Performance of Continuous

PM2.5 Monitors at a Monitoring Site in Ottawa, Canada

Performance of Continuous PM2.5 Monitors at a Monitoring

Site in Ottawa, Canada

Tom Dann

Luc White, Alain Biron

Environment Canada, ETC, Ottawa

Tom Dann

Luc White, Alain Biron

Environment Canada, ETC, Ottawa

Environment EnvironnementCanada Canada

NESCAUM Monitoring and Assessment Committee Meeting Troy, NH April 24/25, 2007

Ottawa Monitoring Site

Study Period: May 2004 – present> 500 days of data for 1 or more continuous instruments with filter-based data

24h PM2.5 Max: 64 µg/m³ 98th Perc: 37 µg/m³ Mean: 10.0 µg/m³

Temperature Range: -27°C to 29°C Filter based samplers: R&P Partisol FRM

and R&P Partisol Sequential Dichot

Ottawa Monitoring Site

Met-One BAM 1020 May 2004

Met-One BAM 1020 (new) Nov 2006

TEOM SES (30°C) May 2004

TEOM FDMS July 2004

GRIMM 180 (PM10, PM2.5, PM1.0) Oct. 2005

Thermo SHARP 5030 June 2006

Met-One BAM 1020

low-level C14 radiation source

uses beta-ray attenuation and a filter tape system

inlet is heated but filter is at ambient (station) temperature

humidity problems in early units caused higher readings in the summer

‘smart heater’ is turned on by a relative humidity sensor

hourly average output

Linear regression results for 24h dataBAM Ottawa (Warm Seasons 2004-2006)

0 10 20 30 40 50 60

Filter Based Sampler (µg/m³)

0

10

20

30

40

50

60

BA

M 1

02

0 (

µg

/m³)

BAM 45%

BAM 35%

BAM45 = -0.9 + 1.34 * FILTERr = 0.984 n = 54BAM35 = -0.6 + 1.09 * FILTERr = 0.961 n = 152

Linear regression results for 24h dataBAM with 35% Humidity Setting Ottawa (2005-2006)

0 10 20 30 40 50

Filter Based Sampler (µg/m³)

0

10

20

30

40

50

BA

M3

5 (

µg

/m³)

BAM35_Cold

BAM35_Warm

BAM35_Cold = -1.7 + 1.02 * FILTERr = 0.983 n = 72BAM35_Warm = -0.6 + 1.09 * FILTERr = 0.961 n = 152

Replacement of BAM

Linear regression results for 24h dataTEOM-FDMS Ottawa (2004-2006)

0 10 20 30 40 50 60 70 80

Filter-based Sampler (µg/m³)

0

10

20

30

40

50

60

70

80

TE

OM

-FD

MS

(µ

g/m

³)

FDMS_Cold FDMS_Warm

FDMS_Cold = -0.8 + 1.07 * FILTERr = 0.993 n = 167FDMS_Warm = -0.3 + 1.08 * FILTERr = 0.969 n = 180

Grimm 180 Multi-channel Aerosol Spectrometer

Simultaneous measurement of PM1.0, PM2.5 and PM10

Particle counts by size (31 channels) and mass determinationusing algorithmsNo size cut on inlet – moisture control using Nafion dryerTruly continuous (real-time)

Results for 24h PM2.5 data: Grimm 180 (Ottawa) Jan. 2006 – Dec. 2006

0 5 10 15 20 25 30 35 40

Filter-based Sampler (µg/m³)

0

5

10

15

20

25

30

35

40

GR

IMM

(µ

g/m

³)

GRIMM_Cold Season GRIMM_Warm Season

GRIMM_Cold = 0.5 + 1.05 * FILTERr = 0.964 n = 47GRIMM_Warm = 0.03 + 1.07 * FILTERr = 0.941 n = 60

Grimm 180 Results – Ottawa Summer 2006

0

5

10

15

20

25

30

35

Conc.

(µg/m

³)

GrimmDichot

Humidity Effects on Grimm 180 (Ottawa)?

-10 -5 0 5 10 15 20 25

Dew Point Temp. (°C)

-6

-4

-2

0

2

4

6

8

GR

IMM

min

us

FIL

TE

R (

µg

/m³)

24h DataApril - September 2006

Results for 24h Coarse data: Grimm 180 (Ottawa) Jan. 2006 – Mar. 2007

0 5 10 15 20 25 30

Dichot Coarse (µg/m³)

0

5

10

15

20

25

30

GR

IMM

PM

10 -

PM

2.5 (µ

g/m

³)

GRIMM Coarse Before RecalGRIMMC = 0.8 + 0.24 * DICH r = 0.711

GRIMM Coarse After RecalGRIMMC = -1.0 + 0.82 * DICH r = 0.977

Results for 24h PM10 data: Grimm 180 (Ottawa) Jan. 2006 – Mar. 2007

0 5 10 15 20 25 30 35 40 45

Dichot PM10 (µg/m³)

0

5

10

15

20

25

30

35

40

45

GR

IM P

M10

(µ

g/m

³)

GRIMM PM10 Before RecalGRIMM10 = -0.4 + 0.85 * DICH r = 0.947

GRIMM PM 10 After RecalGRIMM10 = -0.2 + 0.93 * DICH r = 0.928

Grimm 180 Results – Ottawa January 29-31, 2007

0

10

20

30

40

50

60

70

80

Conc.

(µg/m

³)

FINECOARSE

Thermo SHARP 5030 Synchronized Hybrid Ambient Real-time Particulate Monitor

Truly continuous (real-time) Combination nephelometer

& beta-ray attenuation Light scattering photometer

is continuously calibrated by beta attenuation mass sensor

Intelligent Moisture Reduction (IMR) System heating the inlet tube; threshold is set at 40%

Linear regression results for 24h data: Sharp 5030 (Ottawa Jun. – Dec. 2006)

0 5 10 15 20 25 30 35 40 45

Filter-based Sampler (µg/m³)

0

5

10

15

20

25

30

35

40

45

Th

erm

o S

ha

rp (

µg

/m³)

TSHARP = 1.20+1.00*FILTERr = 0.958 n = 68

Performance During Air Mass Change (Ottawa September 2006)

10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00

Hour of Day

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

20

22

24

Co

nce

ntr

atio

n (

µg

/m³)

TEOM SES

TEOM FDMS

GRIMM

THERMO SHARP

September 27 - 29, 2006

Cost Comparisons for Continuous PM2.5 Instruments (Canadian$):

Met-One BAM 1020$25,000

R&P 1400AB TEOM$25,400

R&P SES Kit $ 4,750

R&P 8500 FDMS Kit$10,700

TEOM-FDMS$36,100

GRIMM 180 (PM10, PM2.5, PM1.0)

$31,000 Thermo SHARP 5030

$26,500

2006 Performance Based on EPA ARM Template

Instrument N Slope Intercept

r

TEOM-SES 133 0.80 0.04 0.940TEOM-FDMS

126 1.06 -0.43 0.961

BAM 126 1.01 0.34 0.942GRIMM 107 1.06 0.27 0.953SHARP 68 1.00 1.20 0.958

PM Workshop – Ottawa April 2007

Presentations and Summary:

http://www.etc-cte.ec.gc.ca/NAPSAnnualRawData/Password: ozoneFolder: PMWORKSHOP2007