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Improvements to Wintertime PM Forecasting with GEM-MACH15

This study explores the issues of high AQHI predicted values and "false alarms" in wintertime particulate matter (PM) forecasting using the GEM-MACH15 model. Investigations led to solutions for improving dust and wood smoke emissions, resulting in more accurate PM forecasts.

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Improvements to Wintertime PM Forecasting with GEM-MACH15

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  1. Improvements to Wintertime Particulate-Matter Forecasting with GEM-MACH15 Michael Moran1, Sylvain Ménard2Paul 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 CMAS October 2010

  2. Talk Outline • GEM-MACH15 and AQHI • Problem description: High AQHI predicted values which were “false alarms” • Investigation • Solution • Outcome

  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. 2009 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(0.000537*O3)-1) + (exp(0.000487*PM2.5)-1) + (exp(0.000871*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)

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

  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. 2010, 12 UTC

  9. 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. 2000 Cdn / 2001 U.S. • newer spatial surrogate fields • These findingsimplicated 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 2005 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. 2010, 12 UTC 300 Model 200 100 0 50 Observed • Within a factor of 2

  18. Impact: 7-d PM2.5 Time Series for Saskatoon Beginning at 18 Nov. 2010, 12 UTC 300 Model 200 100 0 50 Observed • Within a factor of 2

  19. Impact: 7-d PM2.5 Time Series for Toronto Beginning at 18 Nov. 2010, 12 UTC 60 Model 50 40 30 20 10 0 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!

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