Where do these two models leave us
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Where do these two models leave us?. F = K ol * ( C w – C a / H) Whitman two film model un-measurable parameters: z w & z a Surface renewal model un-measurable parameters: r w , & r a. Calculation of Flux. Net Flux = k ol * (C w – C a /H*)

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Where do these two models leave us?

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Where do these two models leave us

Where do these two models leave us?

F = Kol * ( Cw – Ca / H)

Whitman two film model un-measurable parameters: zw & za

Surface renewal model un-measurable parameters: rw, & ra


Calculation of flux

Calculation of Flux

Net Flux = kol * (Cw – Ca/H*)

1 / kol = 1 / kw + 1 / ka H*

Gross fluxes:

Volatilization: Water to air transfer

Fvolatilization = kol * Cw

Absorption: Air to water transfer

Fabsorption = kol * Ca/ H*

Figure from Schwarzenbach, Gschwend and Imboden, 1993


Processes of air water exchange

Processes of Air / Water Exchange

“Little” Mixing: Stagnant, 2-film model - “little” wind

“More” Mixing: Surface Renewal model – “more” wind

Wave Breaking: intense gas transfer ( breaking bubbles) – “even more” wind

Figure from Schwarzenbach, Gschwend and Imboden, 1993


Conceptualization of wind speed effects on air water exchange

Conceptualization of Wind Speed Effects on Air/Water Exchange

Aerodynamic Transport Region

Transfer to broken surface region

~103m

Viscous Sublayer

~10-3m

Broken Surface

Unbroken Surface

Figure from Baker, 1996


Effect of wind speed on flux

Effect of Wind Speed on Flux

Effect of Wind Speed seen only through changes in Mass Transfer Co-efficient

Net Flux = kol * (Cw – Ca/H*)

----

where

1 / kol = 1 / kw + 1 / ka H*

Figure from Schwarzenbach, Gschwend and Imboden, 1993


Water side mass transfer coefficient

Water Side Mass Transfer Coefficient

1 / kol = 1 / kw + 1 / ka H*

Wanninkhof et al. (1991) summarized SF6 experiments in five North American Lakes and found the gas transfer coefficient to be a power function of Wind Speed. The relationship is normalized to the appropriate Schmidt number to relate to the gas transfer of CO2 across the water side boundary layer.

kw,CO2 = 0.45 u 1.64

where kw,CO2 is the mass transfer coefficient (cm / hr) across the water layer for CO2 (SC = 600 at 20oC), and u is the wind speed at a 10 m reference height (m/s).


Addition of weibull distribution in calculation of k ol

Addition of Weibull distribution in calculation of Kol

A two parameter Weibull Distribution can be used to describe the cumulative frequency distribution of wind speeds (Livingstone and Imboden, 1993. Tellus 45B 275-295).

F (u) = e ^ –(u / h)x

Where F (u) is the probability of a measured wind speed exceeding a given value, u.

the form of the distribution curve is determined by the shape parameter, x. The scale of the u-axis is determined by the scale parameter, h .

the negative derivative of F(u) yields the corresponding probability density function:

f(u) = ( x / u) ( u / h ) x e ^-( u / h ) x

Thus, the mean transfer coefficient over the whole range of wind speeds is obtained by integrating the product of the transfer velocity and wind speed probability density f(u).

kmean,CO2 = 0 to 8 f(u) *kw(u) * du

from Xhang et al. 1999


Weibull distribution continued

Weibull distribution - continued

Instead of a single mean wind speed value during a sampling period, the wind speed probability density f(u) can be used to account for the effects of a nonlinear wind speed on mass transfer across the water layer over a range of wind speeds during a certain time period.

This Weibull distribution can be transformed into a linear form y = ax + b as follows:

x = ln u

y = ln [ -ln F(u) ]

and parameters a & b are determined by linear regression. They are related to the Weibull parameters as follows:

x = a

h= exp (-b / a)

Gas transfer of tracer CO2 can be up to 50% higher when accounting for the non-linear influence of wind speed. Calculations based on mean wind speeds underestimate the total PCB flux to Lake Michigan by as much as 20 to 40% depending upon wind speed variation.

from Xhang et al. 1999


Water side mass transfer coefficient1

Water Side Mass Transfer Coefficient

1 / kol = 1 / kw + 1 / ka H*

Wanninkhof et al. (1991) summarized SF6 experiments in five North American Lakes and found the gas transfer coefficient to be a power function of Wind Speed. The relationship is normalized to the appropriate Schmidt number to relate to the gas transfer of CO2 across the water side boundary layer.

kw,CO2 = 0.45 u 1.64

where kw,CO2 is the mass transfer coefficient (cm / hr) across the water layer for CO2 (SC = 600 at 20oC), and u si the wind speed at a 10 m reference height (m/s).


Water side mass transfer coefficient continued

Water Side Mass Transfer Coefficient - Continued

kw,HOC =kw,mean,CO2 (ScHOC / ScCO2 ) -0.5

= cm s-1

Where Sc is the Schmidt number, the ratio of kinematic viscosity of water and the molecular diffusivity in water. Diffusivity of Hydrophobic organic contaminants (HOCs) through water are calculated using the emperical method of Wilke and Chang (1955). The Schmidt number for CO2 ranges from ~1900 to ~500 for temperatures from 0oC to 25oC according to the equation:

ln ScCO2 = -0.052 T +21.71

where ScCO2 is the Schmidt number for CO2 and T is the temperature in K.

ScHOC = Ab. Viscosity / (Water Density * Diffusivity of HOC in Water)

(g cm-1 sec-1) / ( g cm-3 ) * cm2 sec-1


Water side mass transfer coefficient continued 2

Water Side Mass Transfer Coefficient - Continued 2

Viscosity, m ( g cm -1 s-1) Density of Water (g cm-1)

Diffusivity of PAH in water:

Dw,PAHs= 0.0001326/ ((100 * m1.14) * Mol Vol. 0.589)

= cm2 sec-1


Air side mass transfer coefficient importance of density

Air Side Mass Transfer Coefficient – Importance of Density

When air temperatures are lower than water temperatures, then air at the water surface is warmer than the ambient air, and thus a density difference exists between the two air layers. The resulting buoyancy can affect the mass transfer at the air/water interface. This influence is not important when average wind speeds are in excess of ~3 m/s at 10 m reference height.


Air side mass transfer coefficient

Air Side Mass Transfer Coefficient

1 / kol = 1 / kw + 1 / ka H*

Increasing wind speed increases the air-layer mass transfer coefficient approximately linearly:

ka,H2O = 0.2 u +0.3

= cm s-1

where ka,H2O is the mass transfer coefficient across the air layer for the tracer H2O (cm / hr) and u is the wind speed. the tracer is related to HOCs through the ratio of air diffusivities (H2O vs. HOC) and can be estimated by the Fuller method:

ka,HOC = ka,H2O (DHOC,air / DH2O,air)0.61

where ka,H2O is the mass transfer coefficient (cm / hr) across the air layer for H2O, and u is the wind speed at a 10 m reference height (m/s).

Da,PAH = (0.001* (Temp1.75) * ( 1/28.97 + 1/ mol wgt.)0.5) / (20.10.33 + Mol Vol0.33)2

= cm2 s-1

Da,H2O = (0.001* (Temp1.75) * ( 1/28.97 + 1/18)0.5) / (20.10.33 + 180.33)2

= cm2 s-1


Effect of temperature on flux

Effect of Temperature on Flux

Net Flux = kol * (Cw – Ca/H*)

---- ----

1 / kol = 1 / kw + 1 / kaH*

----

Gross fluxes:

Volatilization: Water to air transfer

Fvolatilization = kol * Cw

Absorption: Air to water transfer

Fabsorption = kol * Ca/ H*


Effect of temperature on flux kol

Effect of Temperature on Flux – Kol

Net Flux = kol * (Cw – Ca/H*)

----

1 / kol = 1 / kw + 1 / kaH*

--------

Temperature affects on Kol:

Viscosity and Density of water

Diffusivity of HOC in water

Sc of HOC

Sc of CO2

Diffusivity of H20 and HOC in air

plus Henry’s law associated effects on Kol (via air side)


Effect of temperature on flux henry s law

Effect of Temperature on Flux – Henry’s Law

Net Flux = kol * (Cw – Ca/H*)

---- ----

1 / kol = 1 / kw + 1 / kaH*

----

Gross fluxes:

Volatilization: Water to air transfer

Fvolatilization = kol * Cw

Absorption: Air to water transfer

Fabsorption = kol * Ca/ H*


Temperature dependence of henry s law

Temperature dependence of Henry’s Law


Indirect effects of temperature on a w flux

Indirect Effects of Temperature on A/W Flux

Net Flux = kol * (Cw – Ca / H*)

----

Temperature dependence of ambient atmospheric concentrations (and G/P distributions)

Gross fluxes:

Volatilization: Water to air transfer

Fvolatilization = kol * Cw

Absorption: Air to water transfer

Fabsorption = kol * Ca/ H*

log Cair = ao + a1 / T + a2 sin(wd) + a3 cos(wd)

after Zhang et al. 1999

Note: possible (and likely) interdependence of T and wind direction


Vapor exchange fluxes and estimated fluxes of hocs

Vapor exchange fluxes and estimated fluxes of HOCs

_____________________________________________________________________________

RegionFlux (ng/m2 day)PeriodReference

_____________________________________________________________________________

PCBs

Lake Superior+19, +141AugustBaker and Eisenreich, 1990

1986+63AnnualJeremiason et al., 1994

1992+13 AnnualJeremiason et al., 1994

+8.3AnnualHornbuckle et al., 1994

Lake Michigan+8.5-22AnnualSwackhamer and Armstrong,1986

+244AnnualStrachan and Eisenreich,1988

N. Lake Michigan+34 (24-220)AnnualHornbuckle et al., 1995

Lake Ontario+81AnnualMackay, 1989

Green Bay+13-1300June-OctAchman et al., 1993

+111 avgJuneAchman et al., 1993

Open ocean -40June-AugIwata et al., 1993

+5Nov-MarIwata et al., 1993

Nearshore Chicago-32 to +59whole year Zhange et al. 1999

northern Chesapeak BayNelson et al., 1998

PAHs

Baltimore Harbor (1996-1997)Bamford et al., 1999

fluorene+2200

phenanthrene -140(-140 to 7500)

pyrene+590

Southern Chesapeake Bay

fluorene+570FebruaryDickhut and Gustafson, 1995

phenanthrene -570

fluoranthene -140

pyrene -50

Table from Nelson, 1996


Error analysis of gas exchange calculations

Error analysis of Gas Exchange Calculations

Bamford et al. (1999) predicted gas exchange in Baltimore Harbor using Temperature dependent Henry’s Law values, and measured air and water concentrations, along with ambient meteorological parameters (wind speed, water Temperature, etc).

Using estimated uncertainties of 40%, 20%, 20% and 10% for Kol, Cw, Ca, and H resulted in propagated errors of between 43% and 910% of the calculated fluxes. Errors were larger when the fluxes were smallest (i.e. near equilibrium or nearly 0 ng/m2 day net flux), with average error of 64%. Most of the error associated with the fluxes are related to the estimation of Koldue to the effect of wind speed on the kw.


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