predicting ambient concentrations of sulphur dioxide · pdf filepower stations • majority...
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Predicting AmbientConcentrations ofSulphur Dioxide
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Sulphur Dioxide in the UK
• 65% of total emissions are from coal-firedpower stations
• Majority of the rest is from other industrialpoint sources
• Traffic is <5% of total & falling
• Contrast with far wider range of sources ofparticulates and nitrogen oxides
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Impacts of Sulphur DioxideEmissions in UK
• Annual averages in all locations are low (<5ppb) - even beside large point sources
• Short-term peak concentrations can exceedAQS objectives locally around sources
• Most onerous is objective of < 35 exceedances of100 ppb 15 minute concentration
• Elsewhere, AQS objectives are alreadyachieved - ahead of 2004/5 deadlines
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Predicting Sulphur Dioxideconcentrations
• Only specific locations close to pointsources are of concern
• Predictions of interest are for short-termpeak concentrations from these pointsources
• Prediction example: Coal-fired powerstations
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Ground level concentrations aredetermined by:
• Emission rate of pollutant
• predictable, relatively constant
• Rate of dispersion of plume throughatmosphere
• depends on stack exit conditions, atmosphericconditions, (topography, buildings)
• atmosphere is unpredictable, constantlyvarying
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Coal Station Plume Dispersion
• Extensive dilution of plume in transit
• Very low ground level concentrations for vastmajority of time (annual average SO2 ~1-2 ppb)
• Very occasional high peaks during adversemeteorological conditions
• 99.9th percentile (worst 8 hours of year) can be>100 ppb
• Maximum concentrations typically occur 3 to 6 kmfrom stack in very localised zones
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When do power stationscause high concentrations?
• Low boundary layers (300 - 500m) withsome convection
• Some highly convective situations
• Sometimes in strong winds ( > 8 m/s )
• Low boundary layers (300 - 500m) withsome convection
• Some highly convective situations
• Sometimes in strong winds ( > 8 m/s )
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Short-term sulphur dioxide peaks
• Peaks are very sharp
• Peak events are of short duration (2 hours atmost)
• Peak events are localised, not regional
• Concentrations before and after peak oftenclose to annual average
• Presents a major challenge for prediction
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Predicting Plume Dispersion
• General correlation of monitored eventswith specific meteorological parameters
• But too random for individual short-termpeak event predictions
• Prediction requires some form ofdispersion modelling (e.g. ADMS, Aermod)
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Challenges for modelling short-term peak concentrations
• Dispersion models are most accurate forlong-term mean ensemble predictions
• Good agreement of cumulative percentilepredictions with monitoring data - for fullyear
• But the correspondence between individualhourly predictions is close to random
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Measured versus Predicted 15-min SO2 Percentile Concentrations, Leeds Meteorology
0
50
100
150
200
250
0 50 100 150 200 250 300 350
Measured Concentration, ppb
Pre
dic
ted
Co
nce
ntr
atio
n, p
pb
Cliffe
Smeathalls Fm
Temple Hirst
N Featherstone
Gateforth
Sherburn
N Howden
Womersley
1:1
Concentration distributionsYorkshire 1999
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ADMS Predictions
Station
ESDC monitor
TXU monitor
-6000 -4000 -2000 0 2000 4000 6000
-6000
-4000
-2000
0
2000
4000
6000
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Trial “Protocol”
• Trial “Protocol” for predicting sulphurdioxide dispersion by power industry in 1998
• Scheme formed part of IPC Improvementconditions set by Environment Agency
• Objective was to investigate:
• whether a predictive management scheme couldproduce a reduction in numbers of EPAQS 100ppb exceedances
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Basis of Protocol
• Protocol based on modelled predictions of“event days” using forecast Met data
• Event day = a day in which an EPAQSexceedance occurred
• Avoids need to predict exact hour of event
• Avoidance action taken through entire day
• Would extended one day “action window”sufficiently reduce overall uncertainty?
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Protocol Features
• Complex system - in essence:
• Automated day ahead air dispersion modellingwith predicted Met data
• Check whether expected emissions would lead toAQS exceedance event
• If so, adjust planned plant operation/fuelling toreduce emissions & avoid event
• Full Year “Virtual” Trial carried out at 3 stationsduring 1998 as a JEP project
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Findings from Protocol
• Poor correlation between predicted & actualevents - and overprediction of event numbers
• Best available forecast data limits correct day-in-advance prediction of exceedances to about 50%
• Modelling uncertainty reduces correlation further
• False positive “action days” could be as high as65% of all days
• Extended one day “action window” does notsufficiently reduce overall uncertainty
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Conclusions of Protocol
• Protocol initiating action too often & too randomlyto be effective
• Costs of unnecessary load reduction would be veryhigh - similar to FGD - but FGD guarantees AQScompliance
• Makes a system of local air quality managementbased on forecast meteorological data andcomputer modelling very inefficient and expensive
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Outcome of Protocol
• Protocol clearly not BATNEEC
• More flexible approach needed to achieveSO2 AQS objectives in 2004/5
• Operators proposed a combination of new Alimits & “AQS Management Plan”
• Based on Annual Mean Ensemble modellingpredictions
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AQS Management Plan
• From modelling - derive AQS “envelope ofcompliance” scenario with annual emission “A limit”
• Establish monitoring sites at locations of modelledmaximum station impacts
• Annual Review compares modelling & monitoring andrefines future scenario predictions
• Iterative convergence on actual impacts in 2004/5
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AQS Management Plan Strategy
• Management of the overall “risk” (orprobability) of total no. of exceedances overthe full year
• Not the individual exceedance events
• May include assessment of differingseasonal risk
• Ultimate compliance is judged by monitoring
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Conclusions
• Hourly & daily predictions of concentrationsfrom point sources have low accuracy
• Largely inherent in model sensitivity &limited accuracy of Met parameter forecasts
• Very extreme events are even lesspredictable
• Best prediction & management of sulphurdioxide peak concentrations is on an AnnualMean Ensemble basis