gas-phase oxidation of Hg 0 to RGM due to elevated concentrations of summertime oxidants in this region. The high Hg dry deposition in northern California corresponds to the Geysers geothermal area. The patterns in the change and relative change in simulated dry deposition Figure 9. Relative changes (%) in mean hourly surface air concentrations of (top) Hg 0, (middle) RGM, and (bottom) Hg p in August September 2001 from the base to APT simulations [(APT Base)/Base * 100]. 9of12

10 Figure 10. Fraction of RGM that undergoes gas-phase adsorption on particulate matter: (top) mean hourly surface value in August September 2001 from the base simulation and (bottom) relative change (%) from the base to APT simulations [(APT Base)/Base * 100]. to PM and the subsequent greater dry deposition of RGM and Hg p ). The total deposition flux reflects the combination of the individual wet and dry deposition fluxes described above. The total deposition flux ranges from 30 to 150 ng m 2 d 1 over most of the U.S. and decreases by 2 10% in several parts of the eastern U.S. and by up to 41% near the plants selected for APT in Alabama, Pennsylvania and Texas Mercury Concentrations [27] Figure 8 presents the ground-level mean concentrations of Hg 0, RGM and Hg p in August September 2001 from the base simulation. Hg 0 concentrations are typically ng/m 3 over the U.S. Higher concentrations seen in isolated areas in the eastern U.S. reflect high local Hg 0 emissions. The highest mercury concentrations in this time period are simulated in northern Nevada where Hg 0, RGM and Hg p surface-level concentrations exceed 5 ng/m 3, 150 pg/m 3 and 400 pg/m 3, respectively. These elevated levels occur because of very high mercury emissions from Nevada gold mines in the 2001 CAMR inventory. The Voluntary Mercury Reduction Program adopted by the Nevada gold mining companies in conjunction with the Nevada Division of Environmental Protection and EPA has resulted in about 80 percent decrease in mercury emissions in this area by 2005 ( vmrp-final.pdf), so the mercury air concentrations in this region are anticipated to be considerably lower now. The concentrations of Hg p show the strongest spatial variations across the U.S. because of their strong correlations with source areas and the contribution from RGM adsorption on PM. [28] Figure 9 illustrates the impact of advanced plume treatment on Hg concentrations. Shown are the relative changes in mean hourly surface-level concentrations of Hg 0, RGM and Hg p from the base to APT simulations. With explicit plume treatment of the 30 power plants, Hg 0 concentrations decrease by 0.1 3% in the immediate vicinity of the power plants selected for APT because more of the Hg is held aloft in the plumes of those plants. Hg 0 concentrations increase by 0.1 2% downwind due a combination of enhanced reduction of RGM to Hg 0 and plume touchdown farther downwind of the plants. Decreases of 0.1 3% in the central U.S. are due to the oxidation of greater concentrations of Hg 0 in the APT simulation. RGM concentrations decrease by 3 10% over large parts of the eastern U.S. and by 15 40% in the immediate vicinity of the power plants selected for APT. These decreases are due to a combination of enhanced reduction of RGM to Hg 0 and transport of Hg aloft in power plant plumes. Increases of 0.1 2% occur well away from the plants; these are due to the oxidation of greater concentrations of Hg 0 in the APT simulation. Changes in Hg p concentrations exhibit patterns similar to those of RGM in general because changes in RGM concentrations affect the adsorption of RGM on PM and because Hg p is also held aloft in the plumes. Figure 10 shows the fraction of RGM that undergoes gas-phase adsorption on PM and the relative change in this fraction from the base to APT simulations. The fraction ranges from 10 80% across the U.S. The fraction is higher in the upper Midwest, the west coast and isolated areas in the Southeast compared to the rest of the U.S. because of higher PM concentrations and/or colder temperatures, which both increase the adsorption of Hg to PM. The application of APT results in slight increases or decreases in this fraction depending on the relative magnitude of changes in RGM and adsorbed Hg p. APT typically results in lower simulated PM concentrations [Karamchandani et al., 2006] and, consequently, a slight decrease in the corresponding adsorbed Hg near most of the plants selected for APT. From the APT simulation, we predict that, on average across the modeling domain, 23% of RGM is bound to PM through gas-phase adsorption. 4. Summary and Conclusions [29] Traditional Eulerian gridded air quality models cannot resolve accurately the chemistry and transport of Hg in the plumes from elevated point sources such as power plant stacks. A new plume-in-grid model for Hg, AMSTERDAM, was developed and applied over a modeling domain that encompasses the continental U.S. and parts of southern Canada and northern Mexico. Two simulations, one with and the other without advanced plume treatment (i.e., PinG treatment), were conducted for August September Both simulations used the mercury chemistry of Seigneur et al. [2006b] with two additions. First, a reaction of RGM and SO 2 is used as a surrogate for the possible reduction of RGM to Hg(0) in power plant plumes. Second, the gas- 10 of 12

11 phase adsorption of RGM on PM is simulated for the first time using temperature-dependent adsorption coefficients. An advanced reactive plume model is used in the simulation with APT to explicitly treat plumes from the thirty largest power plant sources of RGM in the U.S. [30] The use of APT results in Hg wet and dry deposition decreases of 2 10% over large parts of the eastern U.S. and of up to approximately 40% near the plants selected for APT. The use of APT also results in partial resolution of the Pennsylvania anomaly, i.e., a decrease in the overestimation of Hg wet deposition in Pennsylvania downwind of the large Hg point sources of the Ohio Valley. On average across the modeling domain, 23% of ground-level atmospheric RGM is predicted to be adsorbed to PM via gasphase adsorption resulting in particle-bound Hg p,ads. [31] It is of interest to examine the relative importance of Hg deposition changes due to RGM reduction by SO 2 and RGM adsorption on PM. Test studies (not shown here) indicate that modeling the reduction of RGM has a stronger effect (by a factor of 2 or more) on Hg deposition than the adsorption of RGM in the immediate vicinity of power plants while adsorption is important in areas with high PM concentrations. The effect of the RGM reduction on Hg deposition is also greater with PinG treatment than without such treatment because of better representation of the high concentrations of SO 2 in power plant plumes in a PinG model. [32] A plume-in-grid treatment of major Hg point sources provides a more realistic representation of the fate and transport of the Hg emissions from those sources. Furthermore, such a PinG treatment leads in this particular simulation to improved model performance. 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