1. Impact of Temporal Fluctuations in Power Plant Emissions on Air Quality Forecasts Prakash Doraiswamy 1, Christian Hogrefe 1,2, Eric Zalewsky 2, Winston Hao 2, Ken Demerjian 1, J.-Y. Ku 2 and Gopal Sistla 2,* 1 Atmospheric Sciences Research Center, University at Albany, Albany, NY 2 New York State Department of Environmental Conservation (NYSDEC), Albany, NY * retired 10/11/2010

2. Background Air quality models such as CMAQ are being used to provide air quality forecast guidance. The accuracy of forecasts from such modeling systems is influenced, in part, by the quality of the emissions used and their associated uncertainties. The typical emissions processing uses annual emissions estimates for anthropogenic emissions that are then allocated to each hour based on “typical” or “average” temporal profiles for each source category. Some source categories, such as electric generating units (EGUs), are known to exhibit significant temporal variations in emissions in response to weather conditions.

3. Goals of this study Retrospective simulations have the advantage of using actual EGU emissions for the modeling period, while routine air quality forecasting simulations have to rely on the typical EGU profiles. Hence, this study examines the sensitivity of predicted ozone levels to these differences in EGU emissions Objectives: – What is the nature and magnitude of the variability introduced into the model predictions of Ozone due to the differences in EGU temporal profiles/emissions? – How widespread is this variability? – How does this variability affect the model performance?

4. Model simulations Time period: May to September 2007 (relatively warm summer in the 2005-2009 time frame) Met model: – NCEP WRF-NMM 12z cycle weather forecast fields for the above time period (from archives maintained by NYSDEC) CMAQ v4.7.1 model Emissions: based on OTC 2007 “proxy” inventory – a mix of 2007 MANE-VU inventories for non-road and point sources, EPA-CHIEF 2005 point source inventory for all other regions, and interpolated 2007 emissions for other source- sectors/regions – 2 emission scenarios: “Actual” & “Average”

5. Emission Scenarios “Actual” emissions: – Temporal allocation of EGU emissions is based on measured hourly emissions from Continuous Emissions Monitors (CEMs) for EGUs – For MANE-VU states, 2007 unit-level hourly and annual total emission files for EGUs were developed using hourly CEMs data, state-submitted emissions and cross-walk files – For non-MANE-VU states, 2005 annual EGU emissions were temporally allocated by SMOKE using 2007 hourly CEMs files obtained from CAMD and cross-referencing ORIS/boiler ID “Average” emissions: – Allocates annual emission totals to specific hours using temporal profiles derived from the actual 2007 CEM data on a state-by-state basis

6. Emissions

7. MANE-VU Daily EGU NO x Emissions

8. MANE-VU Average Diurnal EGU NO x Emissions “Average” emissions appeared to increase rapidly during the early morning hours until about 10 am and then stabilized until about 6 pm and then began to decrease. “Actual” emissions increased at a slower rate than the average emissions, and reached a maximum peak around 3 pm, a little later than the average profile.

9. Effect on Ozone Predictions - At each grid in the modeling domain - Across the ozone monitors in the MANE-VU region

10. Maximum Effect on Modeled 1-hr and 8-hr Daily Max Ozone For each emission scenario, calculate 1-hr and 8-hr daily max ozone for each grid Take the difference of the calculated daily 1-hr and 8- hr daily max between the two emission scenarios (“Actual” – “Average”) What is the maximum/minimum of this difference at each grid over the entire period from May-Sep 2007? – Represents the extreme effect at each grid unpaired in time – Shown on the next 2 slides

11. Max/Min daily difference of 1-hr Daily Max from May-Sep 2007 Peak 1-hr Daily MaxMin 1-hr Daily Max

12. Max/Min daily difference of 8-hr Daily Max from May-Sep 2007 Peak 8-hr Daily MaxMin 8-hr Daily Max

13. Total Variability in Hourly Ozone The difference of the hourly ozone between the two emission scenarios (“Actual” – “Average”) can be both positive and negative The maximum hourly difference and the minimum hourly difference were determined each day. The difference between the max and min gives the range or the total variability for that day. What is the maximum of this daily total variability at each grid?

14. Maximum of the daily total variability from May-Sep 2007

15. Distribution of Max and Min Difference in 1-hr and 8-hr Daily Max at the monitors in MANE-VU Region < ±3 ppb difference at 75% of the sites Certain sites showed effect as much as ±10 ppb

16. Distribution of Max and Min Difference in 1-hr and 8-hr Daily Max at the monitors in MANE-VU Region 3 cases – all days, days when observed ozone > 75 ppb, and days when actual emissions was > 90 th percentile in the respective state < ±3 ppb difference at 75% of the sites Certain sites showed effect as much as ±10 ppb

17. Distribution of Average Difference in 1-hr Daily Max at the monitors in MANE-VU 4 time periods: all days Obs. O 3 > 75 ppb, “Actual” emissions > 90 th percentile in the respective state “Actual” emissions < 10 th %ile Near zero on average, with 75% of the sites showing < ±0.5 ppb difference Slightly larger inter-quartile range (IQR) on high O 3 /electric demand days & narrow IQR on low emission days

18. Similar to the effect on 1-hr daily max, but a slightly narrower distribution in 8-hr daily max than 1-hr daily max for the respective case Distribution of Average Difference in 8-hr Daily Max at the monitors in MANE-VU

19. Time Series of hourly variability: 2 sites with contrasting response NY Site, urban site with high local emissions density Mostly negative difference in O 3 PA Site – rural agricultural site Mostly positive difference in O 3

20. Time Series of daily variability: 1-hr Daily Max O 3 NY Site, urban site PA Site – rural agricultural site Ozone changes mostly coincide with emission changes. Higher “Actual” emissions results in negative O 3 difference at NY site, while positive O 3 difference at PA site.

21. Time Series of daily variability: 1-hr Daily Max O 3 NY Site, urban site PA Site – rural agricultural site Ozone changes mostly coincide with emission changes. Higher “Actual” emissions results in negative O 3 difference at NY site, while positive O 3 difference at PA site. Response dependent on the photochemical regime of the region and its location relative to the source / path of plume

22. Comparison with Observations 8-hr daily max: Norm. Mean Bias

23. Summary “Actual” emissions were typically greater than “Average” emissions on days leading to an ozone episode. The impact on Ozone varied by location, and could be positive or negative. Maximum impact of ±3 ppb and an average impact of < ±0.5 ppb in 1-hr or 8-hr daily max across 75% of the monitors in the MANE-VU region – similar to variability resulting from meteorology or emission inventories The nature of the impact appears to be dependent upon the photochemical regime of the region and its location relative to the source/path of the plume.

24. Acknowledgements & Disclaimer This study was funded in part by NYSDEC and the New York State Energy Research and Development Authority (NYSERDA) under agreement #10599. The results presented here have not been reviewed by the funding agencies. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of NYSDEC or the sponsoring agency.