Development of a High Resolution Air Quality Prediction System for the 2015 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

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Outline

• Objectives for AQ Forecasting during PanAm

• Summary of Prior AQ Studies in Toronto

• Model Development Projects

• Current Results

• Future Work

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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

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• Used satellite visible radiation maps to constrain anthropogenic heat input• Improved predictions of nighttime temp• Significant impact on PBL mixing

height

Urban Heat Island and its Impacts on Air Quality and Weather

Makar et al., (2006) Atmos. Environ.

Struzewska and Kaminski (2012)ACP, 12, 10387-10404•Improved temp and wind speed•In most cases, primary pollutants were more vertically mixed with UHI.

PolandGEM-AQ

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SouthernOntario

Literature Review on PM2.5 ObservationsFor the Greater Toronto Area

• 2001 – 2003 NAPS PM2.5 Data

• 30-45% of PM2.5 was from local sources and 55-70% from regional transport

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• 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.

PMF Analysis24-hr Filter Data2004-2007

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• Ontario NEI has POA area sources calculated at 64% of total POA sources

• Food cooking is the largest area source in summer.

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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 organicaerosol. Comparable to the OOA-1 which is believed to consist largelyof SOA. Primary HOA was 15% and Biomass Burning was 14%.

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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

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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)

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

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

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Average Modeled NOx Mixing RatioJune 18 – July 9, 2007

Little bias in Modeled NOxmixing ratios

Traffic Flow Modelling on Road Network

2.5-km model grid spacing

Link-basedtraffic emissionsinput to model

Evaluating New Traffic Emissions

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Residential, August, Weekday, V2 Commercial, August, Weekday, V2

Food Cooking PM2.5 Inventory and Spatial DistributionMcMaster University Study

• Needs evaluation – PM source apportionment project, in collaboration with Prof Arthur Chan at U of Toronto

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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

24-hr forecast, initiated once a dayReal-time predictions of AQHI at air quality stations

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GEM-MACH v2 Aerosol Mass Average for July 2014, Weekdays

Seasonal PM2.5 CompositionNAPS Filter Data

4 ug/m3 pSO4

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GEM-MACH v2 Aerosol Composition Average for July 2014, Weekdays

Nitrate Black Carbon

POA Sulfate

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O3 Evaluation for GEM-MACH v2, Summer 2008

2.5-km10-km

2.5-km StatsO3 Bias = 9.3 ppbvR = 0.75RMSE = 14.9 ppbv

10-km StatsO3 Bias = 16.4 ppbvR = 0.67RMSE = 21.5 ppbv

ModelO3

Improved O3 bias with 2.5-kmgrid spacing, likely due to higher resolution and new emission data sets.

ModelO3

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• 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.

Sensitivity Runs to Diagnose Ozone Over-prediction

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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.

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Air Quality Mobile Laboratories – CRUISER and MAPLEfor Summer 2008

NOx NO2

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Acknowledgements

Prof. Greg Evans, University of TorontoProf. Pavlos Kanaroglou, McMaster UniversityDavid Henderson, AQHI MSC ProgramDr. Jeff Brook, ARQP SectionDr. Bob Vet, Natchem data, ARQM SectionDr. Sylvie Leroyer and Dr. Stephane Belair, MRDNAPS, IMPROVE, AIRS Data Networks

Thank you for your attention!