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

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

– April 19, 2023

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

– April 19, 2023

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

Miramichi

BathurstEdmundston

Grand Falls St Valentin

Montreal

– April 19, 2023

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

– April 19, 2023

Current AQHI Coverage

Reaches 65% of Canadians-> 88 forecast locations

New Brunswick

– April 19, 2023

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.

– April 19, 2023

Instrumentation

Pollutant Instrument Method Units Flow Rate Range Start DateSampling Interval

Detection Limit Calibration

NO API T200Chemilumines

enceppbv 0.5 l/min 0-500 ppbv Sep 14 2012 1 min 0.4 ppbv API T700

NO2 API T200Chemilumines


NOx API T200Chemilumines


O3 Thermo 49i Photometry ppbv 0.8 l/min 0-200 ppbv Sep 14 2012 1min 1.0 ppbv API T700

PM2.5

Thermo SHARP 5030i

Nephelometry and Beta detection

μg m-3 16.0 l/min 0-10000 g /m3 Sep 14 2012 1 min

± 2.0 μg/m3 <80 μg/m3 (1 hr.)

± 5 μg/m3 >80 μg/m3 (24 hr)

Foils for Beta

Delta Cal flow

CO API T300U IR Absorption ppbv 1.8 l/min 0-5000 ppbv Jan 04 2013 1 min <20 ppbv API T700

MeteorologyDavis

VantagePro 2

tempRH

MSL pressure,

wind spd / dirprecipitation

solar radiation

oC%mb

km h-1

mm

Sep 14 2012 5 min

– April 19, 2023

Local Emissions

– April 19, 2023

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

– April 19, 2023

Wind Stats, Seasonal14 Sep 2012 to 31 Dec 2013

5-Minute Average Wind Direction

– April 19, 2023

Performance of GEM-MACH - NO2

– April 19, 2023

Performance of GEM-MACH – O3

– April 19, 2023

Performance of GEM-MACH - PM2.5

– April 19, 2023

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

– April 19, 2023

Seasonal Performance

(=, +/-1): 99%

(=, +/-1): 98% (=, +/-1): 95%

(=, +/-1): 98%

– April 19, 2023

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.

– April 19, 2023

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

Tdy (AM fcst)

Obs

1 2 3 4 5 6

1 1 0 0 0 0 0 1

Fcst 2 1 59 26 0 0 1 87

3 2 8 23 2 0 0 35

4 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

4 67 49 2 0 1 123

Percent Correct 67.5

Percent Correct +/- 1 97.6

Tdy (AM fcst)

Obs

1 2 3 4 5 6

1 13 50 6 0 0 0 69

Mdl 2 16 101 79 4 0 2 202

3 0 3 8 2 0 0 13

4 0 0 1 0 0 0 1

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

29 154 94 6 0 2 285



Forecast Model

– April 19, 2023

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

Forecast ModelTngt (AM fcst)

Obs

1 2 3 4 5 6

1 1 0 0 0 0 0 1

Fcst 2 8 55 33 0 0 0 96

3 1 6 16 4 0 0 27

4 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

10 61 49 4 0 0 124



Tngt (AM fcst)

Obs

1 2 3 4 5 6

1 21 42 5 0 0 1 69

Mdl 2 24 92 82 5 0 0 203

3 1 1 5 3 0 0 10

4 0 0 0 0 0 0 0

5 0 0 1 0 0 0 1

6 0 0 0 0 0 0 0

46 135 93 8 0 1 283



– April 19, 2023

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

Forecast ModelTmrw (AM fcst)

Obs

1 2 3 4 5 6

1 1 0 0 0 0 0 1

Fcst 2 6 57 35 2 0 2 102

3 0 2 19 1 0 0 22

4 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

7 59 54 3 0 2 125



Tmrw (AM fcst)

Obs

1 2 3 4 5 6

1 11 37 6 0 0 0 54

Mdl 2 13 102 92 5 0 2 214

3 2 2 9 3 0 0 16

4 0 0 0 0 0 0 0

5 0 0 1 0 0 0 1

6 0 0 0 0 0 0 0

26 141 108 8 0 2 285



– April 19, 2023

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

Forecast ModelTngt (PM fcst)

Obs

1 2 3 4 5 6

1 0 0 0 0 0 0 0

Fcst 2 3 40 9 0 0 0 52

3 0 0 12 1 0 0 13

4 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

3 40 21 1 0 0 65



Tngt (PM fcst)

Obs

1 2 3 4 5 6

1 17 43 5 0 0 1 66

Mdl 2 21 97 83 5 0 0 206

3 2 1 5 3 0 0 11

4 0 0 0 0 0 0 0

5 0 0 1 0 0 0 1

6 0 0 0 0 0 0 0

40 141 94 8 0 1 284



– April 19, 2023

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

Forecast ModelTmrw (PM fcst)

Obs

1 2 3 4 5 6

1 0 0 0 0 0 0 0

Fcst 2 2 33 18 0 0 0 53

3 0 0 10 1 0 0 11

4 1 0 0 0 0 0 1

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

3 33 28 1 0 0 65



Tmrw (PM fcst)

Obs

1 2 3 4 5 6

1 11 37 6 0 0 0 54

Mdl 2 13 103 92 5 0 2 215

3 2 2 9 3 0 0 16

4 0 0 0 0 0 0 0

5 0 0 1 0 0 0 1

6 0 0 0 0 0 0 0

26 142 108 8 0 2 286



– April 19, 2023

Air Quality Events• 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

06Z Feb 26 2013

– April 19, 2023

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

– April 19, 2023

AcknowledgementsCo-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

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

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