1. Development of a High Resolution Air Quality Prediction System for the PanAm and Para PanAm Games
Craig Stroud, Sylvie Gravel, Balbir Pabla, Mike Moran, Sylvain Menard,
Paul Makar, Junhua Zhang, Alain Robichaud, Wanmin Gong,
Heather Morrison, Veronique Bouchet

2. Outline Objectives for AQ Forecasting during PanAm
Summary of Prior AQ Studies in Toronto
Model Development Projects
Current Results
Future Work

3. Objectives of the Air Quality (AQ) Science Showcase
To develop an integrated environmental model to address scientific questions related to urbanization:
To study the feedbacks between aerosol and weather in Canada’s largest city
To perform an aerosol source apportionment study at the U of Toronto rooftop comparing both receptor and emission models, assess decadal changes in emission-based models and observations for Toronto.
To improve urban spatial surrogate maps for allocating on-road mobile emissions and food cooking emissions
To develop and validate a “high-resolution” objective analysis
Opportunity to align projects to a common domain and time period to foster collaboration within EC and with our partners
To accelerate the development of a “high-resolution” operational AQ prediction system for urban cities across Canada
opportunity

4. Urban Heat Island and its Impacts on Air Quality and Weather
Makar et al., (2006) Atmos. Environ.
Used satellite visible radiation maps
to constrain anthropogenic heat input
Improved predictions of nighttime temp
Significant impact on PBL mixing height
Poland
GEM-AQ 5.
Struzewska and Kaminski (2012)
ACP, 12,
Improved temp and wind speed
In most cases, primary pollutants were more vertically mixed with UHI.

5. Literature Review on PM2.5 Observations For the Greater Toronto Area
Southern
Ontario
2001 – 2003 NAPS PM2.5 Data
30-45% of PM2.5 was from local sources and 55-70% from regional transport

6. PMF Analysis 24-hr Filter Data 2004-2007
Secondary PM2.5 factors from sulfate and nitrate were 60%. Gasoline and diesel fuel combustion factors summed 26%. Biomass burning and road dust were small for Toronto. Food cooking was not identified as a unique factor in the analysis.

7. Ontario NEI has POA area sources calculated at 64% of total POA sources
Food cooking is the largest area source in summer.

8. Interestingly, a factor analysis study, using highly time-resolved organic aerosol mass spectra and VOC mass spectra, yielded an organic aerosol budget as follows:
Food cooking was identified as a significant source of PM1 organic
aerosol. Comparable to the OOA-1 which is believed to consist largely
of SOA. Primary HOA was 15% and Biomass Burning was 14%.

9. Model Development with GEM-MACH v2
Migrating chemistry routines to the latest version of EC’s operational weather forecast model (GEM v4) to benefit from improvements to the ongoing boundary layer dynamics and cloud physics parameterizations:
New vertical coordinate (hybrid in log-hydrostatic-pressure); new vertical discretization (Charney-Phillips staggering) lowest layer depth is now 40-m; physics spin-up capability; piloting of LAM at the lid; global Yin-Yang grid; native vertical diffusion scheme possible for chemical tracers; new TKE scheme.
Different sets of chemical lateral boundary conditions based on MOZART reanalysis (seasonal 1-D vertical profiles in GEM-MACH v1 vs. seasonal 3-D mean fields in GEM-MACH v2)
Correction of previous errors in surface emissions input and gas-phase deposition in GEM-MACH v1
New parameterization of below-cloud scavenging of size-resolved particles by both rain and snow 6.

10. Model Developments Continued
Evaluated ADOM-II gas chemistry against SAPRC-07 gas chemistry for Canadian domain
Update Maestro task sequencer suite for launching GEM-MACH v2
New evaluation data sets (mobile labs, satellite obs) and evaluation tools (ValidatoR)
Newer emissions inventories and projections
Updated 2006 Can inventory (projection to 2015), 2011 US inventory
New U.S. spatial surrogates based on 2010 U.S. census
Improved Canadian and U.S. temporal profiles (e.g., diurnal NH3 profiles for livestock)
Expanded library of PM speciation profiles
Testing new spatial surrogate maps for high-resolution PanAm domain (for rail, road and food cooking) 7.

11. “Zoom” of New Canadian Railroad Shapefile over Toronto Area: The Shapefile is Used to Construct Spatial Surrogate Fields for Allocating Canadian Railroad Emissions

12. Link-Based On-Road Pollutant Emissions
(McMaster University Traffic Flow Model)

13. Evaluating New Traffic Emissions
Traffic Flow Modelling
on Road Network
Evaluating New Traffic Emissions
Average Modeled NOx Mixing Ratio
June 18 – July 9, 2007
Little bias in Modeled NOx
mixing ratios
Link-based
traffic emissions
input to model
2.5-km model grid spacing

14. Residential, August, Weekday, V2 Commercial, August, Weekday, V2
Food Cooking PM2.5 Inventory and Spatial Distribution
McMaster University Study
Residential, August, Weekday, V2
Commercial, August, Weekday, V2
Needs evaluation – PM source apportionment project, in collaboration with Prof Arthur Chan at U of Toronto

15. Real-Time AQ Forecasts for PanAm are now Running
Real-Time AQ Forecasts for PanAm are now Running ! Nested GEM-MACH v2 at 2.5-km, 250x300 grid
High Resolution Domain
Includes Detroit/Windsor,
Cleveland, Buffalo, Pittsburgh
Includes 3 Great Lakes to capture lake breeze effect
Met will be driven by operational 2.5-km GEMv4
GMv2 just compiled, currently under evaluation, PanAm first project to use v2.
Differences in v2 vs v1, vertical coord and discretization, thinkness of lowest layer increases from 20m to 40m. Native vertical diffusion scheme to GEM now used for tracers, TKE equation for PBL dynamics changed. Cloud physics scheme more physically based (M&Y). For regional GMv2, the driving GEMv4 model currently is GDPS (25km) for met initialization and met piloting files. Operations will switch to global variable at 15-km soon which will match 10-km resolution better.
24-hr forecast, initiated once a day
Real-time predictions of AQHI at air quality stations

16. GEM-MACH v2 Aerosol Mass Average for July 2014, Weekdays
Seasonal PM2.5 Composition
NAPS Filter Data
4 ug/m3 pSO4

17. GEM-MACH v2 Aerosol Composition Average for July 2014, Weekdays
Nitrate
Black Carbon
POA
Sulfate

18. O3 Evaluation for GEM-MACH v2, Summer 2008
2.5-km
10-km
Model O3
Model O3
10-km Stats
O3 Bias = 16.4 ppbv
R = 0.67
RMSE = 21.5 ppbv
2.5-km Stats
O3 Bias = 9.3 ppbv
R = 0.75
RMSE = 14.9 ppbv
Improved O3 bias with 2.5-km
grid spacing, likely due to higher resolution and new emission data sets.

19. Sensitivity Runs to Diagnose Ozone Over-prediction
Evaluated isoprene mixing ratios with NAPS data – no systematic bias was observed for eastern Canada
Evaluated photolysis rate of NO2 at one location – model J-values were biased high for periods with observed cloudy conditions; may contribute to the ozone over-prediction; improvements needed.
Reviewed literature for OH+NO2→ HNO3 reaction rate – our kOH in mechanism is reasonable.
Lowered vertical diffusivity lower limit for rural land use and increased for urban land use – no significant change in ozone predictions
Sensitivity test with doubled ozone dry deposition showed a strong sensitivity to assumed deposition velocity for land use type – review of ozone dry deposition parameters is underway.

20. Future Work with High Resolution AQ Modeling
Complete the evaluation of the high resolution system and emission data sets against measurement data sets:
PM speciation data
Mobile data sets (CRUISER and MAPLE)
Science Code Revisions (this fall):
Port the 2-way interaction code for GEM-MACH v2
Semi-volatile organic aerosol parameterization
Add several toxic species (benzene, benzo-a-pyrene)
Updates for outstanding issues (advection, O3 gas deposition)
Non-linear Post-Processing, develop with summer 2008, test for 2014
Objective Analysis, develop with summer 2008, test for 2014
During Games, upload forecast products to data portals (WISDOM, DATAMART) and work with data users (EC forecast desk, Toronto and Kingston health units)
After Games, develop case studies for impacts of aerosol indirect effect on weather and AQ in an urban environment – evaluate model against rich met mesonet. Interested in joining WMO working groups.

21. Air Quality Mobile Laboratories – CRUISER and MAPLE
for Summer 2008
NOx
NO2

22. Acknowledgements Prof. Greg Evans, University of Toronto
Prof. Pavlos Kanaroglou, McMaster University
David Henderson, AQHI MSC Program
Dr. Jeff Brook, ARQP Section
Dr. Bob Vet, Natchem data, ARQM Section
Dr. Sylvie Leroyer and Dr. Stephane Belair, MRD
NAPS, IMPROVE, AIRS Data Networks
Thank you for your attention!