Predicting the AQHI without aid of observations: results from the northern New Brunswick study

1. Predicting the AQHI without aid of observations: results from the northern New Brunswick study National Air Quality Conference Durham, NC Daniel Jubainville Environment Canada Meteorological Service of Canada Feb 11th, 2014

2. Objectives of this study • Goal is to expand AQHI forecast program to rural areas without air quality monitoring data • Evaluate model performance for AQHI forecasting in rural areas • Determine forecaster skill in the absence of observed data • Observation data was collected starting in September 2012 and is expected to continue until June 2014

3. Companion Studies • Spatial AQHI Study – Dalhousie University, using passive and active sampling. (Interim Report available) • PM2.5 and O3 had high temporal and spatial correlation • NO2 had poor correlation across the network • St Valentin, QC – Rural AQHI site Campbellton Montreal Bathurst Edmundston Miramichi St Valentin Grand Falls

4. Air Quality Health Index: Concept Decouple air quality regulation from provision of health advice Develop an “impact” product, statistically-derived from: Canadian multi-city mortality/morbidity studies of short term health effects Air quality data from historical quality assured/controlled database of the National Air Pollution Surveillance Network (NAPS) Additive risk based on the association of acute health effects and the air pollution mixture (O3, PM and NO2) 3 hour rolling pollutant concentrations averages

5. Current AQHI Coverage Reaches 65% of Canadians -> 88 forecast locations New Brunswick

6. Site Overview • Baie des Chaleurs oriented ENE-WSW • Terrain rises 200-250 metres within a few kilometres of shoreline on either side of the bay.

7. Instrumentation

8. Local Emissions

9. Local Meteorology • Topography strongly influences local meteorological conditions • Air quality and weather data collected from September 14th, 2012 to December 31st, 2013 • Most common wind directions along river valley

10. Wind Stats, Seasonal14 Sep 2012 to 31 Dec 20135-Minute Average Wind Direction

11. Performance of GEM-MACH - NO2

12. Performance of GEM-MACH – O3

13. Performance of GEM-MACH - PM2.5

14. GEM-MACH Air Quality Model - AQHI Model percent correct within +/-1 AQHI = 98 Positive bias September-October mostly due to over-prediction of O3 Negative bias in colder months due to under-prediction of PM2.5 and NO2, and to a lesser extent O3 The negative bias is due to under-represented local emissions and the limited resolution of the boundary layer i.e. thermal inversions develop overnight during periods of light winds -> pollutants build up Bias in O3 due to seasonal variation not captured by model

15. Seasonal Performance (=, +/-1): 99% (=, +/-1): 98% (=, +/-1): 98% (=, +/-1): 95%

16. Forecast – Pilot Project • Atlantic Storm Prediction Centre (ASPC) forecasters asked to generate forecasts starting in January 2013. • Two forecasts per day, issued at 6AM & 5PM AST/ADT. • Forecasts are for maximum expected AQHI per period (Today, Tonight, Tomorrow). • Only issued if operational requirements allow. • Expect forecast availability to be biased towards fair weather situations when operations workload is lower. • Forecasters were not given access to observed data (blind test). • Forecasts ended in November 2013.

17. Forecasts issued 6:00 AM AST/ADTToday (January 17th – November 4th, 2013) Forecast Model

18. Forecasts issued 6:00 AM AST/ADTTonight (January 17th – November 4th, 2013) Forecast Model

19. Forecasts issued 6:00 AM AST/ADTTomorrow (January 17th – November 4th, 2013) Forecast Model

20. Forecasts issued 5:00 PM AST/ADTTonight (January 17th – November 4th, 2013) Forecast Model

21. Forecasts issued 5:00 PM AST/ADTTomorrow (January 17th – November 4th, 2013) Forecast Model

22. Air Quality Events 06Z Feb 26 2013 • Study captured a few events (Long Range Transport, local emissions buildup) • LRT was over-predicted by GEM-MACH, but timing was good. Short time-scale variability not captured. • Trapping of local pollutants under inversions not captured well by GEM-MACH. • Forecasters generally nudged forecast in right direction falling short of removing error. • E.g. 25-26 Feb 2013 GEM-MACH forecast 2/2/2 SPC forecast 3/3/3 Actual AQHI 4/4/3 • Missed smoke events/false alarms

23. Summary • Campbellton site is representative of a semi-rural centre with the measured AQHI generally in the Low Risk category • GEM-MACH showed skill predicting the maximum AQHI to within ± 1 of observed AQHI ~95% of the time • GEM-MACH positive AQHI bias (due to O3) in the fall became a negative bias in the winter and early spring (due to NO2, PM2.5 and to a lesser degree O3). • Cold season biases are due to under-represented local emissions, stronger inversions and inhibited mixing not fully parameterized in the model boundary layer. • ASPC forecasters generally added value to the GEM-MACH forecast predicting to within ± 1 observed AQHI ~98% of the time • ASPC forecasters generally added value by compensating for model’s cold season bias • ASPC forecasters and model both struggle with extreme events related to forest fire smoke

24. Acknowledgements Co-authors: Environment Canada – David Waugh, Alan Wilson, Steve Beauchamp, Doug Steeves Dalhousie University – Mark Gibson, Gavin King, James Kuchta Partners: Environment Canada – Craig Stroud, David Anselmo Collège Communautaire du Nouveau-Brunswick Campbellton Campus – Réjean Savoie New Brunswick Environment & Local Government – Darrell Welles, Eric Blanchard Health Canada – Kamila Tomcik, Christina Daly