1. Improvements to Wintertime Particulate-Matter Forecasting with GEM-MACH15
Michael Moran1, Sylvain Ménard2 Paul Makar1, Radenko Pavlovic2, Mourad Sassi2, Paul-André Beaulieu2, David Anselmo2, Curtis Mooney3, Wanmin Gong1, Craig Stroud1, Sunling Gong1, and Junhua Zhang1
1Air Quality Modelling and Integration Section, Environment Canada, Toronto
2Air Quality Modelling Applications Section, Environment Canada, Montreal
3Air Quality Science Unit, Environment Canada, Edmonton, Alberta
Thank chair for giving EC an opportunity to present some recent improvements to our oper AQ forecast model. I am here representing two groups: Mike Moran leads the …section and Sylvain Menard leads the …..section. Today I am going to talk about some some recent improvements to our wintertime PM forecasting with GEM-MACH.
CMAS October 2010

2. Talk Outline
GEM-MACH15 and AQHI
Problem description: High AQHI predicted values which were “false alarms”
Investigation
Solution
Outcome
Some high AQHI predicted values.
How we improved the operational model.

3. Terminology
GEM is Environment Canada’s operational weather forecast model (global and regional versions)
GEM-MACH is composed of GEM plus in-line chemistry from the AURAMS CTM
GEM-MACH15 is the operational limited-area configuration of GEM­MACH with 15-km horizontal grid spacing
GEM-MACH15 went operational on 18 Nov and is piloted by the regional version of GEM

4. GEM15 and GEM-MACH15 Grids
GEM15 employs a global variable met grid, but with a uniform core grid at 15-km spacing for N. America
GEM-MACH15 employs a limited-area grid (LAM), also with 15-km spacing and co- located with GEM15 grid points
GEM15 supplies meteorological initial conditions and hourly lateral boundary conditions to LAM
GEM15 core grid (red) ; GEM-MACH15 grid (blue)

5. Air Quality Health Index (AQHI)
New Canadian national, multi-pollutant, no-threshold, health-based air-quality index (Stieb et al., 2008)
Varies from 1 (very low risk) to 10+ (very high risk)
Based on weighted sum of [O3], [PM2.5], and [NO2] :
AQHI = (10/10.4) * 100 * ((exp( *O3)-1)
+ (exp( *PM2.5)-1) + (exp( *NO2)-1))
NO2 itself is not likely the toxic species, but rather it is a surrogate for the toxicity associated with combustion emissions
Current values are based on real-time measurements in selected urban centres; forecasts are based on GEM-MACH15 predicted point concentrations (3hr running mean)
NO2 weighted highest

6. Posted for 37 cities
across Canada
Real-time meas
Messaging

7. Problem Description: AQHI "False Alarms"
As soon as GEM-MACH15 went operational in Nov. 2009, some PM2.5 forecasts were found to be too high (up to 300 ug m-3) for some Canadian cities
PM2.5 overpredictions led to AQHI forecast overpredictions

8. Example: 7-day PM2. 5 Time Series for Quebec City Beginning at 18 Nov
Example: 7-day PM2.5 Time Series for Quebec City Beginning at 18 Nov. 2010, 12 UTC

9. Example: 48-h AQHI Time Series for Quebec City Beginning at 20 Jan
Example: 48-h AQHI Time Series for Quebec City Beginning at 20 Jan. 2010, 12 UTC
21 January 2010, 08 UTC
GEM-MACH15
Observations

10. Wintertime meteorological conditions in Canada include:
cold temperatures
reduced solar radiation
widespread snowcover
strong surface inversions
shallow boundary layers
These conditions can affect atmospheric dispersion, chemistry, and removal

11. Investigation (1)
On some winter days predicted PM2.5 concentration fields had isolated “hot spots” as small as one or two grid cells over six Canadian cities (Quebec City, Montreal, Ottawa, Saskatoon, Calgary, Edmonton) but not other Canadian cities (e.g., Halifax, Toronto, Winnipeg, Regina, Vancouver) or U.S. cities
Turning off emissions from (a) major point sources had little impact on hot spots, but turning off emissions from (b) area sources had major impact on hot spots
The two dominant PM2.5 species contributing to the spurious peaks were crustal material (dust, soil) and primary organic matter (POA); both are inert emitted species.

12. Investigation (2)
The previous operational AQ forecast model did not have this problem
The emission files used by GEM-MACH15 were newer than those used by the previous operational model and were based on
newer emissions inventories: 2006 Cdn / 2005 U.S. vs Cdn / 2001 U.S.
newer spatial surrogate fields
These findings implicated the Canadian primary PM2.5 emissions used by GEM-MACH15

13. Solution 1: Improvement to Dust Emissions
The new set of Canadian spatial surrogate fields used to process the Canadian emissions inventory was based in part on socioeconomic data from 2006 Canadian census; the older set of surrogates was based on 2001 census
2006 labour-force statistics were reported by worker’s place of work vs. place of residence reported in 2001; the former should be better, but many workers do administrative or clerical jobs in offices in urban centres that are often not co-located with industrial facilities
Places too much weight on cities and not industrial locations
Spatial surrogate fields for heavy construction, upstream oil & gas, and mining were recalculated after removing workers located in major urban centres; the monthly temporal profiles for these sectors were also reviewed and revised downward in winter months
Mining and construction tend to occur more in fair weather seasons

14. Solution 2: Improvement to Wood Smoke Emissions
Residential wood combustion (RWC) is a major wintertime PM2.5 source in Quebec and Northern Ontario
Spatial surrogate chosen was intersection of “forest” land- cover fraction with residential home density
Likely overweights urban areas, where RWC is less commonly used for space heating
Two direct surveys on RWC use were available for province of Quebec; a new spatial surrogate was developed from these surveys for Quebec only
2006 Quebec RWC emissions were 33% higher than value; could not be justified, switched to 2005 value

15. PM2.5 Emissions Changes (Old – New) (1)
· Red shows areas where new PM2.5
emissions are lower than old
Red hot spots indicate major
decreases in PM2.5 emissions
for cities

16. PM2.5 Emissions Changes (Old – New) (2)

17. Impact: 7-d PM2. 5 Time Series for Quebec City Beginning at 18 Nov
Impact: 7-d PM2.5 Time Series for Quebec City Beginning at 18 Nov. 2010, 12 UTC
300
Model
200
100 50
Observed
• Within a
factor of 2

18. Impact: 7-d PM2. 5 Time Series for Saskatoon Beginning at 18 Nov
Impact: 7-d PM2.5 Time Series for Saskatoon Beginning at 18 Nov. 2010, 12 UTC
300
Model
200
100 50
Observed
• Within a
factor of 2

19. Impact: 7-d PM2. 5 Time Series for Toronto Beginning at 18 Nov
Impact: 7-d PM2.5 Time Series for Toronto Beginning at 18 Nov. 2010, 12 UTC 60
Model 50 40 30 20 10
Observed

20. Conclusions
Wintertime meteorological conditions can cause reduced atmospheric dispersion, maximizing the impact of local primary PM emissions and revealing weaknesses in emissions inventories and emissions processing
Wintertime PM overpredictions by GEM-MACH15 in a number of Canadian cities were reduced by reprocessing the input emissions after modifying the spatial surrogate fields and monthly temporal profiles for three industrial sectors (construction, UOG, mining) and the spatial surrogate field used for residential wood combustion emissions for the province of Quebec
Future wintertime evaluations will be performed with speciated PM measurements (IMPROVE, STN, NAPS, CAPMoN networks)
Model process representations may also need to be improved, including PBL mixing and the meteorological modulation of fugitive PM emissions by precipitation, snow cover, and soil moisture

21. Thank you for your attention!