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Development of a processor in BEIS3 for estimating vegetative mercury emission in the continental United States
ARTICLE IN PRESS
Atmospheric Environment 39 (2005) 7529–7540 www.elsevier.com/locate/atmosenv
Development of a processor in BEIS3 for estimating vegetative mercury emission in the continental United States Che-Jen Lina,, Steve E. Lindbergb, Thomas C. Hoc, Carey Jangd a
Department of Civil Engineering, Lamar University, Beaumont, TX 77710, USA Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA c Department of Chemical Engineering, Lamar University, Beaumont, TX 77710, USA d Office of Air Quality Planning and Standards, USEPA, Research Triangle Park, NC 27711, USA b
Received 7 December 2004; received in revised form 15 April 2005; accepted 15 April 2005
Abstract We have developed a regression-based processor for estimating vegetative mercury emission within the framework of Biogenic Emission Inventory System Version 3.11 (BEIS3 V3.11). In this development, we incorporated the 230 categories of USGS landcover data to generate the vegetation-specific mercury emission in a 36-km Lambert Conformal model grid covering the continental United States (CONUS). The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for selected tree species, wetland and water, or assumed for the tree species without available flux data. Annual simulations using the 2001 USEPA MM5 data were performed to investigate the seasonal emission variation. From our sensitivity analysis using three sets of emission factors, we estimated that the vegetative mercury emission in the CONUS domain ranges from a lower limit of 31 ton yr1 to an upper limit of 140 ton yr1, with the best estimate at 44 ton yr1. The modeled vegetative emission was mainly contributed from southeast US. Using the best estimate data, it is shown that mercury emission from vegetation is comparable to that from anthropogenic sources in summer (nearly half of the total emission). However, the vegetative emission decreases greatly in winter, leaving anthropogenic sources as the major emission source (490% in winter months). Modeling assessment indicates that including vegetative emission (44 ton yr1) can force an increase of ambient mercury concentration of up to 0.2 ng m3 in summer midday, but has little impact on dry deposition of mercury. Additional emission factors can be implemented in the model once further mercury flux data become available. r 2005 Elsevier Ltd. All rights reserved. Keywords: Mercury emission; Natural source; Anthropogenic source; Emission inventory estimates; BEIS3
1. Introduction Emission inventory (EI) is one of the required input fields for comprehensive chemical-transport Corresponding author.
E-mail address: [email protected] (C.-J. Lin). 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.04.044
modeling of atmospheric mercury. Anthropogenic emissions, with detailed emission speciation of gaseous elemental mercury (GEM), reactive divalent mercury (RGM) and particulate mercury (PHg), have been estimated and processed with relatively low uncertainty for atmospheric mercury modeling in the US (Seigneur et al., 2001, 2003,
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2004). However, mercury emission from vegetation, a major contributor of natural emission, has not been treated rigorously in previous modeling efforts. The emission contribution from this natural source was either neglected (e.g., Bullock and Brehme, 2002), or estimated as certain fraction (30–50%) of ‘‘re-emission’’ from mercury deposition calculated in the models (e.g., Bergan et al., 1999; Seigneur et al., 2001, 2004). To reduce mercury emission input uncertainty, it is necessary to develop new model components to estimate the EI with proper temporal variation dynamics from vegetative sources. It is commonly accepted in mercury research community that mercury emission from vegetation is caused by the deposition of mobilized mercury by human activities, followed by the uptake and transpiration of elemental gaseous mercury by vegetations (Bishop et al., 1998; Lindberg et al., 2002, Obrist et al., 2005). Many studies have indicated that the vegetative emission of mercury may dominate the mercury emission from anthropogenic sources, and there is a need to re-assess this diffused emission contribution (Lindberg, 1996; Lindberg et al., 1998; Gustin and Lindberg, 2000; Zehner and Gustin, 2002; Jaffe et al., 2005). Part of the need is due to the large uncertainty associated with the scaling up of mercury flux measurement over vegetation. This uncertainty increases the difficulty in assessing the global cycling of mercury and in incorporating vegetative emission in atmospheric mercury models. Elemental mercury emission from vegetation exhibits a strong diurnal variation, and is influenced by meteorological parameters such as temperature and solar radiation (Lindberg et al., 1998, 2002; Obrist et al., 2005). Because of its dependence on meteorological parameters, strong seasonal variation is also expected. Therefore, when considering the vegetative emission input into the atmosphere for chemical transport modeling, it is important to resolve the temporal and seasonal variation cycle. Recently, there have been research efforts in developing gridded natural emission processors to support atmospheric mercury simulation at regional scales. For example, Bash et al. (2004) developed a Mercury (Hg) Surface Interface Model (HgSIM) to estimate the vegetative emission in the Northeast US and Southeast Canada. However, to assess its seasonal variation and relative importance to anthropogenic emissions, a longer simulation period covering a larger domain is needed.
Biogenic Emission Inventory System Version 3 (BEIS3) is a flexible modeling system for estimating the EI of volatile organic compounds (VOCs) and NOx from biogenic sources (Lamb et al., 1993) in comprehensive air quality modeling. BEIS3 takes the inputs from landuse/landcover data and meteorological fields, and calculates the temporally and spatially resolved biogenic emission of VOCs and NOx. It is favorable to implement vegetative mercury emission processing in a modeling system such as BEIS3 because (1) the emission processing of mercury and biogenic VOCs shares many common routines and input data, and (2) the parallel emission processing of mercury and VOCs/NOx streamlines the EI preparation to meet the model input requirements. The objectives of this study are to develop a processor within the framework of BEIS3 for preparing mercury EI from vegetation, and to estimate its emission quantity in a continental United States (CONUS) domain. In this development, we employed a regression-based method to assimilate the model output to mercury flux measurement data. We also performed a sensitivity analysis to estimate the lower and upper limits of the annual emission. The modeled vegetative emission characteristics and quantity are presented and compared to anthropogenic mercury EI. The implications of including vegetative mercury emission in the modeling of atmospheric mercury are discussed. 2. Method 2.1. Models, domain and input data The version of BEIS3 used in this development is Version 3.11, a stand-along research version on UNIX/Linux platform. The model calculates the speciated, plant-species-specific emission of VOCs and NOx for the landuse/landcover types listed in the USGS Biogenic Emissions Landcover Data Version 3 (BELD3) datasets. The 1-km resolution, 230-category BELD3 data consist of 19 USGS landcover types, 17 agricultural use types and 194 forest tree species types. The BELD3 data, along with the mercury emission factors estimated from this work, were utilized to generate the normalized vegetation-specific mercury emission in a 36-km Lambert Conformal grid covering the entire CONUS. The domain has 14 vertical layers with a surface layer thickness of about 37 m. The surface
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Walcek et al. (2003). Plume rise was treated extensively by the algorithms in SMOKE. The temporal allocation of the anthropogenic emission followed the temporal profiles suggested by USEPA Clearinghouse for Inventories and Emission Factors (http://www.epa.gov/ttn/chief/). Spatial allocations were either treated by the latitude–longitude location of each emitting unit (point sources) or by emission surrogate prepared by Multimedia Integrated Modeling System (MIMS) spatial surrogate generator using the 2002 GIS data.
temperature and cloud-cover-corrected solar radiation from a meso-scale meteorological model (MM5 Version 3.6, Grell et al., 1994) were retrieved and converted into model-ready format using a Meteorology–Chemistry Interface Processor (MCIP2, Byun and Ching, 1999). The converted data were used for temperature and solar radiation corrections to calculate the diurnal variation of vegetative emission. To estimate the annual mercury emission from vegetation and its seasonal variation, we used the 2001 hourly meteorological fields in an annual simulation. Year 2001 is a good representative meteorological year in the CONUS domain with comprehensive air quality data availability. It was also selected by the USEPA for annual simulations of criterion air pollutants. To compare mercury emission quantity from vegetative and anthropogenic sources, annual anthropogenic EI processing was also performed using the same meteorological data. Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System Version 2.0 (the model and its documentation are available at www.cmascenter.org) was used for the EI preparation. In this simulation, the point and area source emissions of the 1999 National Emission Inventory Estimates (NEI99, Final Version 3) for air toxins were used. We downloaded the NEI99 data from ftp://ftp.epa.gov/EmisInventory/, retrieved the mercury emission, and processed the raw mercury EI data to a model-ready format (Inventory Data Analysis format) for SMOKE. The speciation of the anthropogenic emission followed the recommendations of Seigneur et al. (2001) and
2.2. Model implementation A regression-based model was employed to estimate the mercury emission flux from vegetation. The goal of the regression-based approach was to assimilate the modeled mercury emission intensity to the field observations of mercury flux. Since vegetative mercury emission is influenced by meteorological parameters (Lindberg et al., 2002; Obrist et al., 2005), the diurnal variation of the modeled GEM flux was simulated through the correction factors of solar irradiation and temperature at earth’s surface, i.e., F i ¼ F s;i C T C L ,
(1)
where Fi is the estimated mercury emission flux (ng m2 h1) for a given landuse type or a vegetation species i, Fs,i is the implemented emission factor (ng m2 h1) for species i (Table 1), CT is the temperature correction factor, and CL is the solar radiation correction factor. In the evaluation of CT
Table 1 The average daytime GEM emission intensities (ng m2 h1) used in model runs Categories
Case 1
Case 2
Case 3
W/S ratioa
Remarks/references
Water Wetlands Agriculture Maples (13)
1 40 1 4
1 40 4 4
1 40 4 37
0 0.5 Modelb 0
Pirrone et al. (2001) Lindberg et al. (2002) Assumed Hanson et al. (1995), Lindberg et al. (1998) Hanson et al. (1995), Lindberg et al. (1998)
Oaks (44)
5.3
5.3
37
0
Pines (39)
4
4
18
1
Other plants a
1
4
37
Assumed (Cases 1 and 2), Lindberg et al. (1998) b
Model
Assumed (Cases 1 and 2), Lindberg et al. (1998)
W/S ratio indicates the ratio of summertime to wintertime emission factors used in the model. The implemented Fs,i values for different seasons depend on the tree species. Same Fs,i values were used in different seasons for the evergreen plant species, while different Fs,i values were used for deciduous plant species according to a ratio defined in the BEIS3 emission factor table. b
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and CL, a formulation analogous to Guenther Algorithm (Guenther et al., 1995) for biogenic VOC emission was assumed since the emission– temperature–radiation relationship has not been yet available for mercury emission from vegetation: kL1 L C L ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , 1 þ kL2 L2 CT ¼
exp½kT1 ðT 30Þ=kT3 T , 1 þ exp½kT2 ðT 40Þ=kT3 T
(2)
(3)
where L is the solar radiation intensity at ground level (W s1) after cloud cover correction in MM5 (with canopy radiation transfer), and T is the temperature (1C) at 10 m above ground. The terms kL1, kL2, kT1, kT2, and kT3 are regression constants evaluated from the GEM evasion flux measurement data from Lindberg et al. (2002) using the least square technique. After the regression, the typical summer (July) ranges of CL and CT in the domain are 0–1.6 and 0.5–1.8; while the typical winter (January) ranges of CL and CT are 0–1.5 and 0.05–0.16, respectively. The emission factors (Fs,i) were estimated from the measured mercury evasion fluxes for various landuse/vegetation types using the average flux during daytime hours. It is either calculated from the measured mercury flux selected for the regression analysis, or assumed as the ‘‘average’’ emission intensity previously reported in the literature (e.g., Hanson et al., 1995; Lindberg et al., 1998, 2002; Pirrone et al., 2001). Since mercury emission flux was measured only for a limited number of vegetation species (e.g., Hanson et al., 1995; Lindberg et al., 1998, 2002), we grouped all the oaks (13 species), maples (44 species) and pines (39 species) in the BELD3 data categories, and used the same average daytime emission as Fs,i for the three vegetation groups (i.e., oaks, pines and maples). Based on the approach described above, several assumptions/simplifications were made for the scaling of the vegetative mercury emission in the model calculation. First, elemental gaseous mercury is treated as a VOC-like species, and shares similar emission characteristics of biogenic VOC emission in the model. Second, the vegetative mercury emission is only related to the plant types and meteorological parameters without detailed mechanistic consideration of the air-surface exchange process. Third, the obtained regression constants are assumed to be applicable to different vegetation types. We acknowledge that the processes involved in vegetative mercury
emission and in biogenic VOC emission are fundamentally different, and that several mechanistic models have been developed to describe the airsurface exchange of mercury (e.g., Xu et al., 1999; Zhang et al., 2002). However, in terms of assimilating the characteristics of vegetative mercury emission for Eulerian model applications, our test indicated that Eqs. (1)–(3) represent reasonable intensity and diurnal/seasonal trend of the emission. Due to the uncertainty associated with the reported vegetative mercury fluxes, we drove the model with three different sets of Fs,i with summer/winter emission ratios as shown in Table 1. In Case 1, the emission factors of the vegetation species without available mercury flux measurement data were set to a background level similar to soil emission (1.0 ng m2 h1, Lindberg et al., 1998). This case would represent the lower limit of the vegetative emission due to the much greater flux from vegetation compared to soil flux. In Case 2, we assume that the vegetation species other than oaks, pines and maples have similar emission flux intensity at 4 ng m2 h1, which would reasonably estimate the true emission intensity from these species. In Case 3, we use the average daytime flux reported in the earlier literatures for emission estimate (e.g., Lindberg, 1996; Lindberg et al., 1998). This case would give the upper limit estimate since more recent data suggested that earlier flux measurement may overestimate the emission flux from forest due to mercury contamination of the soils (Graydon et al., in review). To implement the above-mentioned regression algorithms, the source codes of BEIS3 V3.11 were modified to include GEM as one of the emitted species. The mercury emission factors shown in Table 1 were appended in the BEIS3 emission factor table for VOCs and NOx. Fig. 1 shows the data flow of our model implementation. The output from the model is temporally (hourly) and spatially resolved gridded GEM emission in netCDF format ready for applications in Eulerian-based chemical transport models such as CMAQ-Hg and CAMx-Hg. The data in the subsequent maps are expressed as the emission intensity (ng h1 m2) in each 36-km grid of the model domain. 3. Results and discussion 3.1. Verification of modeled emission flux with measurements Fig. 2 shows the diurnal emission cycle by the model estimate and by the mercury evasion flux
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Fig. 1. Data flow of simulating vegetative emission of mercury in BEIS3.
80 Mercury Flux (ng m-2 h-1)
measured in Florida wetland in June 1997 (Lindberg et al., 2002). As mentioned, the regression-based approach attempts to assimilate the measured emission flux intensity and daily variation. As shown in Fig. 2, the simulated diurnal cycle of GEM emission depicts the measured emission fluxes reasonably well. Similar implementation was also carried out for other tree species and landcover types. We used the modeled mercury flux intensity and diurnal variation to estimate the monthly and annual emission quantity from vegetative sources.
Measured
70
Modeled
60 50 40 30 20 10 0 0
3.2. Spatial and temporal distribution of modeled emission Figs. 3 and 4 show the typical summer (July) and winter (January) diurnal variation of the modeled GEM emission in the model domain. The results shown in Figs. 3 and 4 were obtained from the average hourly emission of the 31 days in July and January using the emission factors in Case 2 of Table 1. The diurnal patterns of Cases 1 and 3 are similar and therefore not shown. In both figures, the modeled GEM fluxes at hours 8, 12, 16 and 20 (all in Eastern Standard Time) are shown. In summer the simulated GEM emission starts from the east coast and gradually ‘‘migrates’’ to the west coast according to the change of cloud-corrected solar intensity and surface temperature (Fig. 3). The vegetative emission is mainly from the central US due to the high temperature and large coverage area of forest/agricultural landcover, with peak flux in the region ranging from 5 to 10 ng m2 h1. The strongest emission flux intensity occurs at Florida wetland, with a modeled flux of 36 ng m2 h1.
400
800
1200 1600 Hour of Day
2000
2400
Fig. 2. Comparison of the modeled vegetative mercury emission with the measured emission flux. The measured data are from a 5day field measurement in Florida wetland in June 1997 for the Everglades Nutrient Removal project (ENR) (Lindberg et al., 2002).
Similar diurnal variation is also observed in winter (Fig. 4). However, the emission intensity is much weaker and occurs only in the deep south of the domain. This is due to the smaller emission factors employed in the calculation (Table 1), and the much lower surface temperature and solar radiation in winter. The emission flux for the most part of the domain is below 0.5 ng m2 h1. Under these conditions, significant emission flux is observed only in Florida Peninsula and the Caribbean Islands. 3.3. Seasonal variation of modeled vegetative emission The seasonal variation of vegetative mercury emission was characterized by the modeled monthly
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Fig. 3. Typical modeled summer time diurnal variation (July) of the vegetative mercury emission at (a) 8:00 am, (b) 12 pm, (c) 4:00 pm and (d) 8:00 pm using the emission factors in Case 2. The figure represents the hourly average of the 31-day simulation in July 2001 (e.g., (a) represents the flux average of all 31-day fluxes at 8 am). All times are at eastern US time. It exhibits a strong dependence of the surface temperature and irradiation.
emission. The monthly GEM emission for the three sensitivity cases is shown in Fig. 5. As seen, the annual vegetative emission is dominated by the emission in June–August. In fact, about 60% of the annual emission is generated in the three summer months, and more than 90% of the annual vegetative emission comes from April to September emission. Summing up the monthly emission for the entire year, the annual vegetative emissions for the three sensitivity cases are 31 (Case 1), 44 (Case 2), and 140 (Case 3) tons, respectively (Table 2). The upper limit estimate (Case 3: 140 ton yr1) would account for nearly half of the total mercury released to the atmosphere in the US (more discussion in Section 3.4.) and agrees well with earlier estimates (Lindberg et al., 1998; Seigneur et al., 2004). The emission difference between Cases 1 and 2 is only 13 ton yr1. This is because the emission factors were not changed for wetland, pines, oaks, and maples for the two sensitivity cases. The pines, oaks and maples constitute a considerable fraction of the total forest cover according to the BELD3 datasets, especially in the south. Therefore, they contribute relatively larger emission compared to other tree
species due to the higher surface temperature and solar radiation. We feel that the emission quantity estimated in sensitivity Case 2 would better represent the mercury emission contribution from vegetative sources. Earlier flux data over forest may overestimate the emission flux due to the proximity of the field site to the a known mercury emission source which did result in elevated surface soil concentrations of mercury, and could also have biased the measured Hg gradients (Lindberg et al., 1998). Although this estimate (Case 2) was based on the assumption that all the vegetation species emit mercury at a similar flux intensity, there are limited laboratory and field data which indicate that this is the case unless the soils are enriched in mercury content (e.g., Hanson et al., 1995; Poissant and Casimir, 1998; Graydon et al., in review). Previous scaling estimates are to a large extent extrapolated from limited data availability and highly uncertain (e.g., United States Environmental Protection Agency (USEPA), 1997; Lindberg et al., 2004). Our empirical model-based scaling should better represent the mercury contribution from vegetative sources.
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Fig. 4. Typical modeled winter time diurnal variation (January) of the vegetative mercury emission at (a) 8:00 am, (b) 12 pm, (c) 4:00 pm and (d) 8:00 pm using the emission factors in Case 2. The figure represents the hourly average of the 31-day simulation in January 2001. All times are at eastern US time. The emission flux occurs only in the south and the intensity is much reduced compared to the summer flux.
35
Monthly Emission (Tons).
30
Case 1 Case 2
25
Case 3
20 15 10 5
Au gu st Se pt em b O er ct ob er N ov em be D r ec em be r
e
Ju ly
Ju n
Ja nu ar y Fe br ua ry M ar ch Ap ril M ay
0
Fig. 5. Seasonal variation of the modeled vegetative mercury emission in the CONUS domain for various sensitivity cases. It can be seen that the emission is dominated by summer time.
3.4. Comparison with anthropogenic mercury emission The modeled vegetative emission was compared to anthropogenic mercury emission in terms of
emission characteristics and quantity. Identical meteorological data were used for the vegetative and anthropogenic emission processing. The primary emission characteristic differences between the vegetative and anthropogenic mercury emission are:
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(1) vegetative emission exhibits strong diurnal and seasonal variations while anthropogenic emission does not, (2) vegetation emits primarily GEM while anthropogenic emission releases GEM, RGM and PHg depending on the source, fuel types and emission control devices in use, and (3) vegetative emission releases mercury to the surface layer of the model domain only while anthropogenic emission from point sources is subject to the plume rise depending on atmospheric stability. Compared to
Table 2 Comparison of mercury emission and speciation by anthropogenic and vegetative sources in the model domaina Anthropogenic
Case 1
Case 2
Case 3
GEM RGM PHg
78 50 14
31 0 0
44 0 0
140 0 0
Total
142
31
44
140
a
All values are in tons/year in the CONUS domain.
anthropogenic emission, the vegetative source is relatively weaker and more diffused, but covers a much broader region. Fig. 6 shows the spatial distribution of the total annual emission from vegetation and anthropogenic sources, which was generated from the sum of the hourly emission for the entire year. After summing the entire year’s data, the annual vegetative emission is mainly contributed by the southeastern region of the US (Fig. 6a–c) for all three sensitivity cases. The difference in the spatial feature in Figs. 6a–c is caused by the difference in their respective emission factors (Table 1). The anthropogenic emission, on the other hand, is dominated by the emission from the eastern US and the west coast regions (Fig. 6d). Considering emission speciation, anthropogenic sources emit 78 tons of GEM, 50 tons of RGM and 14 tons of PHg, respectively, in the continental US (Table 2). Fig. 7 shows the month-by-month mercury emission quantity and speciation by vegetative (Case 2) and anthropogenic sources. The monthly anthropogenic emissions are relatively constant
Fig. 6. Spatial distribution of the modeled vegetative mercury emission for various sensitivity cases and anthropogenic mercury emission: (a) Case 1, (b) Case 2, (c) Case 3 and (d) total anthropogenic emissions from USEPA NEI99 including point and area sources. Note that the emission quantity is expressed as ton/yr in each 36-km model grid and the color scale for (a) and (b) differs from that of (c) and (d).
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throughout the year. With the emission factors employed in Case 2, the mercury emissions from vegetation and from anthropogenic sources are comparable in summer months. For example, in the month of July, vegetative emission constitutes 47% of total mercury release into the atmosphere. However, in the winter time, anthropogenic sources dominate the mercury emission from vegetation (it contributes more than 95% of the total emission in the months of November– February) due to the smaller emission factors for the deciduous plant species in the model, and the much lower surface temperature and solar radiation. 3.5. Implications in atmospheric mercury simulation From our sensitivity analysis, we conclude that mercury emission from vegetation can constitute a significant fraction of the total mercury input to the atmosphere, and should be included in the EI processing of mercury, especially in warm months. Fig. 8 shows the mercury emission contribution from vegetative sources in various sensitivity cases. We estimate that vegetative emission contribute
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from a lower limit of 18% (Case 1) to an upper limit of 49% (Case 3) of the total annual mercury emission, with our best estimate at 24% (Case 2) in the domain. Considering that vegetation releases mercury to the surface layer only, and that anthropogenic emission can be diluted by plume rise, the contribution from vegetation in the surface layer is even greater, ranging from 57% to 86% in the model domain, with our best estimate at 66%. Due to the relatively small emission quantity of mercury compared to other air pollutants such as VOCs, NOx and PM, both anthropogenic and vegetative emissions are not likely to significantly modify the background concentration of mercury (1–3 ng m3) because of vertical mixing and dilution. Furthermore, since the speciation of vegetative mercury emission is dominated by GEM, it should not increase mercury deposition significantly unless under a highly oxidative condition in the atmosphere, due to the low deposition velocity of GEM (Lindberg et al., 1992, Xu et al., 1999). Contrasting to anthropogenic mercury emissions that can lead to important local deposition due to the presence of considerable RGM and PHg fraction (Fig. 8),
Fig. 7. Comparison of mercury emission quantity and speciation from anthropogenic and vegetative sources in the CONUS domain. The legends HG0, HGIIGAS and PHG represent the anthropogenic emission of GEM, RGM and pHg. The legend HG0-vegetation represents the vegetative emission estimate using the emission factors in Case 2.
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100%
Emission Contribution (%)
90% 80%
18% 24%
8% 49% 7%
70% 60%
57% 66%
29% 86%
27%
50%
5%
40%
4% 18%
20%
3%
15%
30% 45%
12% 42% 28%
23%
10%
1% 5%
19%
8%
0% Case 1
Case 2
Case 3
Total Emission Anthrop-GEM
Case 1
Case 2
Case 3
Surface Emission
Anthrop-RGM
Anthrop-PHg
Vegetative
Fig. 8. Contribution of annual mercury emission input to the model domain and to the surface layer of the domain in various sensitivity cases.
mercury emission from vegetation would affect the surface concentration of GEM only. In our testing of how this natural mercury emission affects the ambient concentration and deposition of atmospheric mercury using the same model domain, we found that the Case 2 emission can force an increase of surface layer GEM concentration up to 0.2 ng m3 during summer midday, but shows little impact on dry mercury deposition. Nevertheless, vegetative emission represents a potentially important mercury input to the atmosphere. 4. Conclusions In this study, we develop a regression-based vegetative mercury emission processor in the BEIS3 framework in a 36-km model domain covering the CONUS. The developed processor can be used for estimating vegetative mercury emission at high temporal and spatial resolutions, along with VOC and NOx emission processing from biogenic sources, to support the chemical transport modeling of atmospheric mercury. From our sensitivity analysis, we estimated that vegetative mercury emission ranges from a lower limit of 31 ton yr1 to an upper limit of 140 ton yr1, with the best estimate at 44 ton yr1 in the continental US. The vegetative emission is mainly contributed from the southeastern US, and exhibits strong diurnal and
seasonal variations. Compared to anthropogenic mercury emission using our best estimate (Case 2), this natural source can account for nearly half of total mercury release in summer months. Such emission input can increase the mercury concentration in the surface layer by up to 0.2 ng m3 in our 36-km domain grid. An uncertainty associated with our current estimate is that we assume many vegetation species have similar emission flux intensity due to the lack of comprehensive flux measurement data for the wide variety of vegetation species in the BELD datasets. Once additional mercury flux data become available, the data can be easily implemented as emission factors in the model. It should also be noted that the regression-based model in this work does not explicitly treat the mechanistic processes of vegetative mercury emission, which involves a series of plant physiological processes not well understood. There is clearly a research need to address the effect of soil types/conditions, soil/water mercury content, plant physiological processes, and other physical/chemical mechanism on mercury exchange between vegetations and the atmosphere before a sound science-based vegetative mercury emission processor can be developed. Nevertheless, the current model serves as a viable modeling tool for generating vegetative mercury input to atmospheric mercury models.
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Acknowledgements This work was supported in part by Texas Commission on Environmental Quality (TCEQ) under Contract no. 582-4-64582 and by Gulf Coast Hazardous Substance Research Center (GCHSRC) under the Contract no. 043LUB0855. The financial support of the sponsors is gratefully acknowledged. We want to thank Ms. Heng Yang for her assistance in setting up the modeling platform for this work. One coauthor (S.L.) acknowledges the Electric Power Research Institute for support at ORNL, which is managed by UT-Battelle for the US Department of Energy.
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Atmospheric Environment 39 (2005) 7529–7540 www.elsevier.com/locate/atmosenv
Development of a processor in BEIS3 for estimating vegetative mercury emission in the continental United States Che-Jen Lina,, Steve E. Lindbergb, Thomas C. Hoc, Carey Jangd a
Department of Civil Engineering, Lamar University, Beaumont, TX 77710, USA Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA c Department of Chemical Engineering, Lamar University, Beaumont, TX 77710, USA d Office of Air Quality Planning and Standards, USEPA, Research Triangle Park, NC 27711, USA b
Received 7 December 2004; received in revised form 15 April 2005; accepted 15 April 2005
Abstract We have developed a regression-based processor for estimating vegetative mercury emission within the framework of Biogenic Emission Inventory System Version 3.11 (BEIS3 V3.11). In this development, we incorporated the 230 categories of USGS landcover data to generate the vegetation-specific mercury emission in a 36-km Lambert Conformal model grid covering the continental United States (CONUS). The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for selected tree species, wetland and water, or assumed for the tree species without available flux data. Annual simulations using the 2001 USEPA MM5 data were performed to investigate the seasonal emission variation. From our sensitivity analysis using three sets of emission factors, we estimated that the vegetative mercury emission in the CONUS domain ranges from a lower limit of 31 ton yr1 to an upper limit of 140 ton yr1, with the best estimate at 44 ton yr1. The modeled vegetative emission was mainly contributed from southeast US. Using the best estimate data, it is shown that mercury emission from vegetation is comparable to that from anthropogenic sources in summer (nearly half of the total emission). However, the vegetative emission decreases greatly in winter, leaving anthropogenic sources as the major emission source (490% in winter months). Modeling assessment indicates that including vegetative emission (44 ton yr1) can force an increase of ambient mercury concentration of up to 0.2 ng m3 in summer midday, but has little impact on dry deposition of mercury. Additional emission factors can be implemented in the model once further mercury flux data become available. r 2005 Elsevier Ltd. All rights reserved. Keywords: Mercury emission; Natural source; Anthropogenic source; Emission inventory estimates; BEIS3
1. Introduction Emission inventory (EI) is one of the required input fields for comprehensive chemical-transport Corresponding author.
E-mail address: [email protected] (C.-J. Lin). 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.04.044
modeling of atmospheric mercury. Anthropogenic emissions, with detailed emission speciation of gaseous elemental mercury (GEM), reactive divalent mercury (RGM) and particulate mercury (PHg), have been estimated and processed with relatively low uncertainty for atmospheric mercury modeling in the US (Seigneur et al., 2001, 2003,
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2004). However, mercury emission from vegetation, a major contributor of natural emission, has not been treated rigorously in previous modeling efforts. The emission contribution from this natural source was either neglected (e.g., Bullock and Brehme, 2002), or estimated as certain fraction (30–50%) of ‘‘re-emission’’ from mercury deposition calculated in the models (e.g., Bergan et al., 1999; Seigneur et al., 2001, 2004). To reduce mercury emission input uncertainty, it is necessary to develop new model components to estimate the EI with proper temporal variation dynamics from vegetative sources. It is commonly accepted in mercury research community that mercury emission from vegetation is caused by the deposition of mobilized mercury by human activities, followed by the uptake and transpiration of elemental gaseous mercury by vegetations (Bishop et al., 1998; Lindberg et al., 2002, Obrist et al., 2005). Many studies have indicated that the vegetative emission of mercury may dominate the mercury emission from anthropogenic sources, and there is a need to re-assess this diffused emission contribution (Lindberg, 1996; Lindberg et al., 1998; Gustin and Lindberg, 2000; Zehner and Gustin, 2002; Jaffe et al., 2005). Part of the need is due to the large uncertainty associated with the scaling up of mercury flux measurement over vegetation. This uncertainty increases the difficulty in assessing the global cycling of mercury and in incorporating vegetative emission in atmospheric mercury models. Elemental mercury emission from vegetation exhibits a strong diurnal variation, and is influenced by meteorological parameters such as temperature and solar radiation (Lindberg et al., 1998, 2002; Obrist et al., 2005). Because of its dependence on meteorological parameters, strong seasonal variation is also expected. Therefore, when considering the vegetative emission input into the atmosphere for chemical transport modeling, it is important to resolve the temporal and seasonal variation cycle. Recently, there have been research efforts in developing gridded natural emission processors to support atmospheric mercury simulation at regional scales. For example, Bash et al. (2004) developed a Mercury (Hg) Surface Interface Model (HgSIM) to estimate the vegetative emission in the Northeast US and Southeast Canada. However, to assess its seasonal variation and relative importance to anthropogenic emissions, a longer simulation period covering a larger domain is needed.
Biogenic Emission Inventory System Version 3 (BEIS3) is a flexible modeling system for estimating the EI of volatile organic compounds (VOCs) and NOx from biogenic sources (Lamb et al., 1993) in comprehensive air quality modeling. BEIS3 takes the inputs from landuse/landcover data and meteorological fields, and calculates the temporally and spatially resolved biogenic emission of VOCs and NOx. It is favorable to implement vegetative mercury emission processing in a modeling system such as BEIS3 because (1) the emission processing of mercury and biogenic VOCs shares many common routines and input data, and (2) the parallel emission processing of mercury and VOCs/NOx streamlines the EI preparation to meet the model input requirements. The objectives of this study are to develop a processor within the framework of BEIS3 for preparing mercury EI from vegetation, and to estimate its emission quantity in a continental United States (CONUS) domain. In this development, we employed a regression-based method to assimilate the model output to mercury flux measurement data. We also performed a sensitivity analysis to estimate the lower and upper limits of the annual emission. The modeled vegetative emission characteristics and quantity are presented and compared to anthropogenic mercury EI. The implications of including vegetative mercury emission in the modeling of atmospheric mercury are discussed. 2. Method 2.1. Models, domain and input data The version of BEIS3 used in this development is Version 3.11, a stand-along research version on UNIX/Linux platform. The model calculates the speciated, plant-species-specific emission of VOCs and NOx for the landuse/landcover types listed in the USGS Biogenic Emissions Landcover Data Version 3 (BELD3) datasets. The 1-km resolution, 230-category BELD3 data consist of 19 USGS landcover types, 17 agricultural use types and 194 forest tree species types. The BELD3 data, along with the mercury emission factors estimated from this work, were utilized to generate the normalized vegetation-specific mercury emission in a 36-km Lambert Conformal grid covering the entire CONUS. The domain has 14 vertical layers with a surface layer thickness of about 37 m. The surface
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Walcek et al. (2003). Plume rise was treated extensively by the algorithms in SMOKE. The temporal allocation of the anthropogenic emission followed the temporal profiles suggested by USEPA Clearinghouse for Inventories and Emission Factors (http://www.epa.gov/ttn/chief/). Spatial allocations were either treated by the latitude–longitude location of each emitting unit (point sources) or by emission surrogate prepared by Multimedia Integrated Modeling System (MIMS) spatial surrogate generator using the 2002 GIS data.
temperature and cloud-cover-corrected solar radiation from a meso-scale meteorological model (MM5 Version 3.6, Grell et al., 1994) were retrieved and converted into model-ready format using a Meteorology–Chemistry Interface Processor (MCIP2, Byun and Ching, 1999). The converted data were used for temperature and solar radiation corrections to calculate the diurnal variation of vegetative emission. To estimate the annual mercury emission from vegetation and its seasonal variation, we used the 2001 hourly meteorological fields in an annual simulation. Year 2001 is a good representative meteorological year in the CONUS domain with comprehensive air quality data availability. It was also selected by the USEPA for annual simulations of criterion air pollutants. To compare mercury emission quantity from vegetative and anthropogenic sources, annual anthropogenic EI processing was also performed using the same meteorological data. Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System Version 2.0 (the model and its documentation are available at www.cmascenter.org) was used for the EI preparation. In this simulation, the point and area source emissions of the 1999 National Emission Inventory Estimates (NEI99, Final Version 3) for air toxins were used. We downloaded the NEI99 data from ftp://ftp.epa.gov/EmisInventory/, retrieved the mercury emission, and processed the raw mercury EI data to a model-ready format (Inventory Data Analysis format) for SMOKE. The speciation of the anthropogenic emission followed the recommendations of Seigneur et al. (2001) and
2.2. Model implementation A regression-based model was employed to estimate the mercury emission flux from vegetation. The goal of the regression-based approach was to assimilate the modeled mercury emission intensity to the field observations of mercury flux. Since vegetative mercury emission is influenced by meteorological parameters (Lindberg et al., 2002; Obrist et al., 2005), the diurnal variation of the modeled GEM flux was simulated through the correction factors of solar irradiation and temperature at earth’s surface, i.e., F i ¼ F s;i C T C L ,
(1)
where Fi is the estimated mercury emission flux (ng m2 h1) for a given landuse type or a vegetation species i, Fs,i is the implemented emission factor (ng m2 h1) for species i (Table 1), CT is the temperature correction factor, and CL is the solar radiation correction factor. In the evaluation of CT
Table 1 The average daytime GEM emission intensities (ng m2 h1) used in model runs Categories
Case 1
Case 2
Case 3
W/S ratioa
Remarks/references
Water Wetlands Agriculture Maples (13)
1 40 1 4
1 40 4 4
1 40 4 37
0 0.5 Modelb 0
Pirrone et al. (2001) Lindberg et al. (2002) Assumed Hanson et al. (1995), Lindberg et al. (1998) Hanson et al. (1995), Lindberg et al. (1998)
Oaks (44)
5.3
5.3
37
0
Pines (39)
4
4
18
1
Other plants a
1
4
37
Assumed (Cases 1 and 2), Lindberg et al. (1998) b
Model
Assumed (Cases 1 and 2), Lindberg et al. (1998)
W/S ratio indicates the ratio of summertime to wintertime emission factors used in the model. The implemented Fs,i values for different seasons depend on the tree species. Same Fs,i values were used in different seasons for the evergreen plant species, while different Fs,i values were used for deciduous plant species according to a ratio defined in the BEIS3 emission factor table. b
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and CL, a formulation analogous to Guenther Algorithm (Guenther et al., 1995) for biogenic VOC emission was assumed since the emission– temperature–radiation relationship has not been yet available for mercury emission from vegetation: kL1 L C L ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , 1 þ kL2 L2 CT ¼
exp½kT1 ðT 30Þ=kT3 T , 1 þ exp½kT2 ðT 40Þ=kT3 T
(2)
(3)
where L is the solar radiation intensity at ground level (W s1) after cloud cover correction in MM5 (with canopy radiation transfer), and T is the temperature (1C) at 10 m above ground. The terms kL1, kL2, kT1, kT2, and kT3 are regression constants evaluated from the GEM evasion flux measurement data from Lindberg et al. (2002) using the least square technique. After the regression, the typical summer (July) ranges of CL and CT in the domain are 0–1.6 and 0.5–1.8; while the typical winter (January) ranges of CL and CT are 0–1.5 and 0.05–0.16, respectively. The emission factors (Fs,i) were estimated from the measured mercury evasion fluxes for various landuse/vegetation types using the average flux during daytime hours. It is either calculated from the measured mercury flux selected for the regression analysis, or assumed as the ‘‘average’’ emission intensity previously reported in the literature (e.g., Hanson et al., 1995; Lindberg et al., 1998, 2002; Pirrone et al., 2001). Since mercury emission flux was measured only for a limited number of vegetation species (e.g., Hanson et al., 1995; Lindberg et al., 1998, 2002), we grouped all the oaks (13 species), maples (44 species) and pines (39 species) in the BELD3 data categories, and used the same average daytime emission as Fs,i for the three vegetation groups (i.e., oaks, pines and maples). Based on the approach described above, several assumptions/simplifications were made for the scaling of the vegetative mercury emission in the model calculation. First, elemental gaseous mercury is treated as a VOC-like species, and shares similar emission characteristics of biogenic VOC emission in the model. Second, the vegetative mercury emission is only related to the plant types and meteorological parameters without detailed mechanistic consideration of the air-surface exchange process. Third, the obtained regression constants are assumed to be applicable to different vegetation types. We acknowledge that the processes involved in vegetative mercury
emission and in biogenic VOC emission are fundamentally different, and that several mechanistic models have been developed to describe the airsurface exchange of mercury (e.g., Xu et al., 1999; Zhang et al., 2002). However, in terms of assimilating the characteristics of vegetative mercury emission for Eulerian model applications, our test indicated that Eqs. (1)–(3) represent reasonable intensity and diurnal/seasonal trend of the emission. Due to the uncertainty associated with the reported vegetative mercury fluxes, we drove the model with three different sets of Fs,i with summer/winter emission ratios as shown in Table 1. In Case 1, the emission factors of the vegetation species without available mercury flux measurement data were set to a background level similar to soil emission (1.0 ng m2 h1, Lindberg et al., 1998). This case would represent the lower limit of the vegetative emission due to the much greater flux from vegetation compared to soil flux. In Case 2, we assume that the vegetation species other than oaks, pines and maples have similar emission flux intensity at 4 ng m2 h1, which would reasonably estimate the true emission intensity from these species. In Case 3, we use the average daytime flux reported in the earlier literatures for emission estimate (e.g., Lindberg, 1996; Lindberg et al., 1998). This case would give the upper limit estimate since more recent data suggested that earlier flux measurement may overestimate the emission flux from forest due to mercury contamination of the soils (Graydon et al., in review). To implement the above-mentioned regression algorithms, the source codes of BEIS3 V3.11 were modified to include GEM as one of the emitted species. The mercury emission factors shown in Table 1 were appended in the BEIS3 emission factor table for VOCs and NOx. Fig. 1 shows the data flow of our model implementation. The output from the model is temporally (hourly) and spatially resolved gridded GEM emission in netCDF format ready for applications in Eulerian-based chemical transport models such as CMAQ-Hg and CAMx-Hg. The data in the subsequent maps are expressed as the emission intensity (ng h1 m2) in each 36-km grid of the model domain. 3. Results and discussion 3.1. Verification of modeled emission flux with measurements Fig. 2 shows the diurnal emission cycle by the model estimate and by the mercury evasion flux
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Fig. 1. Data flow of simulating vegetative emission of mercury in BEIS3.
80 Mercury Flux (ng m-2 h-1)
measured in Florida wetland in June 1997 (Lindberg et al., 2002). As mentioned, the regression-based approach attempts to assimilate the measured emission flux intensity and daily variation. As shown in Fig. 2, the simulated diurnal cycle of GEM emission depicts the measured emission fluxes reasonably well. Similar implementation was also carried out for other tree species and landcover types. We used the modeled mercury flux intensity and diurnal variation to estimate the monthly and annual emission quantity from vegetative sources.
Measured
70
Modeled
60 50 40 30 20 10 0 0
3.2. Spatial and temporal distribution of modeled emission Figs. 3 and 4 show the typical summer (July) and winter (January) diurnal variation of the modeled GEM emission in the model domain. The results shown in Figs. 3 and 4 were obtained from the average hourly emission of the 31 days in July and January using the emission factors in Case 2 of Table 1. The diurnal patterns of Cases 1 and 3 are similar and therefore not shown. In both figures, the modeled GEM fluxes at hours 8, 12, 16 and 20 (all in Eastern Standard Time) are shown. In summer the simulated GEM emission starts from the east coast and gradually ‘‘migrates’’ to the west coast according to the change of cloud-corrected solar intensity and surface temperature (Fig. 3). The vegetative emission is mainly from the central US due to the high temperature and large coverage area of forest/agricultural landcover, with peak flux in the region ranging from 5 to 10 ng m2 h1. The strongest emission flux intensity occurs at Florida wetland, with a modeled flux of 36 ng m2 h1.
400
800
1200 1600 Hour of Day
2000
2400
Fig. 2. Comparison of the modeled vegetative mercury emission with the measured emission flux. The measured data are from a 5day field measurement in Florida wetland in June 1997 for the Everglades Nutrient Removal project (ENR) (Lindberg et al., 2002).
Similar diurnal variation is also observed in winter (Fig. 4). However, the emission intensity is much weaker and occurs only in the deep south of the domain. This is due to the smaller emission factors employed in the calculation (Table 1), and the much lower surface temperature and solar radiation in winter. The emission flux for the most part of the domain is below 0.5 ng m2 h1. Under these conditions, significant emission flux is observed only in Florida Peninsula and the Caribbean Islands. 3.3. Seasonal variation of modeled vegetative emission The seasonal variation of vegetative mercury emission was characterized by the modeled monthly
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Fig. 3. Typical modeled summer time diurnal variation (July) of the vegetative mercury emission at (a) 8:00 am, (b) 12 pm, (c) 4:00 pm and (d) 8:00 pm using the emission factors in Case 2. The figure represents the hourly average of the 31-day simulation in July 2001 (e.g., (a) represents the flux average of all 31-day fluxes at 8 am). All times are at eastern US time. It exhibits a strong dependence of the surface temperature and irradiation.
emission. The monthly GEM emission for the three sensitivity cases is shown in Fig. 5. As seen, the annual vegetative emission is dominated by the emission in June–August. In fact, about 60% of the annual emission is generated in the three summer months, and more than 90% of the annual vegetative emission comes from April to September emission. Summing up the monthly emission for the entire year, the annual vegetative emissions for the three sensitivity cases are 31 (Case 1), 44 (Case 2), and 140 (Case 3) tons, respectively (Table 2). The upper limit estimate (Case 3: 140 ton yr1) would account for nearly half of the total mercury released to the atmosphere in the US (more discussion in Section 3.4.) and agrees well with earlier estimates (Lindberg et al., 1998; Seigneur et al., 2004). The emission difference between Cases 1 and 2 is only 13 ton yr1. This is because the emission factors were not changed for wetland, pines, oaks, and maples for the two sensitivity cases. The pines, oaks and maples constitute a considerable fraction of the total forest cover according to the BELD3 datasets, especially in the south. Therefore, they contribute relatively larger emission compared to other tree
species due to the higher surface temperature and solar radiation. We feel that the emission quantity estimated in sensitivity Case 2 would better represent the mercury emission contribution from vegetative sources. Earlier flux data over forest may overestimate the emission flux due to the proximity of the field site to the a known mercury emission source which did result in elevated surface soil concentrations of mercury, and could also have biased the measured Hg gradients (Lindberg et al., 1998). Although this estimate (Case 2) was based on the assumption that all the vegetation species emit mercury at a similar flux intensity, there are limited laboratory and field data which indicate that this is the case unless the soils are enriched in mercury content (e.g., Hanson et al., 1995; Poissant and Casimir, 1998; Graydon et al., in review). Previous scaling estimates are to a large extent extrapolated from limited data availability and highly uncertain (e.g., United States Environmental Protection Agency (USEPA), 1997; Lindberg et al., 2004). Our empirical model-based scaling should better represent the mercury contribution from vegetative sources.
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Fig. 4. Typical modeled winter time diurnal variation (January) of the vegetative mercury emission at (a) 8:00 am, (b) 12 pm, (c) 4:00 pm and (d) 8:00 pm using the emission factors in Case 2. The figure represents the hourly average of the 31-day simulation in January 2001. All times are at eastern US time. The emission flux occurs only in the south and the intensity is much reduced compared to the summer flux.
35
Monthly Emission (Tons).
30
Case 1 Case 2
25
Case 3
20 15 10 5
Au gu st Se pt em b O er ct ob er N ov em be D r ec em be r
e
Ju ly
Ju n
Ja nu ar y Fe br ua ry M ar ch Ap ril M ay
0
Fig. 5. Seasonal variation of the modeled vegetative mercury emission in the CONUS domain for various sensitivity cases. It can be seen that the emission is dominated by summer time.
3.4. Comparison with anthropogenic mercury emission The modeled vegetative emission was compared to anthropogenic mercury emission in terms of
emission characteristics and quantity. Identical meteorological data were used for the vegetative and anthropogenic emission processing. The primary emission characteristic differences between the vegetative and anthropogenic mercury emission are:
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(1) vegetative emission exhibits strong diurnal and seasonal variations while anthropogenic emission does not, (2) vegetation emits primarily GEM while anthropogenic emission releases GEM, RGM and PHg depending on the source, fuel types and emission control devices in use, and (3) vegetative emission releases mercury to the surface layer of the model domain only while anthropogenic emission from point sources is subject to the plume rise depending on atmospheric stability. Compared to
Table 2 Comparison of mercury emission and speciation by anthropogenic and vegetative sources in the model domaina Anthropogenic
Case 1
Case 2
Case 3
GEM RGM PHg
78 50 14
31 0 0
44 0 0
140 0 0
Total
142
31
44
140
a
All values are in tons/year in the CONUS domain.
anthropogenic emission, the vegetative source is relatively weaker and more diffused, but covers a much broader region. Fig. 6 shows the spatial distribution of the total annual emission from vegetation and anthropogenic sources, which was generated from the sum of the hourly emission for the entire year. After summing the entire year’s data, the annual vegetative emission is mainly contributed by the southeastern region of the US (Fig. 6a–c) for all three sensitivity cases. The difference in the spatial feature in Figs. 6a–c is caused by the difference in their respective emission factors (Table 1). The anthropogenic emission, on the other hand, is dominated by the emission from the eastern US and the west coast regions (Fig. 6d). Considering emission speciation, anthropogenic sources emit 78 tons of GEM, 50 tons of RGM and 14 tons of PHg, respectively, in the continental US (Table 2). Fig. 7 shows the month-by-month mercury emission quantity and speciation by vegetative (Case 2) and anthropogenic sources. The monthly anthropogenic emissions are relatively constant
Fig. 6. Spatial distribution of the modeled vegetative mercury emission for various sensitivity cases and anthropogenic mercury emission: (a) Case 1, (b) Case 2, (c) Case 3 and (d) total anthropogenic emissions from USEPA NEI99 including point and area sources. Note that the emission quantity is expressed as ton/yr in each 36-km model grid and the color scale for (a) and (b) differs from that of (c) and (d).
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throughout the year. With the emission factors employed in Case 2, the mercury emissions from vegetation and from anthropogenic sources are comparable in summer months. For example, in the month of July, vegetative emission constitutes 47% of total mercury release into the atmosphere. However, in the winter time, anthropogenic sources dominate the mercury emission from vegetation (it contributes more than 95% of the total emission in the months of November– February) due to the smaller emission factors for the deciduous plant species in the model, and the much lower surface temperature and solar radiation. 3.5. Implications in atmospheric mercury simulation From our sensitivity analysis, we conclude that mercury emission from vegetation can constitute a significant fraction of the total mercury input to the atmosphere, and should be included in the EI processing of mercury, especially in warm months. Fig. 8 shows the mercury emission contribution from vegetative sources in various sensitivity cases. We estimate that vegetative emission contribute
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from a lower limit of 18% (Case 1) to an upper limit of 49% (Case 3) of the total annual mercury emission, with our best estimate at 24% (Case 2) in the domain. Considering that vegetation releases mercury to the surface layer only, and that anthropogenic emission can be diluted by plume rise, the contribution from vegetation in the surface layer is even greater, ranging from 57% to 86% in the model domain, with our best estimate at 66%. Due to the relatively small emission quantity of mercury compared to other air pollutants such as VOCs, NOx and PM, both anthropogenic and vegetative emissions are not likely to significantly modify the background concentration of mercury (1–3 ng m3) because of vertical mixing and dilution. Furthermore, since the speciation of vegetative mercury emission is dominated by GEM, it should not increase mercury deposition significantly unless under a highly oxidative condition in the atmosphere, due to the low deposition velocity of GEM (Lindberg et al., 1992, Xu et al., 1999). Contrasting to anthropogenic mercury emissions that can lead to important local deposition due to the presence of considerable RGM and PHg fraction (Fig. 8),
Fig. 7. Comparison of mercury emission quantity and speciation from anthropogenic and vegetative sources in the CONUS domain. The legends HG0, HGIIGAS and PHG represent the anthropogenic emission of GEM, RGM and pHg. The legend HG0-vegetation represents the vegetative emission estimate using the emission factors in Case 2.
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100%
Emission Contribution (%)
90% 80%
18% 24%
8% 49% 7%
70% 60%
57% 66%
29% 86%
27%
50%
5%
40%
4% 18%
20%
3%
15%
30% 45%
12% 42% 28%
23%
10%
1% 5%
19%
8%
0% Case 1
Case 2
Case 3
Total Emission Anthrop-GEM
Case 1
Case 2
Case 3
Surface Emission
Anthrop-RGM
Anthrop-PHg
Vegetative
Fig. 8. Contribution of annual mercury emission input to the model domain and to the surface layer of the domain in various sensitivity cases.
mercury emission from vegetation would affect the surface concentration of GEM only. In our testing of how this natural mercury emission affects the ambient concentration and deposition of atmospheric mercury using the same model domain, we found that the Case 2 emission can force an increase of surface layer GEM concentration up to 0.2 ng m3 during summer midday, but shows little impact on dry mercury deposition. Nevertheless, vegetative emission represents a potentially important mercury input to the atmosphere. 4. Conclusions In this study, we develop a regression-based vegetative mercury emission processor in the BEIS3 framework in a 36-km model domain covering the CONUS. The developed processor can be used for estimating vegetative mercury emission at high temporal and spatial resolutions, along with VOC and NOx emission processing from biogenic sources, to support the chemical transport modeling of atmospheric mercury. From our sensitivity analysis, we estimated that vegetative mercury emission ranges from a lower limit of 31 ton yr1 to an upper limit of 140 ton yr1, with the best estimate at 44 ton yr1 in the continental US. The vegetative emission is mainly contributed from the southeastern US, and exhibits strong diurnal and
seasonal variations. Compared to anthropogenic mercury emission using our best estimate (Case 2), this natural source can account for nearly half of total mercury release in summer months. Such emission input can increase the mercury concentration in the surface layer by up to 0.2 ng m3 in our 36-km domain grid. An uncertainty associated with our current estimate is that we assume many vegetation species have similar emission flux intensity due to the lack of comprehensive flux measurement data for the wide variety of vegetation species in the BELD datasets. Once additional mercury flux data become available, the data can be easily implemented as emission factors in the model. It should also be noted that the regression-based model in this work does not explicitly treat the mechanistic processes of vegetative mercury emission, which involves a series of plant physiological processes not well understood. There is clearly a research need to address the effect of soil types/conditions, soil/water mercury content, plant physiological processes, and other physical/chemical mechanism on mercury exchange between vegetations and the atmosphere before a sound science-based vegetative mercury emission processor can be developed. Nevertheless, the current model serves as a viable modeling tool for generating vegetative mercury input to atmospheric mercury models.
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Acknowledgements This work was supported in part by Texas Commission on Environmental Quality (TCEQ) under Contract no. 582-4-64582 and by Gulf Coast Hazardous Substance Research Center (GCHSRC) under the Contract no. 043LUB0855. The financial support of the sponsors is gratefully acknowledged. We want to thank Ms. Heng Yang for her assistance in setting up the modeling platform for this work. One coauthor (S.L.) acknowledges the Electric Power Research Institute for support at ORNL, which is managed by UT-Battelle for the US Department of Energy.
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