Dissolved Gaseous Mercury Behavior in Shallow ...

Task 5.4 Air-Water Exchange Studies1 by Gary Gill, Pacific Northwest National Laboratory The major effort for this task was to conduct measurements of the Dissolved Gaseous Mercury (DGM) concentration in surface waters and to use this information to model the air-water exchange flux of DGM.

General Background on DGM in Natural Waters Mercury Speciation and Cycling In Natural Waters Mercury can exist in many chemical and physical forms in natural waters (Hines and Brezonik, 2004). The speciation of mercury ultimately governs its bioavailability, toxicity, and biogeochemical cycles in the aquatic environment (Bloom, 1992; Benoit et al., 2001; Choe et al., 2003; O’Driscoll et al., 2003a&b). In estuaries and the ocean, total mercury typically exists in picomolar concentrations (Gill and Fitzgerald, 1988). Some of the prevalent forms of mercury in natural waters include: dissolved inorganic mercury species [Hg(II)], dissolved gaseous mercury [Hgo (aq)], monomethyl mercury (CH3Hg+), and particulate adsorbed mercury species. Numerous studies have shown that the cycling of elemental mercury (Hgo) is of great importance to Hg transport, residence time, and reactivity in natural waters (Amyot et al., 1997b; Mason et al., 1994; Mason and Sheu, 2002; Morel et al., 1998). In the atmosphere, mercury exists predominantly as gaseous elemental mercury (GEM) at a concentration of approximately 1.5 ng m-3 in the Northern Hemisphere. In the Southern Hemisphere, GEM is approximately 1 ng m-3. The tropospheric residence time for GEM is approximately 1 year (Temme et al., 2003). Dissolved gaseous mercury (DGM) is mainly composed of gaseous elemental mercury (GEM) in lakes, estuaries, the surface ocean, and rivers (Amyot et al., 1997b). Other volatile mercury species, such as dimethyl mercury, can be present at depth in the ocean (Amyot et al., 1997b).

Factors Influencing the Production and Destruction of DGM Light Driven Reactions Dissolved gaseous mercury production and consumption has been documented by many authors. In high Arctic lakes, Amyot et al. (1997a) found a significant positive relationship between solar radiation and DGM production. This process is 1

This work was conducted by Charlie M. Landin of Texas A&M University as part of thesis research requirements for a master’s degree in Chemical Oceanography.

Calfed Mercury Project – Task 5.4

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called photoreduction. In lakes containing medium to high dissolved organic carbon, DGM production may be limited to biotic reductive processes; but in clear waters DGM may be produced photochemically by UV radiation (Amyot et al., 1997a; Amyot et al., 1994). The reverse reaction of photoreduction is photooxidation (i.e. Hg(0) Æ Hg(II)) which has been documented by Lalonde et al. (2000). Solar radiation can promote both oxidation and reduction of DGM through direct and indirect pathways (Hines and Brezonik, 2004; Amyot et al., 1997b). The peak absorption by Hg(0) occurs at 253.68 nm, but radiation 6 ng/m2/h (>720 pmol/m2/day). At lower fluxes, this model over predicted fluxes significantly at all wind speeds.

Results and Discussion DGM in Surface Grab Sample Collections Given in Table 5.4.2 and Figure 5.4.4 below is a summary of the DGM results for the grab sample collections obtained in this study. Included in the Table and Figure are the results for DGM determined using the continuous system for light conditions


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only (April 2006). Additional data detail is provided in the Appendix. The DGM results obtained for the Cosumnes river are not included in any of the statistical treatments because this site is known to be elevated in elemental mercury content and unrepresentative of most of the Delta sites. The percent saturation calculations were made using temperature measurements taken at the site when the DGM values were collected. The GEM flux measurements were made using the model of Poissant et al. (2000) described above. Table 5.4.2. Summary of Average DGM Concentrations, Saturation and Fluxes for Surface Water Grab Collections Collection Date

Hgo Flux

No. of

DGM

%

Samples

(pg/L)

Saturation

pmol/m2/day

µg/m2/yr

2

24

544%

149

11

Jul-04

Site Cosumnes River1

Jul-04

Mandeville 14 Mile Slough Suisun Bay

5

6.9 ± 3.6

154 ± 81

19 ± 26

0.50 ± 0.9

Dec-04

Mandeville

4

6.1 ± 4.7

93 ± 71

-0.91 ± 9.7

-0.067 ± 0.71

Mar-05

Mandeville Georgiana Little Break

7

16 ± 7.9

291 ± 145

39 ± 30

2.9 ± 2.2

Jul-05

Little Break Oakley Mandeville

8

10 ± 4.5

225 ± 101

42 ± 34

3.1 ± 2.5

Oct-05

Little Break Oakley Mandeville

12

16 ± 9.0

305 ± 176

36 ± 31

2.7 ± 2.3

74

9.7 ± 5.1

153 ± 55

19 ± 28

1.4 ± 2.0

41

12 ± 7.7

229 ± 149

29 ± 31

2.1 ± 2.3

Apr-06 All Periods3

Georgiana All Sites3

2

1

Cosumnes River Not Included in Averages Determinations made using continuous system; Mean Value for 2½ days of light period only 3 Excluding Continuous Measurements (4/2006) 2

Wind speed data used for this modeling was taken from meteorological data available for Twitchell Island by the California Irrigation Management Information System (CISMS). Data for the Twitchell Island site are available for the period beginning in October 1997. Access to daily and monthly average wind speed and other meterological data can be obtained from the web site: http://wwwcimis.water.ca.gov/cimis/welcome.jsp). A summary of the wind speed data used for this modeling is given in Figure 5.4.5 below. Additional wind speed data information is given in the Appendix. DGM concentrations in surface grab samples ranged from below detection (~ 3 pg/L) to 40 pg/L. The average DGM concentration for all 41 grab samples collected


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was 12 ± 7.7 pg/L. The variation in DGM concentrations during any sampling trip was also quite high; typically the coefficient of variation for a data set was around 50% (see Appendix). One major reason for this variability is likely due to the time of collection (discussed later). Most of the grab samples were collected during mid day, when DGM concentrations would likely be at their maximum. All of the DGM concentrations, with the exception of samples collected in December 2004, had concentration levels that exceeded saturation (Table 5.4.2), indicating that DGM was effluxing out of the water to the air. Typical saturation levels were between 150 and 300%. Figure 5.5.4. DGM Concentrations For Surface Water Grab Samples. 30 25

DGM (pg/L)

20 15 10 5 0 Jan

Feb

Mar

Apr

May

June

Jul

Jul

Sep

Oct

Nov

Dec

The highest DGM flux observed, 230 pmol/m2/day, was determined for the Cosumnes River. This is not suprising given that the Cosumnes river is well known to contain elemental mercury contamination from past gold mining activities. Excluding this site, DGM fluxes in the Delta ranged from -11 to 121 pmol/m2/day (0.81 to 8.8 µg/m2/yr) (see Appendix). The mean DGM flux for 41 samples was 29 ± 31 pmol/m2/day (2.1 ± 2.3 µg/m2/yr). It is important to note at this point that these flux values represent averages based on daytime DGM determinations and are not necessarily representative of true daily averaged (24 hour) or yearly fluxes as no night time measurements are included in the determinations (see discussion in next section). The relative sensitivity of variations in wind speed and temperature on air-water exchange flux calculations are illustrated in Table 5.4.3. For this exercise, a DGM value of 10 pg/L was selected as representative of typical concentrations observed in the Delta (Figure 5.4.3). Flux calculations were conducted using the model described previously by Poissant et al. (2000). As illustrated in Table 5.4.3, DGM air-water exchange flux calculations are highly sensitive to variations in temperature and wind speed. Doubling the temperature results in approximately a similar increase in exchange flux at a constant wind speed. Similarly, doubling the wind speed results in approximately a three-fold increase in exchange flux at


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constant temperature. Based on seasonal wind speed (Figure 5.4.5) and temperature variations in the Delta, it is easy to demonstrate that maximum airwater exchange rates would occur in the summer, when winds and temperature are typically elevated, compared to wintertime conditions. Figure 5.4.5 Monthly Average Wind Speed for Twitchell Island. 6 Twitchell Island Wind Speed (m/s)

5 4 3 2 1

ug us Se t pt em be r O ct ob er N ov em be D r ec em be r

A

Ju ly

Ju ne

ay M

A pr il

ru ar y M ar ch

Fe b

Ja nu a

ry

0

Table 5.4.3. Variations in DGM flux (pmol/m2/day) as a function of water temperature and wind speed. DGM fluxes were calculated using the Poissant et al. (2000) model and a DGM concentration in surface water of 10 pg/L. Windspeed (m/s) 1 2 3 4 5 6 7 8 10

5 0.86 2.7 5.2 8.4 12 16 21 26 38


10 1.6 4.9 10 15 22 30 38 48 69

Temperature (°C) 15 20 2.3 3.0 7.1 9.3 14 18 22 29 32 42 43 56 55 73 69 90 99 130

25 3.7 12 23 36 52 70 91 113 163

30 4.5 14 27 44 63 85 110 137 198

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Continuous DGM Measurements The continuous DGM measurement system was operated in Georgiana Slough, near its confluence with the Mokuleme river for approximately 60 hours beginning at 7:00 PM on March 30 and ending at 1:00 PM on April 2. The system was setup on a dock at the rivers edge and water was drawn directly into the sampler from the river. Sample determinations were made approximately every 30 minutes for the majority of the interval and 125 individual DGM measurements were obtained. A tabulation of DGM data along with ancillary measurements is given in the Appendix. A summary of the data concentration information is shown in Figures 5.4.6a-e. DGM concentrations in surface waters changed quite rapidly in response to sunrise and sunset as evidenced by the concentration trends of DGM and Photosynthetically Available Radiation (PAR) (Figure 5.4.6). DGM concentrations were observed to rise from background night time concentrations to maximal levels during light periods within 3-4 hours. Similarly, once the sun set, the DGM concentrations fell to background levels quite rapidly, typically within 3-4 hours. The average DGM concentration observed during dark periods was 3.1 ± 2.0 pg/L (n=51). The average DGM concentration observed during light periods was 9.7 ± 5.1 pg/L (n=74). The average DGM observed over the ~ 60 hour observation period was 7.0 ± 5.2 pg/L, which represented approximately 14 hours of daylight each day. Estimated DGM modeling fluxes using measured DGM concentrations, information on wind speed taken from the CICMS meterology station (described earlier) and PAR data collected at the time of the DGM measurements are illustrated in Figures 7a&b; tabulated data are given in the appendix. Figure 5.4.7a represents fluxes calculated by the modeling approaches of Poissant et al. (2000) and Schroeder et al. (1992). The predictive model of Boudala et al. (2000) is shown in Figure 5.4.7b. In order to match the hourly averaged wind speed data with DGM measurements made more frequently, extrapolations were necessary. The average fluxes obtained for the daylight periods is given in Table 5.4.2. The three modeling approaches all predict different characteristics of the air-water exchange flux of DGM. Among the more notable differences are: (1) The Schroeder et al. (1992) air-water exchange model predicts slightly higher fluxes (~ 50-100%) than the fluxes estimated by the Poissant et al (2000) modeling approach during daylight hours (Figure 5.4.7a); (2) During dark periods, the Poissant model predicts minimal or no exchange occurs, while the Schroeder model suggests that fluxes are generally negative (flow into the water from the air), and in some cases quite significantly so. The fluxes estimated using the approach by Boudala et al. (2000), which are driven by light intensity, predict a much higher flux than either of the other two modeling approaches. As noted previously, the Boudala modeling approach does not do a good job of predicting fluxes when DGM levels are only slightly above saturation. This most likely the reason for the vast discrepancy between the Boudala modeling approach and the other two methods.


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Figure 5.4.6. DGM measurements and ancillary data collected using the continuous DGM monitoring system at Georgiana Slough near the confluence with the Mokuleme River between March 30 and April 2, 2006 30

25

DGM (pg/L)

20

15

10

5

0 6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM

March 30 to April 2, 2006 (PST)

6

Wind Speed (m/s)

5

4

3 2

1

0 6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM


1800 1600

1200

-2

-1

PAR (umol m s )

1400

1000 800 600 400 200 0 6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM



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o

Surface Water Temperature ( C)

11.1 11.0 10.9 10.8 10.7 10.6 10.5 10.4 6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM


11.2

Dissolved Oxygen (mg/L)

11.1 11.0 10.9 10.8 10.7 10.6 6 PM

12 AM

6 AM

12 PM

6 PM

12 AM

6 AM

12 PM



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Figure 5.4.7a. DGM Flux estimates using the Poissant and Schroeder flux models based on DGM and ancillary data collected at Georgiana Slough near the confluence with the Mokuleme River between March 30 and April 2, 2006

150 Poissant Schroeder

-2

-1

DGM Flux (pmol m d )

100

50

0

-50

-100 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM March 30 to April 2, 2006 (PST)

Figure 5.4.7b. Comparison of the Boudala et al (2000) DGM Flux estimate with the Poissant and Schroeder flux models for DGM and ancillary data collected at Georgiana Slough near the confluence with the Mokuleme River between March 30 and April 2, 2006

1400 Poissant Schroeder Boudala

1000

-2

-1

DGM Flux (pmol m d )

1200

800 600 400 200 0 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM -200 March 30 to April 2, 2006 (PST)


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DGM Air-Water Exchange Flux Estimates for the Delta The previous sections clearly indicate that air-water exchange fluxes of DGM are influenced by several parameters, including temperature, wind speed, and length of photo period. All of these parameters change on a seasonal basis and must be considered to estimate fluxes in different periods of the year. Given in Table 5.4.4 and illustrated in Figure 5.4.8 are estimated monthly averaged DGM fluxes considering these factors. Efflux of DGM is assumed to occur only during daylight hours; no air-water exchange is assumed to occur during dark periods. Monthly average DGM concentrations are estimated from data given in Table 5.4.2 and are assumed to be minimal in winter months and to have two peak periods, one in spring and one in fall. Wind speed information is taken from the meteorological data available from the CIMIS weather station at Twitchell Island described previously. Surface water temperatures are estimated from our own field observations and that of data from other monitoring stations. The DGM fluxes are calculated using the approach described by Poissant et al. (2000). The fluxes in Table 5.4.4 represent the daily or monthly flux that occurs only for the light periods on a daily or monthly basis. Summing the monthly flux estimates (last column of Table 5.4.4) gives an annual DGM flux of 0.99 µg/m2/yr.

Table 5.4.4. Estimated monthly averaged DGM fluxes.

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sum

Estimated DGM

Total Hours of Light

(pg/L) 6 10 12 15 10 8 8 11 13 15 10 6

(hours) 9:47 10:47 11:55 13:12 14:16 14:51 14:36 13:40 12:27 11:13 10:05 9:30

U10

Surf. Temp.

Sat. Index

(m/s) 2.36 2.75 3.29 3.83 4.25 4.79 4.41 3.96 3.71 3.00 2.24 2.53

(oC) 9 11 14 18 20 24 26 24 23 18 14 11

% 89% 158% 207% 288% 202% 177% 185% 244% 282% 288% 172% 95%


DGM Fluxes (Light Periods Only) Poissant et al (2000) pmol/m2/d -0.56 4.0 11 27 19 18 17 23 24 16 3.4 -0.29

ng/m2/d -0.11 0.81 2.2 5.5 3.8 3.7 3.5 4.6 4.7 3.1 0.67 -0.06

ng/m2/mth -3.4 25 67 167 116 112 106 140 144 95 20 -1.8 987

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There are additional considerations in using DGM concentration measurements to estimate flux that should be noted. For example, it is important to recognize that DGM is produced by a photochemical reaction and its rate depends on reducible Hg(II), reductant, and light. The removal of the end product (DGM) is controlled in part by wind speed, but cannot exceed the rate of production. Hence, DGM concentrations measured when wind is low are probably not accurate in modeling gas flux when wind is high, because under those conditions the DGM would become depleted. While the data used to generate this modeling exercise is sparse, and required a number of assumptions, it nonetheless suggests that air-water exchange fluxes of DGM vary on a seasonal basis. Minimal fluxes (< 25 ng/m2/month) occur during the winter months (November through February) when water temperatures are cold, wind speeds tend to be minimal compared to summer months, and photoperiod is minimal. Beginning in spring (April) and continuing through early Fall (October), DGM fluxes are more constant, typically varying between 100 and 140 ng/m2/month. On an annual basis, the monthly fluxes estimates average out to around 82 ng/m2/month or approximately 0.99 µg/m2/yr (Table 5.4.4).

Figure 5.4.8. Estimated average monthly air-water exchange fluxes of DGM. 180 160

2

ng/m /month

140 120 100 80 60 40 20 0 -20 Jan Feb Mar


Apr May Jun

Jul

Aug Sep Oct

Nov Dec

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Comparison of Annual Evasion Flux Estimates Based on Grab vs. Continuous Sampling The estimate of the annual flux of DGM based on grab samples given in Table 5.4.2 was 2.1 ug/m2/yr. This is twice the flux estimated using the DGM concentration information obtained with the continuous sampler (0.99 µg/m2/yr) in Table 5.4.4. To understand this apparent discrepancy it is important to first recognize the many assumptions and limitations that went in to the estimates. The grab samples were obtained only during daylight hours, but during different times of the day. Also, the DGM flux calculation described previously by Schroeder used with grab samples is based solely on the concentration of DGM in water and in the air. Since only a single water concentration is typically used for this calculation, and since it is usually not known how DGM varies on a diurnal basis, there is no way to correct for changes in DGM on a temporal basis. Given that DGM varies considerably in response to photoperiod, this is a significant limitation of using one sample to characterize flux. If the assumption is made that DGM flux only occurs during daylight periods, then the flux estimated with the Schroeder formulation can be cut approximately in half and the two estimates then give very comparable results.

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