Approach Estimating Mercury Dry Deposition for AMNeT Leiming Zhang

Transcription

1 Approach Estimating Mercury Dry Deposition for AMNeT Leiming Zhang Air Quality esearch Division Science and Technology Branch Environment Canada, Toronto

2 Gaseous oxidized Hg (GOM) Particulate-bound Hg (PBM) Gaseous elemental Hg (GEM)

3 Dry deposition model for GOM A big-leaf gaseous dry deposition model Pollutant Concentration F = C * V d a b V d = a + 1 b + c c cut st st 1 c = 1 Wst + st m + 1 ns ac m ns g Zhang et al., 2003 ACP Zhang et al., 2009 A.E Soil

4 Annual average Vd MD08 MD99 MS12 NH06 NJ05 NJ30 NJ32 NJ54 NS01 NY06 NY20 NY43 NY95 OH02 OK99 UT96 UT97 VT99 WV99 ELA Zhang et al., 2012 ACP

5 Dry deposition model for PBM A size-resolved dry deposition model F = C * V d V d = V g + ( a 1 + s ) s = 3u 1 *( EB + EIM + EIN ) 1 Zhang et al., 2001 A.E

6 Assumed size distribution 5 4 PM PM 10+ dm/d(logd) (frequency, %) PM Aerodynamic diameter D (µm) Zhang et al., 2012 ACP

7 Non-negligible coarse Hg mass fraction and higher Vd for coarse Wetland site Industrial site PM2.5 PM PM10+ Fang et al., 2012 A.E.

8 Coarse Hg contributed 50-85% of the total particulate Hg dry deposition Fang et al., 2012 A.E.

9 ecommendation for PBM Vd for both fine and coarse PM will be provided F = C * V finepbm AMNeT d F CF F total = C AMNeT ( V + V df dc 1 CF ) CF assumed mass fraction for coarse PBM

10 Previous approach for GEM Same model as for GOM but with different input parameters and with annual natural emission from GAHM 40 GEM Net GEM MD08 MD99 MS12 NH06 NJ05 NJ30 NJ32 NJ54 NS01 NY06 NY20 NY43 NY95 OH02 OK99 UT96 UT97 VT99 WV99 ELA Zhang et al., 2012 ACP

11 Proposed new approach - Bi-directional exchange model Flux F t, Concentration x a x c ac F g g x g >0 a b F cut cut st Soil χ c F s x s >0 χ g, χ s? F F = F + F + F F t st t cut = st ( χ ( cut a a cut ( χc χst ) = st χc = + F χ g c b F ) ) g ( χc χ g ) = ( + ) { χ 1 + χ 1 + χ 1 } { } = a ab st st g g ab st cut g ac g

12 Knowledge on GEM bi-directional exchange Sample vegetation measurements χ cp min (ng m -3 ) χ cp max (ng m -3 ) Conditions 7 30 Dark Light 2 3 Dark 3 4 Light Data trends Higher compensation points in light over dark conditions Higher values in spring and summer Increases with growth of foliage More dependent on ambient atmospheric [Hg] than soil [Hg] Dark 5 6 Light Hansen et al., 1995; Ericksen and Gustin,

13 Knowledge on GEM bi-directional exchange Sample soil measurements χ cp min (ng m -3 ) χ cp max (ng m -3 ) Conditions Dark Light 1 2 Dark Both 5 - Dark Data trends Higher compensation points in light conditions Increases with increasing soil [Hg] Strong dependence on solar radiation Dependent on ambient and soil temperatures Seasonal changes more noticeable over ground surfaces than vegetation Xin and Gustin, 2007; Gustin et al., 2006; Edwards and Howard,

14 Knowledge on GEM bi-directional exchange Soil conditions ph, moisture, [Hg], temperature all increase emissions Canopy Growth Stage - Trend of deposition in spring - Increasing emissions in summer with foliage Canopy wetness Increase in emissions in dry conditions Increase in deposition with canopy/leaf wetness Atmospheric Hg 0 Concentration Higher [Hg 0 ] deposition, lower [Hg 0 ] emission Diurnal variations Emission in daytime/light conditions Deposition in nighttime/dark conditions

15 Knowledge on GEM bi-directional exchange Soil conditions ph, moisture, [Hg], temperature all increase emissions Canopy Growth Stage - Trend of deposition in spring - Increasing emissions in summer with foliage Canopy wetness Increase in emissions in dry conditions Increase in deposition with canopy/leaf wetness Atmospheric Hg 0 Concentration Higher [Hg 0 ] deposition, lower [Hg 0 ] emission Diurnal variations Emission in daytime/light conditions Deposition in nighttime/dark conditions

16 Example model results Flux (ng m -2 s -1 ) Flux (µg m -2 yr -1 ) LUC 7 New LUC 7 Old Ambient Hg 0 Concentration (ng m -3 )

17 Input data Meteorology data: Model output from Canadian weather forecast model at 15 km x 15 km resolution at surface and the first model layer Land use data: GIS generated 1 km or 2 km circle from remote sensing data, converted to 26 LUC used in the dry deposition models Brook et al., 1999 A.E. Zhang et al., 2012 ACP 17

18 eferences Brook J.., Zhang L., Franco D., and Padro J., Description and evaluation of a model of deposition velocities for routine estimates of air pollutant dry deposition over North America. Part I. Model development. Atmospheric Environment, 33, Zhang L. Gong S., Padro J., and Barrie L.A., A size-segregated particle dry deposition scheme for an atmospheric aerosol module. Atmospheric Environment, 35, Zhang L., Brook J.., and Vet., A revised parameterization for gaseous dry deposition in air-quality models. Atmospheric Chemistry and Physics, 3, Zhang L., Wright L.P., and Blanchard P., A review of current knowledge concerning dry deposition of atmospheric mercury. Atmos. Environ 43, Zhang L., Blanchard P., Gay D.A., Prestbo E.M., isch M.., Johnson D., Narayan J., Zsolway., Holsen T.M., Miller E.K., Castro M.S., Graydon J.A., St. Louis V.L., and Dalziel J., Estimation of speciated and total mercury dry deposition at monitoring locations in eastern and central North America. Atmos. Chem. Phys. 12, Fang G.C., Zhang L., and Huang C.S., Measurements of size-fractionated concentration and bulk dry deposition of atmospheric particulate bound mercury. Atmos. Environ. 61, Wright L.P., Zhang L., et al., Modeling bi-directional air-surface exchange for elemental gaseous mercury. In preparation.

19 Acknowledgements Pierrette Blanchard Jeffrey. Brook Silvina Carou David Johnson Julie Narayan L. Paige Murphy et al. David Gay, AMNeT contributors, and many U.S. colleagues