Adaptive air quality management through operational sensitivity forecasting
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Adaptive air quality management through operational sensitivity forecasting
Amir Hakami and Matthew Russell
3rd IWAQFR WorkshopDecember 1, 2011
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3rd IWAQFR December 1, 2011
Air quality management
• Models are used for long-term or short-term predictions– Long-term planning (e.g. strategy design)– Short-term prediction (public warning, etc)
• Can we do short-term planning?
Is modifying emission behavior in the short term (a day) worth the effort?
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3rd IWAQFR December 1, 2011
Influence on Canadian mortality
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3rd IWAQFR December 1, 2011
Variability of influence on Canadian mortality
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3rd IWAQFR December 1, 2011
Adaptive but targeted emission modification
• In cases where forecast leads to control measures, these efforts usually pay limited attention to effectiveness
• Sensitivity analysis can guide short-term measures.
Can forecast pollution episodes be averted?
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3rd IWAQFR December 1, 2011
Adjoint or backward sensitivity analysis
Inputs/Sources Outputs/Receptors
jxy
xyi
yx
• Backward analysis is efficient for calculating sensitivities of a small number of outputs with respect to a large number of inputs. Forward analysis is efficient for the opposite case.
• Complementary methods (Source-based vs. Receptor-based), each suitable for specific types of problems.
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3rd IWAQFR December 1, 2011
CMAQ Adjoint simulations
- Continental domain- 36 km resolution- 13 vertical layers
- Summer of 2007
- Gas-phase CMAQ with adjoint sensitivity
- SAPRC-99 chemistry
- Sensitivities of ozone to NOx emissions
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3rd IWAQFR December 1, 2011
Baltimore
0 10 20 30 40 50 60 70 80 90 100
110
0
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3rd IWAQFR December 1, 2011
Methodology
• 16 days in summer• Sensitivity of Baltimore to each source
calculated• Top 10% locations (sources) modify
their emissions by the following:– Scenario 1: 5% point + 8% mobile– Scenario 2: 10% point + 15% mobile– Scenario 3: 15% point + 25% mobile
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3rd IWAQFR December 1, 2011
Baltimore 8-hr max ozone
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3rd IWAQFR December 1, 2011
Baltimore 8-hr max ozone
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3rd IWAQFR December 1, 2011
Baltimore 8-hr max ozone
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3rd IWAQFR December 1, 2011
Spatio-temporal selectivity
• Adding temporal sensitivities
• Top 5% locations (sources) modify their emissions by the following:– Scenario 1: 5% point + 8% mobile– Scenario 2: 10% point + 15% mobile– Scenario 3: 15% point + 25% mobile
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3rd IWAQFR December 1, 2011
Baltimore: hourly emission modification
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3rd IWAQFR December 1, 2011
Baltimore: hourly emission modification
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3rd IWAQFR December 1, 2011
AQHI in Toronto
0 1 2 3 4 5 6 7 8 9 10 11 120
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AQHI (summer) = 10*(0.101*NO2+0.104*O3)/12.8
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3rd IWAQFR December 1, 2011
AQHI: hourly emission modification
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3rd IWAQFR December 1, 2011
Challenges/Issues
• The model predicts some improvement, but how can you confirm that?
• Emission behavior modification on a fluctuating basis is not desirable
• At moderate levels of aggressiveness, impacts are not sufficiently large to consistently move the receptor into attainment
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3rd IWAQFR December 1, 2011
Next Steps
• Should be done at high resolution!
• How different the effectiveness is for various receptors?
• Impact of regional planning
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3rd IWAQFR December 1, 2011
Conclusions/thoughts
• Short-term emission behavior modification can have sizeable impact; however, the impact is not large enough to significantly increase attainment likelihood
• Adjoint sensitivities can guide the short-term, operational management
• Temporal selectivity is an important component of the increased effectiveness.
• The method may be more effective for longer forecast windows (> 1 day)
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3rd IWAQFR December 1, 2011
Comments, questions?