# Presentation: 855769

## HYSPLIT Modelingin Phase II of theEMEP Mercury Modeling Intercomparison Study

- Dr. Mark Cohen
- Physical Scientist
- NOAA Air Resources Laboratory
- Silver Spring, Maryland

-  Presentation at the
**Expert Meeting on**
**Mercury Model Comparison**** **
**MSC-East, Moscow, Russia**
**April 15-16, 2003**

**Notes:**
Thank you ___________, it is a pleasure to have the opportunity to participate in this meeting.

## Slide 2

**Notes:**
From a given source, material is emitted a certain location into the air, and then begins to be blown downwind – in whichever direction the wind is blowing at the time.

Several things begin to happen -- all at the same time...

First, as the material is carried along by the wind, it becomes progressively diluted by dispersion (in all three directions, i.e., up-down, in the direction of the wind, and in the direction perpendicular to the wind direction...) ... Thus, the concentrations and impacts will generally decrease with distance from the source (Note: very close to the source, where the plume hasn’t yet hit the ground, can have very little impact -- sometimes the safest place to be is right next to the stack!).

Also, mercury is transformed among several different types, as we’ve discussed, and there are important droplet, cloud, and ice interactions... -- i.e., into a new chemical, which may be more or less toxic -- by reactions (for example, with hydroxyl radical) or the direct impact of sunlight hitting the molecule.  Different chemical are more or less vulnerable to transformation by these phenomena, and, there can be significant diurnal variations as well. At night, there’s no sunlight and there’s very little hydroxyl radical, so, the rates of transformation can be substantially lower than in the daytime.  While there is some knowledge about these phenomena, there are substantial uncertainties for most pollutants.

Finally, there are deposition (and re-emission) phenomena. 

For this project, we are using a model developed at NOAA called HYSPLIT, and we are using meteorology data archived by NOAA for use with the model.

This is the same model that is used for emergency response situations, in the event of accidental releases of dangerous substances, and has been extensively tested and evaluated.

Advantages to using a model like HYSPLIT are that 

	Because of its emergency response role, it is designed to be very fast; thus we can simulate long time periods, e.g., an entire year, or even multiple years. For the Great Lakes, this is important, as one would like information on annual averages or even longer time periods -- not just for a few days...

	Also, it’s a type of model that is specially designed to preserve information about source-receptor relationships – it tries to keep track of the individual impacts of each source in the inventory, so that not only do you get the total deposition to the Lakes, but also the individual contributions from each source to the each Lake.



## Slide 3

**Notes:**
This is way we typically run the HYSPLIT model.  We do a run for one particular source location, and we release puffs periodically throughout the year (e.g., once every 7 hours).  We follow each puff and keep track of the dispersion and the fate processes (deposition, transformation, etc.).  The model uses the physical-chemical properties of the pollutant we are modeling, of course. By the end of the year, we have a good idea of the average impact that a source in this location would have on the receptors of interest (e.g., the Great Lakes).

## Slide 4

## ?

**(for the same emissions speciation)**

- Impact of
- 5 gram/hr
- source

**?**
**=**

**5  x**

- Impact of
- 1 gram/hr
- source

- Source

**RECEPTOR**

## Slide 6

## Spatial interpolation

**RECEPTOR**

**Impacts from**
**Sources 1-3**
**are Explicitly**
**Modeled**

- 2

- 1

- 3

## Comparison of interpolated transfer coefficients to the Great Lakes with explicitly modeled transfer coefficients for 2378 TCDD and OCDD

**Comparison of interpolated transfer coefficients to the Great Lakes with explicitly modeled transfer coefficients for 2378 TCDD and OCDD**

## Slide 9

## Slide 10

## Transfer Coefficients for Hg are strongly influenced by the “type” of Hg emitted [Hg(II) has much greaterlocal impacts than Hg(0)]

- Transfer Coefficients for Hg are strongly influenced by the “type” of Hg emitted [Hg(II) has much greaterlocal impacts than Hg(0)]

## Slide 12

## =

**Impact of**
**Source**
**Emitting**
**30% Hg(0)**
**50% Hg(II)**
**20% Hg(p)**

**=**

- Source

**RECEPTOR**

**Impact of Source Emitting Pure Hg(0)**

**0.3 x**

**Impact of Source Emitting Pure Hg(II)**

**0.5 x**

**Impact of Source Emitting Pure Hg(p)**

**0.2 x**

**+**

**+**

## Slide 14

## Slide 15

## Do the emissions from one source affect the fate and transport of emissions from another source? 

**Do the emissions from one source affect the fate and transport of emissions from another source?**

***If***** interaction is important,**
**then sources not independent,**
**and Eulerian approach is needed**

## Why might the atmospheric fate of mercury emissions be essentially linearly independent?

**Hg won’t affect meteorology**
**  ****(can simulate meteorology independently, **
**	****and  provide results to drive model)**

**Most species that complex or react with Hg are generally present at *****much***** higher concentrations than Hg**

**Other species (e.g. OH) generally react with many other compounds than Hg, so while present in trace quantities, their concentrations cannot be strongly influenced by Hg**

**The current “consensus” chemical mechanism (equilibrium + reactions) does not contain any equations that are not 1****st**** order in Hg**

**Wet and dry deposition processes are generally 1****st**** order with respect to Hg**

**Why might the atmospheric fate of mercury emissions be essentially linearly independent?**

## Chemical Equilibrium and Reaction

- <date/time>

**Chemical Equilibrium and Reaction**
**Scheme for Atmospheric Mercury**

## Slide 19

**Notes:**
So far, we’ve just included the U.S. and Canadian emissions... you’ve seen this map... and we are trying to determine the impact of each source in the inventory... (Data from U.S. EPA and Environment Canada)

## Slide 20

## Slide 21

## Slide 22

## Slide 23

## Correlation Coefficient = - 0.03

***But daily avg conc not too far off...***

## Slide 25

## Slide 26

## In the first version of the HYSPLIT-Hg model used in this intercomparison, Hg(p) was assumed to be completely converted to dissolved Hg(II) whenever a particle becomes a droplet (e.g., above approximately 80% relative humidity); and dissolved Hg(II) assumed to become Hg(p) whenever the droplet dries out 

Hg(p) and Hg(II) were thus somewhat “equivalent” in the model

With this assumption, the model tended to underpredict Hg(p) and overpredict Hg(II), suggesting that the assumption of complete conversion was not valid.

However, it was encouraging to note that the model was getting approximately the right answer for the sum of the two forms of mercury (Hg(p) + Hg(II), representing the total pool of oxidized Hg in the atmosphere [see the following graphs]

**Hg(p) and Hg(II) were thus somewhat “equivalent” in the model**
**With this assumption, the model tended to *****under*****predict Hg(p) and *****over*****predict Hg(II), suggesting that the assumption of *****complete***** conversion was not valid.**
**However, it was encouraging to note that the model was getting approximately the right answer for the sum of the two forms of mercury (Hg(p) + Hg(II), representing the total pool of oxidized Hg in the atmosphere [*****see the following graphs*****]**

## Slide 28

## As a result of this observation, the model was re-run with the assumption that Hg(p) was not soluble.

- With this assumption, the results for Hg(p) and RGM were dramatically better. [These new results are what have been shown in this presentation, except for the immediately preceding RGM+Hg(p) graphs]
- The affect of changing this assumption had a negligible impact on Hg(0), as might be expected, given the generally very low concentrations of Hg(II) and Hg(p) relative to Hg(0).

## Some Concluding Notes

**The version of HYSPLIT-Hg used for these calculations represented a very early stage of development of the model.**

**Methodology assumes linear independence of sources; potential advantage that detailed source-receptor relationships can be estimated**

**The model has been changed significantly since these runs... (hopefully improved!)**

**It may be useful to reconsider some of the model evaluation metrics**

**Hg(p) solubility? **