The Transport and Deposition of Dioxin to Lake Michigan: A ... · The Transport and Deposition of...
Transcript of The Transport and Deposition of Dioxin to Lake Michigan: A ... · The Transport and Deposition of...
The Transport and Deposition of Dioxinto Lake Michigan: A Case Study
Dr. Mark CohenNOAA Air Resources Laboratory
SSMC3, R/ARL, Room 33161315 East West Highway, Silver Spring MD, 20910
ph: 301-713-0295 x122; fax: [email protected]
Presentation at:
“Using Models to DevelopAir Toxics Reduction Strategies:Lake Michigan as a Test Case”
Milwaukee, Wisconsin, Nov 8-9, 2000
The views expressed in this presentation are those of the author, and do notnecessarily represent the views of the Air Resources Laboratory, NOAA,
the Department of Commerce, or the United States Government.
The Transport and Deposition of Dioxinto Lake Michigan: A Case Study
Presentation Outline
ØØ Policy Making Contextand the potential role of models
ÙÙ regarding atmospheric deposition, What Do We Need to Know?
and How Well Do We Need to Know It?
ÚÚ Atmospheric Deposition of Dioxin to Lake Michigan
ÛÛ Uncertainty Analysis
ÜÜ Conclusions and Recommendations
POLICY MAKING CONTEXT
1. EFFECTS?Harmful effects on wildlife, public health?[what is the exposure? consequences of thisexposure?]
2. CAUSES ?
What is the relative contribution ofdifferent loadings pathways contributing tothe harmful effects?
And, for any given significant loadingpathway, what are the relativecontributions of different sources?
(THIS TALK: ATMOSPHERIC PATHWAY DETAILS)
3. COSTS ?
What are the technical options involved inreducing or eliminating the contributionsfrom major sources?
What are the costs to implement theseoptions?
-- Decision Making: Need Info in All Three AreasOr, it doesn’t necessarily do you much good to have precise informationin one area, if one or more of the other areas remain very uncertain
-- How much do we know regarding these questionsfor dioxin in Lake Michigan?
THE ROLE AND POTENTIAL VALUE OF MODELS
ëë MODELS are mathematical/conceptual descriptions of real-world phenomena
è Necessarily a simplification; the real world is very complicated
è Key processes must be sufficiently characterized
ëë MODELS are POTENTIALLY VALUABLE for:
è Examining different large-scale scenarios that cannot be easily testedin the real world (e.g., different emissions reduction scenarios).
è Interpreting measurements
è Filling in spatial and temporal gaps between measurements
ëë MODELS are a TEST of our KNOWLEDGE:
è Attempts to synthesize everything important about a given system.
è If a model fails, we don’t understand enough about the system.
ëë MODELS are USED IN developing approximate answers inALL THREE fundamental policy information areas
0 - 0.10.1 - 2525 - 5050 - 7575 - 100100 - 300300 - 50005000 - 335000
Areal Density of Dioxin Emissions (µgrams TEQ/km²-yr)
No Data Available
N
500 0 500 1000Miles
500 0 500 1000Kilometers
Figure 1: Total Dioxin Emissions for 1996
No Data Available
Contribution to Deposition(µgrams TEQ/km²-yr)
Mid-Range Estimate of the Contribution to 1996 Atmospheric Deposition of Dioxin to Lake Michigan (µgrams TEQ/km²-yr)
500 0 500 1000 Miles
500 0 500 1000 Kilometers
N
0 - 0.010.01 - 0.10.1 - 11 - 1010 - 100100 - 10001000 - 7000
Estimates of the Percent of Lake Michigan Dioxin Loadings
Attributable to the Atmospheric Deposition Pathway
Study Fraction of Current Loadings Contributed
Through the Atmospheric Pathway
Cohen et al. PCDD/F TEQ: 50-100
(central estimate ~ 88)
Pearson et al. PCDD: 50-100
PCDF: 5-35
ë Cohen, M., et al., 1995. Quantitative Estimation of the Entry of Dioxins, Furans, andHexachlorobenzene into the Great Lakes from Airborne and Waterborne Sources. Flushing, NY: CBNS, Queens College. Final Report to the Joyce Foundation.
ë Pearson, R.F., D.L. Swackhamer, S.J. Eisenreich, and D.T. Long (1998). “AtmosphericInputs of Polychlorinated Dibenzo-p-dioxins and Dibenzofurans to the Great Lakes:Compositional Comparison of PCDD and PCDF in Sediments.” J. Great Lakes Research24(1): 65-82.
What Do We Need to Know, and.How Well Do We Need to Know It?
For most policy considerations, the exact contributions of individual sourcesdo not need to be known.
It is generally sufficient to know about:
ëë The geographical extent of the problem
C relative impact of local, regional, national, continental,and/or global sources
C don’t need exact answers, e.g., if 70% or 50% of thecontributing air sources arise from within 100 km of theLake – the policy response will be similar in either case.
C Only if the estimates are grossly incorrect will policydeliberations be seriously affected.
ëë Which source categories are the most significantcontributors?
C don’t need exact answers; e.g., it does not matter that muchwhether municipal solid waste incinerators contribute 20% or40% to the deposition – the policy response will likely bevery similar.
C Again, the estimates will be of little or no use only if they areextremely inaccurate.
Figure 10. Percent of Total Emissions or Total Deposition of Dioxin (1996)Arising from Within Different Distance Ranges From Each of the Great Lakes
0 - 100 - 200 - 400 - 700 - 1000 - 1500 - 2000 - 2500 - > 3500100 200 400 700 1000 1500 2000 2500 3500
Distance Range from Lake (km)
Emissions Deposition
0
10
20
30
40
50
Lake Michigan
0
10
20
30
40
50
Lake Erie
0
10
20
30
40
50
Lake Superior
0
10
20
30
40
50
Lake Huron
0
10
20
30
40
50
Lake Ontario
Figure 12. Contriibution of Different Source Sectors to Atmospheric Deposition of Dioxin( pg TEQ deposition / km2 ) / ( person - year )
(Each country's annual deposition flux contribution amount normalized by their total population)
United States Canada
"incin" = waste incineration; "metals" = metallurgical processing; "fuel" = fuel combustion
incin metals fuel
Emissions Sector
0.00.10.20.30.40.50.60.7
Lake Erie
incin metals fuel
Emissions Sector
0.00.10.20.30.40.50.60.7
Lake Michigan
incin metals fuel
Emissions Sector
0.0
0.1
0.2
0.3
0.4
0.5
Lake Superior
incin metals fuel
Emissions Sector
0.00.10.20.30.40.50.6
Lake Huron
incin metals fuel
Emissions Sector
0.0
0.2
0.4
0.6
0.8
Lake Ontario
incin metals fuel
Emissions Sector
0.00.10.20.30.40.50.60.7
Great Lakes Average
Major Sources of Uncertaintyin Atmospheric Dioxin Modeling
ëë Emissions Inventory
ëë Meteorological Data Used as Input to the Model
ëë Atmospheric Dispersion Simulation
ëë Atmospheric Fate Processes
ëë Vapor/Particle Partitioningëë Chemical Transformationsëë Wet Depositionëë Dry Deposition
Other than uncertainties in the emissions inventory, the dry deposition modeling
methodology is probably the most important factor influencing the results...
1 2 3 4 5 6
Dry Deposition Algorithm
0.0
0.5
1.0
1.5
2.0
2.5
to D
epo
siti
on
wit
h C
han
ged
Par
amet
er V
alu
e
Rat
io o
f D
epo
siti
on
wit
h D
efau
lt P
aram
eter
Val
ue
Dry Deposition Algorithms: 1 - HYSPLIT_4 default; 2 - vpfin5f; 3 - Slinn & Slinn, 1980, no RH correction, for water, vpfin5f for other processes4 - EPA "best method" (ADOM-II) for particles, vpfin5f for gases; 5 - Williams (1982) for particles over water, vpfin5f for other processes;6 - same as 3, but with RH correction, i.e., Slinn and Slinn (1980) with RH correction for particles over water, vpfin5f for other processes
Sensitivity Analysis for Model Estimated Deposition to Lake MichiganFactor Varied: Dry Deposition Algorithm (note: Default = #6)
Continuous Year Long Source of 2,3,7,8-TCDD at Center of Modeling Domain
What are the most important factors in the simulation uncertainty
(other than the dry deposition methodology and the emissions inventory)?
wetr_
tcdd_c
ente
r
wetr_
tcdd_m
ilw
wetr_
tcdd_s
f
times
tep
phot_tc
df_sf
partn
um
phot_tc
df_ce
nter
emit
freq
psd_t
cdd_m
ilw
phot_tc
dd_sf
phot_tc
dd_cen
ter
psd_t
cdd_s
f
phot_tc
df_m
w
phot_tc
dd_mw
psd_t
cdd_c
ente
r
phot_ocd
d_sf
height_
tcdd_c
ente
r
phot_ocd
d_cen
ter
height_
tcdd_m
ilw
phot_ocd
d_mw
Sensitivity Analysis Series
0%
5%
10%
15%
20%
Var
iatio
n in
Par
amet
er
Rat
io o
f Var
iatio
n in
Res
ults
to
Source Locations: "center" = center of modeling domain (lat/long = 40,95); "sf" = San Francisco; "milw" = Milwaukee.Parameters: "wetr" = in-cloud particle wet deposition; "phot" = photolysis rate; "psd" = ambient particle conc. (affects V/P partitioning);
"height" = height of source; "timestep" = model time step; "partnum" = # of puffs simulated; "emitfreq" = puff emissions frequency.
Summary of Sensitivity Analyses (for Factors Other than Dry Deposition Methodology or Emissions)
0 - 100100 - 200
200 - 400400 - 700
700 - 10001000 - 1500
1500 - 20002000 - 2500
2500 - 3500
Distance Range from Lake (km)
0%
10%
20%
30%
40%
50%
60%
Per
cen
t o
f T
ota
l Dep
osi
tio
n
drydep = 1drydep = 2
drydep = 4drydep = 5
drydep = 6no photolysis
particle wet deposition x 4
Dry Deposition Algorithms: 1 - HYSPLIT_4 default; 2 - vpfin5f; 3 - Slinn & Slinn, 1980, no RH correction, for water, vpfin5f for other processes4 - EPA "best method" (ADOM-II) for particles, vpfin5f for gases; 5 - Williams (1982) for particles over water, vpfin5f for other processes;6 - same as 3, but with RH correction, i.e., Slinn and Slinn (1980) with RH correction for particles over water, vpfin5f for other processes
Effect of Different Deposition and Fate Methodologieson Percent Dioxin Deposition Contribution
to Lake Michigan From Different Distance Ranges
Mu
nicip
al Waste In
cineratio
n
Iron
Sin
tering
Med
ical Waste In
cineratio
n
Cem
ent K
ilns B
urn
ing
Haz W
aste
Backyard
Waste B
urn
ing
Seco
nd
ary Co
pp
er Sm
elting
Seco
nd
ary Alu
min
um
Sm
elters
Resid
ential W
oo
d C
om
bu
stion
Mo
bile S
ou
rces
Utility C
oal C
om
bu
stion
Electric A
rc Fu
rnaces
Hazard
ou
s Waste In
cineratio
n
Sew
age S
lud
ge In
cineratio
n
Cem
ent K
ilns N
ot B
urn
ing
Haz W
as
Pu
lp-P
aper: H
og
Fu
el / Slu
dg
e Co
Resid
ential F
uel C
om
bu
stion
Ind
ustrial W
oo
d C
om
bu
stion
Grey Iro
n F
ou
nd
ries
Pu
lp-P
aper: K
raft Black L
iqu
or
Resid
ential O
il Co
mb
ustio
n
Co
mm
ercial Fu
el Co
mb
ustio
n
Seco
nd
ary Lead
Sm
elting
Base M
etal Sm
elting
Ind
ustrial F
uel C
om
bu
stion
Ag
ricultu
ral Fu
el Co
mb
ustio
0%10%20%30%40%
Pe
rce
nt
of
To
tal
drydep = 1drydep = 2
drydep = 4drydep = 5
drydep = 6photolysis = 0
particle wet dep x 4
Dry Deposition Algorithms: 1 - HYSPLIT_4 default; 2 - vpfin5f; 3 - Slinn & Slinn, 1980, no RH correction, for water, vpfin5f for other;4 - EPA "best method" (ADOM-II) for particles, vpfin5f for gases; 5 - Williams (1982) for particles over water, vpfin5f for other;6 - same as 3, but with RH correction, i.e., Slinn and Slinn (1980) with RH correction for particles over water, vpfin5f for other.
Results from Different Sensitivity Simulations (using 28 standard source locations)on Emissions Sector Contributions to Lake Michigan Atmospheric Dioxin Deposition (1996)
Mu
nicip
al Waste In
cin.
Iron
Sin
tering
Med
ical Waste In
cin
Cem
ent K
ilns B
urn
ing
HW
Backyard
Waste B
urn
ing
Seco
nd
ary Co
pp
er Sm
elting
Seco
nd
ary Alu
min
um
Sm
elti
Resid
ential W
oo
d B
urn
ing
Mo
bile S
ou
rces
Utility C
oal C
om
bu
stion
Electric A
rc Fu
rnaces
Hazard
ou
s Waste In
cin.
0%
10%
20%
30%
40%
Per
cen
t o
f T
ota
l
drydep = 1
drydep = 2
drydep = 4
drydep = 5
drydep = 6
photolysis = 0
particle wet dep x 4
Dry Deposition Algorithms: 1 - HYSPLIT_4 default; 2 - vpfin5f; 3 - Slinn & Slinn, 1980, no RH correction, for water, vpfin5f for other;4 - EPA "best method" (ADOM-II) for particles, vpfin5f for gases; 5 - Williams (1982) for particles over water, vpfin5f for other;6 - same as 3, but with RH correction, i.e., Slinn and Slinn (1980) with RH correction for particles over water, vpfin5f for other.
Results from Different Sensitivity Simulations (using 28 standard source locations)on Emissions Sector Contributions to Lake Michigan Atmospheric Dioxin Deposition
(for top 12 contributing sectors, 1996)
OntarioErie
SuperiorHuron
Michigan
1
10
100
1000
Dep
osit
ion
(g T
EQ
/yea
r)
central estimate range due to emissions uncertainty fate variations
Figure 3. Ranges in Estimated Deposition Arising fromUncertainties in Emisisons and Fate Simulation
What could we do to improve theaccuracy of atmospheric loadingestimates?
ë More information on non-atmospheric loadingpathways needs to be collected in order to moreaccurately place the atmospheric contributions intheir proper context.
ë Ambient monitoring for dioxin must be increasedin the Great Lakes region. This will allow modelevaluation and independent semi-empiricalestimates of atmospheric deposition to be made.
ë Additional efforts to improve the accuracy ofemissions inventories – including timely updates– must be made. Timely (e.g., annual) updates forat least the largest sources in the inventory wouldbe extremely helpful, because often, these largestsources tend to drive the analysis. If they can bebetter characterized, the accuracy of the overallanalysis can be greatly (and relatively easily)improved.