Modelling the contributions of major UK industrial sources to regional air quality with Models 3

1. Modelling the contributions of major UK industrial sources to regional air quality with Models 3 Ye, Yu, Ranjeet Sokhi, Douglas R Middleton and Bernard Fisher Centre for Atmospheric and Instrumentation Research (CAIR) Met Office

2. Objectives • To examine the use of advanced 3D air quality models as a tool for assessing the potential of industrial emissions to create secondary pollutants under different meteorological conditions; • To give advice on the application of the Chemical Reactivity Index method in regulation.

3. Method ECMWF Reanalysis (1 degree resolution, every 6 hour) MM5 Meteorological Model MCIP Meteorology-Chemistry Interface Processor CORINE land cover data (100 m resolution) CMAQ Community Multiscale Air Quality Modelling System SMOKE Emission processor Biogenic emission Hourly 3-D Gridded Concentrations EMEP (A,M) NAEI (A,M,P) EPER (P) reformat MM5/CMAQ Modelling System

4. Spatial distribution of NOx emissions processed by SMOKE Domain 1- 81 km Domain 3- 9 km

5. Experiment design • Simulation periods (three episodes): • a. 12 UTC 22 Jun -12 UTC 28 Jun 2001: Summer O3 and NO2 episode (T3) • b. 00 UTC 09 Dec – 00 UTC 15 Dec 2001: Winter NO2 episode (T1 &T2)) • c. 00 UTC 31 Aug. – 00 UTC 04 Sept. 1998: SO2 episode (T1&T2)

6. Locations and indicative emissions of the 9 stacks Annual emissions (tonne)

7. CMAQ Model Configuration Initial and boundary conditions: Monthly averaged concentrations of species from global 3-D chemical-transport model STOCHEM Chemical mechanism: CB-IV 26 vertical layers For Sep. 1998 & Dec. 2001 For June 2001

8. Model evaluation(June 2001 episode)

9. Observed concentrations (June 2001) Hourly NO2 (left) and O3 (right) concentrations during the June 2001 episode at selected sites.

10. Air quality stations

11. Temporal variations (23 Jun –28 Jun 01)

12. Scatter plots of Observed vs. Modelled O3 and NO2 concentrations for 3 km resolution Fraction of predictions within a factor of 2 of observations O3 NO2 53% 82 %

13. Estimating the contribution of industrial point sources to near surface pollutant concentrations

14. Contribution of industrial point sources to near surface O3 (June 2001) daily maximum (top); daily maximum 8-h mean (bottom)

15. Contribution of industrial point sources to near surface NO2 (Daily maximum NO2) June 2001 (top); Dec. 2001 (bottom)

16. Contribution of industrial point sources to ambient pollution levels (Daily maximum SO2) June 2001 (top); Sept. 1998 (bottom)

17. Percentage contribution of all UK point sources to near surface pollutant concentrations Percentage contribution=(Exp. A-Exp. B)/Exp. A

18. Conclusions • CMAQ is able to give information on the quantity of point source contributions to near surface pollutant concentrations and its spatial distribution. These information will help the Agency to identify the most affected areas and the most important pollutant to regulate. • On average, point source emissions have the largest contribution to near surface SO2 concentrations, followed by NO2. The overall contribution of point source emissions to ground level O3 is very low and negative especially for the winter episode. • Weather and meteorological conditions can significantly affect the degree of contributions from point source emissions, suggesting that regulatory control of emissions from industrial sources is essential to abate pollution levels under particular meteorological conditions.

19. Plume Chemistry

20. Evolution of O3 and NO2 in plumes from two point sources (3km resolution)

21. Plume chemistry (3km resolution)

22. Plume chemistry (3km resolution)

23. Plume chemistry (3km resolution)

24. Plume chemistry (3km resolution)

25. Plume chemistry (3km resolution)

26. VOC/NOx and H2O2/NOz (3km resolution) VOC= PAR+2OLE+2ETH+2ALD2+7TOL+8XYL+5ISOP+FORM NOz = PAN + HONO + HNO3 + NO3 + N2O5

27. Conclusion • The O3 and NO2 concentrations in plumes simulated by CMAQ captured several qualitative behaviour of chemistry in the plume that is common in NAME III results. For example the formation of raised NO2 in the plume at night, the removal of ozone in the plume region by titration with NO, and the formation of ozone further downwind point sources if sufficient hydrocarbons are available.

28. Hourly [NO2] vs [NOx] Hourly [NO2]/[NOx] vs [NOx]

29. [NO2] versus [NOx] (hourly) June, 2001 Kingsnorth power station

30. June, 2001 [NO2]/[NOx] versus [NOx] (hourly) Kingsnorth power station

31. Dec., 2001 [NO2] versus NOx (hourly) Lindsey oil refinery

32. Dec., 2001 [NO2]/[NOx] versus [NOx] (hourly) Lindsey oil refinery

33. Sept. 1998 [NO2] versus NOx (hourly) Lindsey oil refinery

34. Sept. 1998 [NO2]/[NOx] versus [NOx] (hourly) Lindsey oil refinery

35. Conclusions • The CMAQ results confirm the sensitivity of [NO2] and [NO2]:[NOx] to day or night seen in NAME III results and extends it to include the dependence on weather and meteorological conditions • By selecting representative background [O3], the enclosing curves derived from NAME III encompass most of the data point of CMAQ results. • The results also show that different model runs all tend to suggest that the empirical curves from urban monitoring data are tending to underestimate the amount of NO2 presented in model simulations