predicting the aqhi without aid of observations: results from the northern new brunswick study
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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 11 th , 2014. Objectives of this study. - PowerPoint PPT PresentationTRANSCRIPT
Predicting the AQHI without aid of observations: results from the northern New Brunswick study
National Air Quality ConferenceDurham, NCDaniel JubainvilleEnvironment CanadaMeteorological Service of CanadaFeb 11th, 2014
Page 2 – April 24, 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
Page 3 – April 24, 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
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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
Page 5 – April 24, 2023
Current AQHI Coverage
Reaches 65% of Canadians-> 88 forecast locations
New Brunswick
Page 6 – April 24, 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.
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Instrumentation
Pollutant Instrument Method Units Flow Rate Range Start Date Sampling 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
enceppbv 0.5 l/min 0-500 ppbv Sep 14 2012 1 min 0.4 ppbv API T700
NOx API T200Chemilumines
enceppbv 0.5 l/min 0-500 ppbv Sep 14 2012 1 min 0.4 ppbv API T700
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
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Local Emissions
Page 9 – April 24, 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
Page 10 – April 24, 2023
Wind Stats, Seasonal14 Sep 2012 to 31 Dec 2013
5-Minute Average Wind Direction
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Performance of GEM-MACH - NO2
Page 12 – April 24, 2023
Performance of GEM-MACH – O3
Page 13 – April 24, 2023
Performance of GEM-MACH - PM2.5
Page 14 – April 24, 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
Page 15 – April 24, 2023
Seasonal Performance
(=, +/-1): 99%
(=, +/-1): 98% (=, +/-1): 95%
(=, +/-1): 98%
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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.
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Forecasts issued 6:00 AM AST/ADTToday (January 17th – November 4th, 2013)
Tdy (AM fcst) Obs
1 2 3 4 5 61 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 67.5Percent Correct +/- 1 97.6
Tdy (AM fcst) Obs
1 2 3 4 5 61 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
Percent Correct 42.8Percent Correct +/- 1 95.8
Forecast Model
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Forecasts issued 6:00 AM AST/ADTTonight (January 17th – November 4th, 2013)
Forecast ModelTngt (AM fcst)
Obs1 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
Percent Correct 58.1Percent Correct +/- 1 99.2
Tngt (AM fcst)Obs
1 2 3 4 5 61 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
Percent Correct 41.7Percent Correct +/- 1 95.4
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Forecasts issued 6:00 AM AST/ADTTomorrow (January 17th – November 4th, 2013)
Forecast ModelTmrw (AM fcst)
Obs1 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
Percent Correct 61.6Percent Correct +/- 1 96.8
Tmrw (AM fcst)Obs
1 2 3 4 5 61 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
Percent Correct 42.8Percent Correct +/- 1 94.4
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Forecasts issued 5:00 PM AST/ADTTonight (January 17th – November 4th, 2013)
Forecast ModelTngt (PM fcst)
Obs1 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
Percent Correct 80.0Percent Correct +/- 1 100.0
Tngt (PM fcst)Obs
1 2 3 4 5 61 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
Percent Correct 41.9Percent Correct +/- 1 95.1
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Forecasts issued 5:00 PM AST/ADTTomorrow (January 17th – November 4th, 2013)
Forecast ModelTmrw (PM fcst)
Obs1 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
Percent Correct 66.2Percent Correct +/- 1 98.5
Tmrw (PM fcst)Obs
1 2 3 4 5 61 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
Percent Correct 43.0Percent Correct +/- 1 94.4
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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 2013GEM-MACH forecast 2/2/2SPC forecast 3/3/3Actual AQHI 4/4/3
• Missed smoke events/false alarms
06Z Feb 26 2013
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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
Page 24 – April 24, 2023
AcknowledgementsCo-authors:Environment Canada – David Waugh, Alan Wilson, Steve Beauchamp, Doug SteevesDalhousie University – Mark Gibson, Gavin King, James Kuchta
Partners: Environment Canada – Craig Stroud, David AnselmoCollège Communautaire du Nouveau-Brunswick Campbellton Campus – Réjean SavoieNew Brunswick Environment & Local Government – Darrell Welles, Eric BlanchardHealth Canada – Kamila Tomcik, Christina Daly