1. Reassessment of Tropospheric Ozone due to Fires/Pollution Stephan Gallagher

2. Motivation The contribution to the total tropospheric ozone of these sources can be accurately determined: The contribution to the total tropospheric ozone of these sources can be accurately determined: Boundary Layer Boundary Layer Regional Convection and Lightning (Gravity Wave) Regional Convection and Lightning (Gravity Wave) Stratosphere (Rossby Wave) Stratosphere (Rossby Wave) However, there is a significant amount of ozone unaccounted for, which is referred to as aged or advected ozone However, there is a significant amount of ozone unaccounted for, which is referred to as aged or advected ozone Based on the budget formula, advected ozone can be thought of “everything else” after the aforementioned sources Based on the budget formula, advected ozone can be thought of “everything else” after the aforementioned sources

3. Motivation Budget Plot Budget Plot Blue: ozone source is advected or “Other” Blue: ozone source is advected or “Other”

4. Overview This study was done to try to find a specific contribution, in the form of fires/pollution, to this significant portion of the ozone budget This study was done to try to find a specific contribution, in the form of fires/pollution, to this significant portion of the ozone budget Using ARCIONS ozone budgets, the following specific contributions to the advected ozone were determined: Using ARCIONS ozone budgets, the following specific contributions to the advected ozone were determined: Asian Pollution/California Fires Asian Pollution/California Fires Siberian Fires Siberian Fires Canadian Fires Canadian Fires In the end, a new ozone budget was able to be computed that accounted specifically for the contribution of fires/pollution In the end, a new ozone budget was able to be computed that accounted specifically for the contribution of fires/pollution

5. ARCIONS Locations Boulder, CO (13) Boulder, CO (13) Bratt’s Lake, SK (11) Bratt’s Lake, SK (11) Kelowna, BC (10) Kelowna, BC (10) Stony Plain, AB (9) Stony Plain, AB (9) Whitehorse, YK (6) Whitehorse, YK (6) Yellowknife, NT (7) Yellowknife, NT (7)

6. Methods: Back Trajectory Layers Layers were determined Layers were determined Using Hysplit back trajectories, 3 distinct, average layers were determined Using Hysplit back trajectories, 3 distinct, average layers were determined From the boundary layer to the tropopause, back trajectories in increments of 1 km were run to indicate different levels (based on directional changes) From the boundary layer to the tropopause, back trajectories in increments of 1 km were run to indicate different levels (based on directional changes) Provided a fairly complete representation of the movement of air parcels throughout the entire tropospheric column, prior to reaching the location Provided a fairly complete representation of the movement of air parcels throughout the entire tropospheric column, prior to reaching the location If no distinct directional changes were apparent in the back trajectories, average, standard levels were assumed If no distinct directional changes were apparent in the back trajectories, average, standard levels were assumed Low: Boundary layer to ~5 km Low: Boundary layer to ~5 km Middle: ~5 km to ~8 km Middle: ~5 km to ~8 km Upper: ~8 km to Tropopause Upper: ~8 km to Tropopause Standard levels were used based on the average level heights for each location and allowed data from all days to be used Standard levels were used based on the average level heights for each location and allowed data from all days to be used

7. Methods: Back Trajectory Layers Example from Yellowknife, NT (July 2, levels: red: 4 km, blue: 7 km, green: 10km) of three levels as each direction of back trajectories are substantially different Example from Yellowknife, NT (July 2, levels: red: 4 km, blue: 7 km, green: 10km) of three levels as each direction of back trajectories are substantially different

8. Methods: Back Trajectory Layers Back Trajectory Data Summary Average Levels (km)Standard Levels Used SiteAverage Boundary Layer (km)LowMiddleUpperAverage Tropopause (km)Low MaxMiddle Max Boulder2.22.2-4.584.59-8.588.59-14.014.027 Bratt's Lake1.91.9-4.054.06-7.367.37-10.910.937 Kelowna2.92.9-5.195.20-8.048.05-11.711.7611 Stony Plain2.52.5-4.574.58-7.717.72-11.311.348 Whitehorse2.22.2-4.844.85-8.208.21-10.910.947 Yellowknife2.02.0-4.874.88-7.807.81-10.610.667 Summary of layer data at each location Summary of layer data at each location

9. Methods: Fires/Pollution Contribution The contribution of fires/pollution to ozone: The contribution of fires/pollution to ozone: Determine if any of the 3 back trajectory levels pass over: Determine if any of the 3 back trajectory levels pass over: Asian Pollution: a major Asian city (ex: Beijing, Tokyo, Hong Kong) Asian Pollution: a major Asian city (ex: Beijing, Tokyo, Hong Kong) California fires: any fire in California California fires: any fire in California Siberian Fires: any fire in Siberia (primarily eastern Russia) Siberian Fires: any fire in Siberia (primarily eastern Russia) Canadian Fires: any fire in Canada Canadian Fires: any fire in Canada The location of fires (across all categories) was determined using daily interactive maps from the Fire Information for Resource Management System (FIRMS) The location of fires (across all categories) was determined using daily interactive maps from the Fire Information for Resource Management System (FIRMS) All back trajectories were run for up to 6 days of data All back trajectories were run for up to 6 days of data

10. Methods: Asian Pollution Contribution Example of a back trajectory from Kelowna, BC (June 27, upper levels) that passes over a polluted Asian city (Beijing) Example of a back trajectory from Kelowna, BC (June 27, upper levels) that passes over a polluted Asian city (Beijing) Beijing, China

11. Methods: California Fires Contribution Example of back trajectory from Kelowna, BC (July 4, middle levels) passing over California fires Example of back trajectory from Kelowna, BC (July 4, middle levels) passing over California fires

12. Methods: Siberian Fires Contribution Example of back trajectory passing over Siberian fires from Whitehorse, YK (July 1, upper levels) Example of back trajectory passing over Siberian fires from Whitehorse, YK (July 1, upper levels)

13. Methods: Canadian Fires Contribution Example of back trajectory from Yellowknife, NT (July 9, upper levels) passing over Canadian fires Example of back trajectory from Yellowknife, NT (July 9, upper levels) passing over Canadian fires

14. Methods: Fires/Pollution Contribution Roughly 15 days worth of data at each location, at 3 levels, with 3 possible fire/pollution sources Roughly 15 days worth of data at each location, at 3 levels, with 3 possible fire/pollution sources

15. Methods: Level Assessment Ozone totals were computed individually for each of the 3 average levels (as determined by back trajectories) Ozone totals were computed individually for each of the 3 average levels (as determined by back trajectories) At each location, for every level with a fire/pollution contribution, the amount of advected ozone for that day was compared to the average for that level for all days At each location, for every level with a fire/pollution contribution, the amount of advected ozone for that day was compared to the average for that level for all days Yielded a percent fluctuation in advected ozone due to fires/pollution for that day and when averaged over all days, a percent fluctuation for advected ozone at each level, at each location Yielded a percent fluctuation in advected ozone due to fires/pollution for that day and when averaged over all days, a percent fluctuation for advected ozone at each level, at each location With a percent fluctuation at each level, the percentage of ozone due to fires/pollution could be determined at each location With a percent fluctuation at each level, the percentage of ozone due to fires/pollution could be determined at each location

16. Results Average % and Total Enhancement of Advected Ozone By Individual Fires/Pollution Sources at each Location Location SourceBoulderBratt's LakeKelownaStony PlainWhitehorseYellowknifeOverall Averages Asian Pollution/California Fires2.48%12.36%45.16%8.69%0.00% 11.45% Canadian Fires0.00% 40.73%6.79% Siberian Fires0.00%70.51%0.00% 11.75% Average % of Ozone Enhancement0.83%27.62%15.05%2.90%0.00%13.58%10.00% Total Enhancement (DU)0.166.692.650.540.002.522.09 Average ozone enhancement due to fires/pollution: 2.09 DU Average ozone enhancement due to fires/pollution: 2.09 DU Average percentage of Advected ozone enhancement due to fires/pollution: 10.00% Average percentage of Advected ozone enhancement due to fires/pollution: 10.00% Site most affected: Bratt’s Lake: 27.62 % increase Site most affected: Bratt’s Lake: 27.62 % increase Site least affected: Whitehorse: 0.00% change Site least affected: Whitehorse: 0.00% change Source with largest impact: Siberian Fires: 11.75 % increase Source with largest impact: Siberian Fires: 11.75 % increase

17. Results: New Budget Plot Budget Plot now includes Fires/Pollution contribution Budget Plot now includes Fires/Pollution contribution

18. Conclusion Fires/Pollution do in fact impact ozone levels in an appreciable way Fires/Pollution do in fact impact ozone levels in an appreciable way Allow us to have a better understanding of what makes up part of the advected ozone at a location Allow us to have a better understanding of what makes up part of the advected ozone at a location Fires/Pollution that exist globally can impact other continents Fires/Pollution that exist globally can impact other continents Asian pollution and Siberian Fires both impact ozone levels in western Canada and United States Asian pollution and Siberian Fires both impact ozone levels in western Canada and United States Evident of a global issue to limit emissions of pollutants to guarantee better air quality across the globe (and not simply locally) Evident of a global issue to limit emissions of pollutants to guarantee better air quality across the globe (and not simply locally)