1. NO 2 and SO 2 Over the eastern USA: Policy relevant science Presented at the OMI Science Team Meeting by Russell R. Dickerson T. Canty, J. Hains, H. He, N. Krotkov, R. Salawitch, J. Stehr, K. Vinnikov Univ. Maryland and NASA/GSFC March 11, 2014 UMD/URF Cessna Photo by J. Stehr

2. Overview The main air quality issues in the US are O 3, fine particulate matter (PM2.5), and haze. The main precursors are NO 2, SO 2, and VOC’s Emissions for SO 2 are monitored and well known; the lifetime (conversion to sulfate) is not. Neither emissions nor lifetimes are well quantified for NO 2 and VOC’s. Using OMI obs in combination with in situ measurements and models has led to advances in our ability to understand and improve air quality. 2

3. An experiment of opportunity: SO 2 In 2010, Maryland implemented the “Healthy Air Act” 3

4. Power Plant Emissions in Maryland and surrounding states. The Healthy Air Act

5. 5 Aircraft profiles show great variability, but a significant SO 2 decrease.

6. Ambient SO 2 (ppb) at Beltsville, MD Daily cycle ~factor of two. Seasonal cycle ~factor of two. Dramatic decreases after Healthy Air Act. Maxima in mid day when PBL entrains plumes from aloft. Surface SO 2 reflects emissions reductions.

7. OMI SO 2 2005-2009

8. OMI SO 2 2010-2012

9. Difference in DU

10. Copyright © 2010 R.R. Dickerson10

11. Interim conclusions In situ and remote obs in agreement The good news: Local emissions reductions have an immediate and profound impact on local concentrations of SO 2. The lifetime of SO 2 can be estimated from the column content and known emissions flux. Lee, Hains, et al., JRG (2011). CMAQ (EPA regulatory model) overestimates the lifetime, but better representation of clouds improved measurement-model agreement. Loughner et al., Atmos. Environ. 2011; in review 2014. 11

12. The bad news: PM2.5 only fell modestly.

13. Last December, the Supreme Court heard arguments on the Cross-State Air Pollution Rule, CSAPR. NASA data went into an Amicus Brief.

14. Ongoing projects Helping CMAQ more accurately predict ozone. Quantifying bias in OMI SO 2 from clouds. BrO over oceans. Impact of increasing oil natural gas and production (fracking). 14

15. Canty et al., submitted. CMAQ modeled NO 2 high in cities low in countryside. Inspired by Castellanos et al., 2011.

16. Ready results If CB05 is modified to shorten the lifetime of alkyl nitrates and reduce mobile source NOx emissions then CMAQ and OMI agree better.

17. Conclusions Air Quality managers are using satellite data including OMI! The Healthy Air Act produced 80% reductions in SO 2 emissions in Maryland starting in 2010. OMI and ambient measurements of short lived (24 ± 10hr) SO 2 showed dramatically and immediately AQ improvement. Longer lived (~10 d) PM2.5 improved much less so. Surface SO 2 is lower on overcast days than sunny days – this multiphase removal may lead to a bias in OMI obs. 17

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19. Change in SO 2 column from OMI Using Fioletov’s sub-pixel resolution product. Orange ~ 0.35 DU decrease ~ 3.5 ppb in 1000 m

20. More SO 2 on sunny days; site at 500m. For Hao’s paper – bias? We need to compare the ratio of the ratio in situ SO2 to OMI SO2 Thanks to Jim Schwab SUNY

21. Is there an impact from cloud cover?

22. Mean, ppb STD, ppb Decay time, hours All times 2.53.510.2 Night, Cold season 3.64.411.0 Night, Warm season 1.42.27.1 Day, Cold season 3.54.310.5 Day, Warm season 1.52.17.4 R(t)=exp(-t/T) - is Lag-Correlation function. t - is Lag, T - is decay time or Scale of temporal correlation.

23. What is the lifetime of SO 2 ? 23 The decay time (  decay ) reflects the sum of actual losses and dispersion. Using CO for dispersion time (~17 hr << chemical lifetime) we can estimate the lifetime for SO2 all losses including oxidation, wet and dry deposition. The implied lifetime for SO 2 is 24 (±10) hr at Pinnacles (elevation 500 m).

24. Change in SO 2 column from OMI Using Fioletov’s sub-pixel resolution product. Orange ~ 0.35 DU decrease ~ 3.5 ppb in 1000 m

25. Mean, ppb STD, ppb Decay time, hours All times 1532517.0 Night, Cold season 1652615.5 Night, Warm season 1432317.6 Day, Cold season 1602516.9 Day, Warm season 1422316.3 R(t)=exp(-t/T) - is Lag-Correlation function. t - is Lag, T - is decay time or Scale of temporal correlation.

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