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Temporal Patterns in OMI NO2 Columns Benjamin de Foy, Saint Louis University AQAST-9, Saint Louis University, 3 June 2015 Cluster 1 (Blue) shows areas.

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Presentation on theme: "Temporal Patterns in OMI NO2 Columns Benjamin de Foy, Saint Louis University AQAST-9, Saint Louis University, 3 June 2015 Cluster 1 (Blue) shows areas."— Presentation transcript:

1 Temporal Patterns in OMI NO2 Columns Benjamin de Foy, Saint Louis University AQAST-9, Saint Louis University, 3 June 2015 Cluster 1 (Blue) shows areas with constant emissions except for a large drop during the recession (lowest values: 2009,10,11). Cluster 2 (Green) shows mainly areas in the developed world with decreasing columns and a drop during the recession. Cluster 3 (Red) is in growth areas, showing a much smaller impact of the recession. Cluster 4 (Beige) is between Cluster 1 and 2. Cluster analysis on annual trends identifies impacts of the recession across the world Boxplot values are % change during individual years relative to the 10-year average

2 St. Louis, Mo Vertical Profiles Aug. 30, 2013
3 Ozone Enhancements Identified in 1 Sounding Concurrent Ozonesondes launches from 7 SE-US sites during SEAC4RS/SEACIONS Authors: Joseph L. Wilkins Dr. Jack Fishman, Dr. Benjamin de Foy, Dr. Anne Thompson, Dr. Gary Morris, Dr. Charles Graves, and Dr. Edward Hyer St. Louis, Mo Vertical Profiles Aug. 30, 2013 O3 ppb (black) and RH % (green), Potential Vorticity 10^-6 pvu (blue), and FLEXPART-WRF CO BB tracer (shaded pbl (gray), pyro-cb (pink)). 7 day aged air from western Wildfires + Stratospheric Air (8-11km ~50ppb) 5 day Aged Air from Wildfires + Stagnation (5-7km ~30ppb) Anthropogenic + local ag. fires less than 2 days old (1-4km ~10ppb). Supplemental information SouthEast American Consortium for Intensive Ozonesonde Network Study (SEACIONS) Pyro-cb - pyro-cumulonimbus Fig.: Aug 30, 2013; STL ozonesonde O3 ppb black line and Relative Humidity % green line, NASA Goddard High Resolution Potential Vorticity Kinematic model 10^-6 pvu blue line, and FLEXPART-WRF CO BB particle tracer entering a 0.5° grid box over STL gray/pink shading. Stratospheric Air is indicated by PVU> 1.5 and RH<20. The Black arrows indicate three ozone enhancements day Aged Air from California and Idaho Wildfires + Stratospheric Air (8-11km, 10.25kg/cell of particles, ~50ppb) 2. 5 day Aged Air from Wildfires + Stagnation (5-7km, 1.8kg/cell, ~30ppb) and 3. Anthropogenic + local agricultural fires less that 2 days old (1-4km, 3,450.7kg/cell, ~10ppb). ; total day sum (3.4627E03 kg/cell) The Fires identified in this study where from California Rim Fire, Idaho Beaver Creek Fire, And local agricultural fires from SE-US, near Kansas and another cluster near Louisiana/Mississippi border. Take home message: With inclusion of proper injection heights, we can model pollution transport more accurately. With more accurate modeling of transport we can gauge photochemistry and lifetimes of pollution plumes better. Leading to quantification methods for production of ozone from emissions amounts. This work was supported in part from NASA Grant NNX11AJ63G to Saint Louis University through its AQAST Program. SEACIONS Data can be found at FLEXPART-WRF Pyro-cumulus FLEXPART-WRF Boundary Layer Emissions Our key findings: SEAC4RS data allowed for identification of ~42 pyro-cb which produced considerable O3 plumes downwind over SE-US. Without the inclusion of pyro-cbs models will incorrectly place or miss pollution transport.

3 See Poster by Valorie Wilmoth
An increase in stability trends in the lapse rate of the bulk layer from the surface to 850 hPa See Poster by Valorie Wilmoth

4 EC/OC Emissions based on inverse model and hourly measurements in St
EC/OC Emissions based on inverse model and hourly measurements in St. Louis: Diurnal and Monthly Profiles of Emissions Green: Weekday, Blue: Weekend Solid markers show LADCO Prior Inventory Shading shows uncertainty in inverse model results using bootstrapping Results obtained for On-Road, Non-Road, MAR, Point Sources and Other Non-Road emissions have double peak in the monthly profile due to agricultural equipment use Inverse model suggests emissions are lower on weekends than current inventory de Foy, B., Cui, Y. Y., Schauer, J. J., Janssen, M., Turner, J. R., and Wiedinmyer, C.: Estimating sources of elemental and organic carbon and their temporal emission patterns using a Least Squares Inverse model and hourly measurements from the St. Louis-Midwest Supersite, Atmos. Chem. Phys., 2015.

5 Power Plant NOx Emissions Estimated by OMI Benjamin de Foy, Saint Louis University
EMG lifetimes were not found to be representative of chemical reactivity Accuracy and reliability drop for 2 month period such that chemical lifetimes could not be inferred EMG gives reliable estimates for multi-year time periods Box model gives accurate and robust estimates for 6 month periods or longer de Foy, Benjamin, et al. “Estimates of power plant NOx emissions and lifetimes from OMI NO2 satellite retrievals." Atmospheric Environment, 2015, In Press.

6 Average Vertical Tropospheric NO2 Columns from OMI
Rows 10-27, Years Swath data averaged to 1 degree resolution

7 Number of days with data, out of 3,652 maximum for 10 year time series
Rows 10-27, Years Swath data averaged to 1 degree resolution

8 Least Squares Model of NO2 Time Series Example: Chicago pixel
Log normal transformation of variables Include impacts from: WS, U, V, T, Cloud Cover, Pixel Area Annual variation, Seasonal variation, Day of week variation Iteratively Reweighted Least Squares for outlier removal

9 Least Squares Model of NO2 Time Series Correlation Coefficient at each grid point
Log normal transformation of variables Include impacts from: WS, U, V, T, Cloud Cover, Pixel Area Annual variation, Seasonal variation, Day of week variation Iteratively Reweighted Least Squares for outlier removal

10 Least Squares Model of NO2 Time Series Contribution from Wind Speed Time Series
WS Blue Areas: Low wind speeds associated with higher NO2 columns Source regions have more NO2 when there is less dispersion Red Areas: High wind speeds associated with higher NO2 columns Clean areas have more NO2 when there is more transport

11 Least Squares Model of NO2 Time Series Contribution from Surface Temperature
Red Areas: Higher temperature associated with more NO2: Biogenic NOx signature? (Note that this is separate from seasonal variation)

12 Sunday Impacts in OMI NO2 in North America
Plot shows percentage drop in OMI NO2 columns on Sundays. Percentage reduction in OMI NO2 columns on Sundays relative to the rest of the week

13 Sunday Impacts in OMI NO2 in Asia
Plot shows percentage drop in OMI NO2 columns on Sundays. Percentage reduction in OMI NO2 columns on Sundays relative to the rest of the week

14 Weekday Patterns in OMI NO2 In Europe and the Middle-East
Percentage Reduction on different days of the week by geographic location

15 Seasonal Patterns in OMI NO2
Normalized scale factor by month of the year shows different seasonal patterns, including burning season in the Amazon and in central Africa

16 Annual Trends and Recession Impacts in OMI NO2
Cluster 1 (Blue) shows areas with constant emissions except for a large drop during the recession (lowest values: 2009,10,11). Cluster 2 (Green) shows mainly areas in the developed world with decreasing columns and a drop during the recession. Cluster 3 (Red) is in growth areas, showing a much smaller impact of the recession. Cluster 4 (Beige) is between Cluster 1 and 2. Cluster analysis on annual trends identifies impacts of the recession across the world Boxplot values are % change during individual years relative to the 10-year average

17 Temporal Patterns in OMI NO2 Columns Benjamin de Foy, Saint Louis University AQAST-9, Saint Louis University, 3 June 2015 Conclusions: Least Squares fit on 10 years of daily 1-degree averaged OMI NO2 columns identifies: Recession impacts and long term growth or reductions Seasonal variations Day of week changes Dependency on wind speed and temperature

18 Extras

19 Least Squares Model of NO2 Time Series Example: Chicago pixel
Include impacts from: WS, U, V, T, Cloud Cover, Pixel Area Annual variation, Seasonal variation, Day of week variation Iteratively Reweighted Least Squares for outlier removal

20 Least Squares Model of NO2 Time Series Example: Chicago pixel
Include impacts from: WS, U, V, T, Cloud Cover, Pixel Area Annual variation, Seasonal variation, Day of week variation Iteratively Reweighted Least Squares for outlier removal

21 Least Squares Model of NO2 Time Series Contribution from U10

22 Least Squares Model of NO2 Time Series Contribution from V10

23 Least Squares Model of NO2 Time Series Contribution from Cloud Cover

24 Least Squares Model of NO2 Time Series Contribution from Swath Pixel Area

25 Sunday Impacts in OMI NO2
Least-Squares Model identifies changes in OMI NO2 columns on Sundays relative to the rest of the week

26 Friday Impacts in OMI NO2
Least-Squares Model identifies changes in OMI NO2 columns on Fridays relative to the rest of the week

27 CAMx evaluation of different methods for estimating emissions from column data
Box Model Gaussian Fit EMG Fit Emissions Estimate: Linear dependence on plume speed estimate Plume Speeds: Robust Speed-dependent bias Stronger Winds Plume Direction: Omni-directional Accurate Plume Rotation Chemistry: Required on input Fairly Robust Somewhat Robust Lifetime Estimate: Model Input Dispersion, very short Chemical, biased low de Foy, Benjamin, et al. "Model evaluation of methods for estimating surface emissions and chemical lifetimes from satellite data." Atmospheric Environment 98 (2014):

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