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Compliance monitoring pilot for Marpol Annex VI
Inversion Tools
Ari Karppinen, Res. Man., FMIJari HärkönenJuha NikmoTimo MäkeläJukka-Pekka JalkanenLasse Johansson
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Integrated use of models and data
Monitoring Models Satellite
Goal: operational system taking into account all sources of information
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ECMWFHIRLAMRCR
HARMONIE LAPS
Weather prediction models Dispersion models - long-
range, regional
Dispersion and effects models
– urban, local
PALM FINFLO,FLUENT(CFD),
DNS-code development
SILAM LRT, meso-
scale, radioactivity , pollen
Modelling system - FMI
OSPM (NERI), street canyon
UDM-FMI, urban
CAR-FMI, roadside
Aerosol process models: UHMA (U Helsinki, FMI)
MONO32 (U Helsinki, Stadia)
SALSA(UH,UKU,FMI)
SILAM-APMs
MPP-FMI, Meteorological pre-
processing model
ESCAPE, chemical accidents
BUOYANT, fires
EXPAND (FMI, YTV)
population exposure
FMI- EnFuser
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Jalkanen, J.-P., L. Johansson, and J. Kukkonen, 2016. A comprehensive inventory of ship traffic exhaust
emissions in the European sea areas in 2011. Atmos. Chem. Phys., 16, 71–84, 2016. http://www.atmos-chem-
phys.net/16/71/2016/acp-16-71-2016.pdf.
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Local inversion: in theory trivial..but..
13/12/2016 5
u-vS
rM0
vM-vS
SL r2(tS2) r0(tS0) r1(tS1)=rM0 + vM tS1
GL
vM rS(tS0) vS
𝐶 𝑥𝑤,𝑦𝑤, 𝑧 = 𝑓1 𝑥𝑤 ∗ 𝑓2 𝑥𝑤 *𝑓3 𝑥𝑤 , where (2a)
𝑓1 𝑥𝑤 =𝑄
2∗𝜋∗𝜎𝑦 𝑥𝑤 ∗𝜎𝑧 𝑥𝑤 ∗ 𝒖−𝒗𝑺 (2b)
𝑓2 𝑥𝑤 = 𝑒𝑥𝑝 −0.5 ∗ 𝑦𝑤 𝑥𝑤
𝜎𝑦 𝑥𝑤
2
(2c)
𝑓3 𝑥𝑤 = 𝑒𝑥𝑝 −0.5 ∗ 𝑧−ℎ
𝜎𝑧 𝑥𝑤
2
+ 𝑒𝑥𝑝 −0.5 ∗ 𝑧+ℎ
𝜎𝑧 𝑥𝑤
2
(2d)
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First complication: measurements (concentration & met)
13/12/2016 6
..part of the problemcan be in most cases handledwith simple filtering/smoothing
The “modeled” signal is the filtered line from which discrete values are
sampled at raw observation times. The maximum of the signal is monitored 2016-01-27 17:38:20.
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..the major problem : turbulent flow /meteorology is hard to handle in small time sclaes like this
• Dierect analytical solution does not
genarally work:
Possible solutions:
1.The ”normal” one : use tracer(s) to eliminante (?!) the problems with disperson
2. Formulate the problem as an optimisation problem
• Minimize ~|| measured- modeled ||
• For time of transform & and speed & heading of the ship
• Solve ship emsissions (least squares) basedon equations (2*)
13/12/2016 7
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
1.10.1.12.31.1. 1.4. 1.6. 1.8. 1.10.1.12.31.1.
S %
Marraskuu 2015-2016
2.6.2016 1.76%
1,76
0,0
0,5
1,0
1,5
2,0
09
01
80
27
03
60
1.10. 1.12. 31.1. 1.4. 1.6. 1.8. 1.10. 1.12. 31.1.
S-%
Tu
ule
n s
uu
nta
⁰
Utö, 1 year ”standard” method
wind dir ⁰ polttoaineneen-S %
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Utö test (with NOx )
Constraints:
• Ship speed = ShipSpd(STEAM2) 2 m/s
• Ship heading = ShipDir(STEAM2) 30o
All dirty details given in the final report.. here we just jump to one of the conclusions
13/12/2016 8
~FAC5
N=66FAC2 ~50%>FAC5~15 %
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Conclusions from the Utö excersise
13/12/2016 9
Simple local scale modelling toolstogether with ”simple” optimisationroutines can be used for INDICATIVEemsssion inversion EVEN without the knowledge/measurements on CO2
Utö is quite an ideal case for modeller..but.. still some problems in asessingthe plume rise & mixing height & long distance from ship route to monitorslead to relatitvely high uncertainty on the final optimisation
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Final conclusions
• The local scale inversion-tool can givean (strong) indication on ships notobeying the regulations
• The method is unfortunately verysensitive to availability of detailedmeteorological information, so canwork well only in locations wherethese good quality measurements areavailbale (like Utö)
The method also requires smoothterrain/no major obstacles: complexterrain would require methods which areseveral orders of magnitude heavier
13/12/2016 10
These type of existing measurementsshould always be utilized as an additional info to other methods:
modelling tools required/developed in CompMon are easy and cheap to install/test
Naturally the ”official” solution:
adding a parallel CO2 measurement as close as possible to the SO2/NOX
measurements is always the prefereedoption/ uncertainty with this methodespecially with NOx should however becarefully asessed
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