Evaluating Emissions from Top-Down Observations

1. Evaluating emissions from top-down observations T. Ryerson – NOAA Chemical Sciences Division Motivation • Most inventories compiled from bottom-up estimates, where reported data from many individual sources are summed an enormous undertaking regulatory / legal “baggage” independent assessments are needed to evaluate accuracy • Emissions routinely change over all time scales hours - power plant loads - 30% days - urban mobile fleet mix - 50% years - response to control strategies - up to 80% • Accurate inventory input is critical for models (AQ, Carbon Cycle, Climate). } difficult, and slow, to update Evaluation methods and selected examples: Power plant NOx, petrochemical VOCs, ship BC, oil & gas CH4 (biogenics covered in other presentations today)

2. Top-down methods for inventory assessment – power plant NOx, SO2, CO, CO2 Emission ratios derived from field data “snapshots” agree with continuous emission monitoring systems (CEMS) at power plants W.A. Parish power plant plume 1-Hz aircraft data • Method accuracy: ratios are within ±5% of CEMS Peischl et al., JGR (submitted) absolute emissions within ±30% Ryerson et al., JGR (1998), Science (2001) - apply top-down approach to wider variety of sources with reasonable confidence W.A. Parish emission estimates compared NOx/CO2 SO2/CO2 observed slope 0.24 ±0.03 1.9±0.3 CEMS inventory 0.201.9

3. Top-down methods for inventory assessment – urban CO Urban CO emissions overestimated in US national inventory NOAA WP-3D aircraft data flight track scaled by measured CO NOAA Tall Tower Network data 457m level, KWKT tower, Waco, TX Additional data, analysis: A. Andrews, NOAA GMD Flexpart model: A. Stohl, NILU

4. Top-down methods for inventory assessment – urban CO Urban CO emissions overestimated in US national inventory NOAA WP-3D aircraft data flight track scaled by measured CO NOAA Tall Tower Network data 457m level, KWKT tower, Waco, TX Additional data, analysis: A. Andrews, NOAA GMD Flexpart model: A. Stohl, NILU

5. Top-down methods for inventory assessment – urban CO Urban CO emissions overestimated in US national inventory NOAA WP-3D aircraft data flight track scaled by measured CO NOAA Tall Tower Network data 457m level, KWKT tower, Waco, TX • field project inventory “snapshots” very similar to data from longer-term monitoring • top-down assessments are critical for both carbon cycle… - determining biospheric CO2 uptake using tall-tower data requires accurate knowledge of urban CO/CO2 signatures Additional data, analysis: A. Andrews, NOAA GMD Flexpart model: A. Stohl, NILU

6. Top-down methods for inventory assessment – petrochemical industry Errors in petrochemical VOC inventories persist in Houston Ethene flux from the Freeport petrochemical complex Measurement TCEQ inventory • Top-down methods point to large (factor of 30) underestimates of alkene emissions from petrochemical industries in Houston, TX in 2000 and 2006 Ryerson et al., JGR (2003); de Gouw et al., ES&T (2009); Washenfelder et al., JGR (submitted).

7. Top-down methods for inventory assessment – petrochemical industry Errors in petrochemical VOC inventories persist in Houston Ethene flux from the Freeport petrochemical complex Measurement TCEQ inventory • Top-down methods point to large (factor of 30) underestimates of alkene emissions from petrochemical industries in Houston, TX in 2000 and 2006 • These alkenes dominate the most extreme (>150 ppbv) ozone exceedances Ryerson et al., JGR (2003); de Gouw et al., ES&T (2009); Washenfelder et al., JGR (submitted).

8. Measured O3 Predicted O3 - Reported VOC emissions Predicted O3 - Corrected VOC emissions Top-down methods for inventory assessment – petrochemical industry Errors in petrochemical VOC inventories persist in Houston Gaussian plume model WRF-Chem 3D model 70 Sweeny Freeport Reported VOC emissions Increased alkene emissions 60 Ozone (ppbv) 50 40 30 3D model and analysis: Grell, Bao, McKeen - NOAA GSD and CSD -96.0 -95.8 -95.6 -95.4 longitude (deg) Gaussian model and analysis: M. Trainer - NOAA CSD • Models realistically simulate observations of ozone and other photoproducts only after reactive VOC emissions are made consistent with top-down assessments • State of Texas changed its emissions control strategy as a result of these findings

9. Top-down methods for inventory assessment - shipping Soot emission factors vary by a factor of 3 between marine engine types Soot emission dependencies on engine load and fuel quality are not well established Observed variability suggests using a single value in large-scale models will hamper the understanding of regionality of soot forcings and exposure Lack et al., GRL (2008).

10. Top-down methods for inventory assessment – oil & gas extraction NASA DC-8 data from ARCTAS April 2008 EDGAR 3.2 (2000 reporting year) US/Canadian Arctic total:0.4 Tg CH4/year 32 million cubic feet per day ~0.3 Tg CH4 per year) Prudhoe Bay plume Prudhoe Bay includes the world’s largest gas-handling facility, and is the single largest CH4 source in the EDGAR oil production sector

11. Top-down methods for inventory assessment – oil & gas extraction 2000 EDGAR inventories for 2000 and 2005 show 1000-fold differences for Prudhoe Bay Emissions unlikely did not not change substantially between these two years Working with EDGAR inventory teams to resolve these issues; a direct comparison is on hold 2005

12. Top-down methods for inventory assessment Conclusions • Tabulated inventories can be substantially in error, by factors of 2 to 10 or more, for many important chemical species from many of the largest source types. - continuing need for top-down, independent assessment of inventories • Bottom-up inventories appear to be accurate to ±25% or better only if directly measured monitoring data are routinely available (e.g., point source CEMS) • Errors are sufficiently large to confound development of scientifically sound control strategies based on anthropogenic emissions reductions. - relevant to air quality, carbon cycle, and climate issues