Quantifying CO 2 Emissions from Individual Coal Power Plants with OCO-2 Observations

Transcription

1 Quantifying CO 2 Emissions from Individual Coal Power Plants with OCO-2 Observations Ray Nassar 1, Tim G. Hill 2, Debra Wunch 3, Dylan B.A. Jones 3 and Tom Oda 4 1 Environment and Climate Change Canada, 2 University of Waterloo, 3 University of Toronto, 4 Universities Space Research Corporation IWGGMS, Helsinki, 2017 June 7

2 Bovensmann et al. (2010) proposed CarbonSat, a mission aiming to monitor CO 2 and CH 4 emissions from point sources like individual power plants CarbonSat would have a much wider swath (180 km or 240 km) than OCO-2 ( 10.3 km) but can their method still be used with OCO-2 in select cases? 1 m/s 13 MtCO 2 /yr Bovensmann et al. (2010), A remote sensing technique for global monitoring of power plant CO 2 emissions from space and related applications, Atmospheric Measurement Techniques, 3, We adapt the CarbonSat method and apply it to OCO-2 observations near coal power plants

3 Gaussian Plume Model (2-Dimensional) Equations adapted from Bovensmann et al. (2010, AMT 3, ): where V is the CO 2 vertical column in (g/m 2 ) at and downwind of the point source. The x-direction is parallel to the wind direction and the y-direction perpendicular to the wind direction. V depends on the emission rate F (in g/s), the across wind distance y (in m), wind speed u (in m/s), and the standard deviation in the y-direction, s y (in m). Here x is specified in m and x o =1000 m is a characteristic length so that the argument of the exponent is dimensionless. a is the atmospheric stability parameter, which we determine by classifying a source environment by the Pasquill-Gifford stability, which depends on the surface wind speed, cloud cover and time of day. Emissions are approximately linearly dependent on wind speed (i.e. a 10% error in wind speed contributes a ~10% error in emissions) Point sources in flat regions for short time (<3 h) and space scales (<50 km)

4 US EPA Data A wealth of publicly available GHG emissions data at the facility (and even stack/unit) scale including annual, monthly, daily and hourly emissions currently updated to the end of

5 Direct Overpass at Westar Jeffrey Energy Center XCO2 Relative to Background Average Wind direction adjusted by -2.0 from mean ECMWF 10.8 m/s, 2.7 MERRA 7.8 m/s, Kansas Dec 2015 cut-off at 35 km 12.5 MtCO2/yr Tim Hill (U Waterloo) Enhancement is small due to high wind speeds (~8-11 m/s) EPA Reported Emissions: 26.7 ktco2/day Estimated Emissions: 26.4±5.1 ktco2/day Error budget: wind speed: ±4.2 kt/day background ensemble: ±1.2 kt/day enhancement ensemble: ±2.6 kt/day

6 Westar Jeffrey Energy Error Budget MERRA and ECMWF each differ from mean Wind Speed by 15.9% so x 26.4 ktco 2 /day = ±4.21 ktco 2 /day Background Estimate ktco 2 /day 0-75 km km km km σ ± 1.21 Bias Correction Estimate ktco 2 /day Corrected S 31 + Corrected Partial Uncorrected σ ± 2.63 XCO 2 Corrected = (XCO 2 Raw Foot fp,mode Feats mode ) / TCCON_adjust mode Foot2 fp,mode XCO 2 with S31 = XCO 2 Corrected + 7 * [ * (albedo 3 / albedo 1 ) - 0.2] XCO 2 Partial = (XCO 2 Raw Foot fp,mode ) / TCCON_adjust mode Total Uncertainty = = ±5.11 ktco 2 /day

7 Close Flyby of Ghent Generating Station XCO 2 Relative to Background Average Wind direction adjusted by from mean ECMWF , 0.50 m/s MERRA , 1.41 m/s Kentucky Aug MtCO 2 /yr 2.5 km buffer zone ~8 km to swath Enhancement is large due to low wind speed (~ 1 m/s) EPA Reported Emissions: 29.2 ktco 2 /day Estimated Emissions: 27.6±13.7 ktco 2 /day Error budget: wind speed: ±13.2 kt/day background ensemble: ±0.5 kt/day enhancement ensemble: ±2.8 kt/day

8 CARMA and Global Energy Observatory CARMA CO 2 emissions for 2004, 2009 and Future but locations are very inaccurate in many countries GEO has excellent geolocation and specifies design capacity (MWe/year) but only occasionally has some old CO 2 emission data

9 Sasan Ultra Mega Power Plant (UMPP) in the Singrauli region, India s Energy Capital Sasan Ultra Mega Plant has MWe units that were completed , so it is too new for real values in CARMA ( Future Value: 33.7 MtCO 2 /yr) Sasan UMPP Oct 2014 ECMWF , 1.86 m/s MERRA , 2.02 m/s Low wind speed, large source ~10 ppm XCO 2 enhancement Google earth Unit #6 was actually commissioned on (after this overpass) 14 km from Vindhyachal (32.4 MtCO 2 CARMA) and 16 km from Singrauli (14.8 MtCO 2 CARMA) so we assume these emission values for these sources to solve for Sasan s emissions Rihand (14.9 MtCO 2 in 2009) and Anpara (10.6 MtCO 2 in 2009) are far enough away to be negligible with this wind direction

10 Sasan Ultra Mega Power Plant, India XCO 2 Relative to Background Average No wind adjusted from mean cut-off at 50 km Clean Development Mechanism (CDM) Application Value = MtCO 2 /yr, CARMA = 33.7 MtCO 2 /yr Daily mean from 5/6 CDM value = 60.2 ktco 2 /day (CARMA = 76.9), our Estimate = 63.7±7.0 ktco 2 /day Error budget: wind, background, enhancement, others sources ±2.6, 0.6, 3.7, 5.7 ktco 2 /day

11 Table of Overpasses Coal Plant Country Month year Mode, configuration Reported Emissions (kt/day) Estimated Emissions (kt/day) Soundings in plume / backgrnd R Largest source of uncertainty Westar USA Dec 2015 Nadir, Direct Overpass ± / Wind Ghent USA Aug 2015 Nadir, Flyby (~ 8 km) ± / Wind Gavin USA July 2015 Nadir, Direct Overpass ± / Background Sasan India Oct 2014 Nadir, Direct Overpass ± / Other Sources Sasan India Nov 2014 Glint, Flyby (~ 4.5 km) ± / Background Matimba S. Africa Nov 2014 Glint, Flyby (~ 7 km) ± / Wind Matimba S. Africa Oct 2016 Glint, Direct Overpass ± / Wind Nassar et al. submitted.

12 Summary and Conclusions Using OCO-2 observations from direct overpasses and close flybys with a 2D Gaussian plume model to estimate point source emissions and uncertainties, where opportunities exist Demonstrated the method with coal plants in the US, where reported CO 2 emissions have low uncertainty, then applied it to other countries where emissions uncertainties may be higher OCO-2 accuracy and precision are sufficient, but limited coverage of plume/background, knowledge of wind speed and other external factors contribute uncertainty to the estimates There is some potential to reduce uncertainties with improved modeling methods, but bigger gains to come with improved observational capacity new/future high spatial resolution CO 2 imaging missions like TanSat, OCO-3, MicroCARB, geocarb, CarbonSat - various technologies / orbits

13 Method Overview Begin with a list of sources of interest and search OCO-2 data for overpasses within 0.3 Generate kml files for viewing bias corrected OCO-2 XCO 2 in Google Earth along with MERRA2 (0.5 x hr average) and ECMWF ERA Interim (T255, ~0.75, 6-hr instantaneous) wind vectors Run a 2D Gaussian plume model with wind speed at the stack height and reported emissions to calculate a priori CO 2 column enhancements in g/m 2 Define a background XCO 2 value from the observations and convert the model g/m 2 values to XCO 2 based on the background value Sample the plume model at the OCO-2 obs locations and fit the data using least squares multiple times with different adjustments to the mean MERRA-ECMWF wind direction to maximize the correlation coefficient (R) Use the optimal wind direction and fit model and observations to get a scale factor and thus an emission estimate based on the observations Derive error estimates based on wind speed uncertainty, background ensemble uncertainty, plume enhancement / bias ensemble uncertainty