Wood‐smoke Wood smoke ‐ optical detection of optical detection of an important ambient pollutant “Fine!Dust‐Free” Congress Klagenfurt, 1 October 2009 g , Griša Močnik Aerosol d.o.o. grisa mocnik@aerosol si grisa.mocnik@aerosol.si
Aerosol Black Carbon Aerosol Black Carbon • BC is a product of incomplete combustion
dpp=20 nm
• BC not automatically related to CO2 emission • BC emissions can not be predicted: must be measured • BC particles from different sources can have different characteristics that produce different effects in the atmosphere:
dpp=46 nm
dm=472 nm
( (Coal/Diesel/Biomass, USA/Asia/Europe) l/ l/ / / ) Note change in scale
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Climate Change Effects of Black Carbon Climate Change Effects of Black Carbon BC is inert, long range transport! range transport! 2nd most important important warming agent: 15% ‐ 40% contribution to global warming
Haze over Asia: up to 40% of sunlight absorbed. Crop yields reduced ; local rainfall changed. S. Menon, J. Hansen et al., Climate Effects of Black Carbon Aerosols in China and India, Science (2002) 2250‐2253 3
Why are atmospheric aerosols important? Why are atmospheric aerosols important? • Public Health: loss of life in months due to PM2.5 (CAFE baselines, RAINS 2004)
2000
2010 2020 E Even with reduced emissions: statistical life loss ith d d i i t ti ti l lif l still around 5 months average in EU in 2020! 4
Health Effects Health Effects THE “HARVARD 6‐CITIES” STUDY Dockery et al., N. Engl. J. Med. 329: 1753 (1993) • Longitudinal cohort study of 8111 adults, 1974 – 1977 Steubenville, OH (S) Harriman, TN (H) St. Louis, MO (L) Watertown, MA (W) Topeka, KS (T) Portage, WI (P) • 1401 death certificates after 14‐16 yr follow‐up Smoking‐adjusted mortality rates (deaths/1000 population)
Mortality
Mortality Ratio Highest vs. g Lowest PM
All causes Lung cancer Other cardiopulmonary
1.26 (1.08 – 1.47) 1.37 (0.81 – 2.31) 1.37 (1.11 – 1.68)
Non‐cardiopulmonary
1.01 (0.79 – 1.30)
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Wood is a major energy source • wood/biomass is a sustainable fuel – trees recycle CO2 • burning biomass is a major energy source in the Alps • various combustion regimes: high‐efficiency distric heating ovens – individual wood‐stoves • possible extreme emissions of particulate matter • local and regional air quality issue – up to 40% of all particulate matter is wood‐smoke
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Wood‐smoke Wood smoke – part of PM10 part of PM10 • urban areas (Vienna, Graz, Salzburg, Kl Klagenfurt): f ) WS annual average g
5 ‐ 16%
winter
8 ‐ 22%
WS + HULIS 9 ‐ 20% 13 ‐ 28%
• rural rural areas areas (small villages in Lower (small villages in Lower Austria, Styria, Carinthia, Burgenland and Salzburg): WS annual average winter
8 ‐ 21%; 14 ‐ 32%
WS + HULIS 15 ‐ 28% 19 ‐ 42%
Aquella, TUW; A. Caseiro et al. / Atmospheric Environment 43 (2009) 2186–2195
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Advantages / Attributes of Optical Analysis Advantages / Attributes of Optical Analysis Typical chemical speciation time resolution – Typical chemical speciation time resolution hours, day! hours day! Optical methods – minute! • • • • •
Instantaneous Non‐destructive Mobile / Portable Added dimension ‐ time Added dimension – wavelength
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Analytical Instrument : Aethalometer™ • Collect sample continuously. • Measure optical absorption continuously Measure optical absorption continuously : optical wavelengths from 370 nm to 950 nm. Convert optical absorption to mass of BC. to mass of BC. • Convert optical absorption • Determine increment of mass in each time period. • Measure air flow rate; convert mass to concentration. • Real‐time data: 1 s / R l ti d t 1 / 1 min / 1 i / 5 minutes 5 i t – Dynamical, real‐time measurement, updated each period
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Aethalometer ‐ Measurement Principle Aethalometer Measurement Principle • Convert optical absorption p p to mass concentration. – BC (g/m3) = Diff. absorption (g/cm2) * Spot Area (cm2) / Volume (m3) – Diff. absorption ~ ATN @ sample spot / ATN @ reference spot
• • • •
Measurement precision: <5% Measurement precision: <5% Lower limit of detection: down to 100 picograms Real‐time data: 1 second // 5 minute // 1 hour Mass flow measurement and control
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Aethalometer Models : Continuous rack mount instruments Continuous rack mount instruments AE22 Dual beam – Ambient Air Quality Monitoring AE22 Dual beam – Ambient Air Quality Monitoring 9 9 9 9 9
Dual wavelength (880, 370nm) Local source identification Human health exposure Air quality monitoring network locations Roadside emissions studies
AE31 Spectrum Ambient Air Quality Monitoring AE31 Spectrum – Ambient Air Quality Monitoring 9 9 9 9 9
Seven wavelength (370, 470, 520, 590, 660, 880, and 950 nm) Local source identification Regional, Continental, Global Atmospheric studies Particle size distribution, radiative transfer Climate change, albedo, cloud modification
Project • Source apportionment on a short timescale – measurement of BC BC, wood burning vs. traffic. db i ffi • Instrumentation – PM10, gases (network), BC, absorption; site ,g ( ), , p ; pairs: urban, rural, background. • D Data processing t i – methodology, real‐time and off line data th d l l ti d ff li d t acquisition, relay and processing. • Partners: Carinthia, Klagenfurt, Aerosol...
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Aethalometer Installations in and around Klagenfurt
urban/roadside background
rural
Aethalometer installations in and around Klagenfurt
background
Voelkermarkterstr. / Enzenbergerstr. urban/roadside rural
PM10 and BC at the urban site BC diurnal profile ‐ KLAGENFURT
PM10 PM10 diurnal profile diurnal profile ‐ urban PM10 diurnal profile ‐ KLAGENFURT
90000
BC diurnal profile ‐ p urban
14000
80000
weekday Sunday
12000
70000
10000
60000 50000
8000
40000
6000
30000
4000 20000 weekday Sunday
10000
2000
0
30000
6000
25000
5000
PM10 data ‐ Carinthia
22 2:00
20 0:00
18 8:00
16 6:00
14 4:00
22:00
15
20:00
18:00
16:00
14:00
12:00
22:00
20:00
18:00
16:00
14:00
0 12:00
0 10:00
1000 8:00
5000 6:00
2000
4:00
10000
2:00
3000
0:00
15000
10:00
4000
8:00
20000
12 2:00
6:00 6
6:00
7000
10 0:00
4:00 4
4:00
8:00 8
2:00 2
BC “Weekday‐Sunday” ‐ urban BC "weekday ‐ y Sunday" ‐ y KLAGENFURT
2:00
8000
0:00 0
35000
PM10 “Weekday‐Sunday” PM10 Weekday Sunday ‐urban urban
0:00
PM10 "weekday ‐ Sunday" ‐ KLAGENFURT
222:00
220:00
118:00
116:00
114:00
112:00
110:00
8:00
6:00
4:00
2:00
0:00
0
PM10 and BC at the rural site BC Diurnal profile ‐ ZELL
PM10 diurnal profile PM10 diurnal profile ‐ p ‐ ZELL rural
90000 80000
14000
BC Weekday
12000
weekday Sunday
70000
BC diurnal profile ‐ p rural BC Sunday
10000
60000 50000
8000
40000
6000
30000
4000
12 2:00
14 4:00
16 6:00
18 8:00
12:00
14:00
16:00
18:00
22 2:00
10 0:00
10:00
22:00
8:00 8
8:00
20 0:00
6:00 6
6:00
20:00
4:00 4
4:00
5000
15000
4000
10000
3000
5000
2000
22:0 00
20:0 00
18:0 00
16:0 00
14:0 00
12:0 00
10:0 00
8:0 00
6:0 00
4:0 00
2:0 00
0:0 00
0
‐15000
2:00 2
6000
20000
‐10000
Sunday" ‐ ZELL ‐ rural BC BC "Weekday ‐ BC “Weekday‐Sunday” Weekday Sunday
7000
25000
‐5000
8000
2:00
PM 10 "weekday ‐ Sunday" ‐ ZELL PM10 PM10 “Weekday‐Sunday” Weekday Sunday ‐rural rural
0:00 0
22:00 2
20:00 2
18:00 1
16:00 1
14:00 1
12:00 1
8:00
10:00 1
30000
6:00
0 4:00
0
2:00
2000
0:00
10000
0:00
20000
1000 0
16
PM10 and BC in urban and rural sites
BC diurnal profile ‐ KLAGENFURT
PM10 diurnal profile ‐ p urban
90000
BC diurnal profile ‐ urban
14000
PM10 diurnal profile ‐ KLAGENFURT PM10 diurnal profile
80000
weekday Sunday
12000
70000
10000
60000 50000
8000
40000
6000
30000
4000 20000 weekday Sunday
10000
2000
0
80000
weekday Sunday
70000
10000
50000
8000
40000
22:00 2
20:00 2
18:00
16:00
14:00
BC Diurnal profile ‐ ZELL BC diurnal profile ‐ rural BC Weekday
12000
60000
12:00
10:00
8:00
6:00
4:00
2:00
14000
0:00
PM10 diurnal profile ‐ rural
22:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
90000
0
PM10 diurnal profile ‐ ZELL
BC Sunday
6000
30000 4000
20000
20:00
17
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
22:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
0:00
0
0
22:00
2000
10000
PM10 and BC at the rural site ‐ Diurnal profile ‐ ZELL weekdays 80000 BC Weekday
70000
PM10 Weekday
60000 50000 40000 30000 20000 10000
22:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
0
18
From Black&
to Color
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Wood‐smoke Wood smoke vs. diesel vs. diesel • measure attenuation with the Aethalometer • calculate absorption coefficient – p b, or mass • for completely black sample: b~1/λ • aromatic aromatic substances increase absorption: more at substances increase absorption: more at lower wavelengths!
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Wood‐smoke Wood smoke vs. diesel vs. diesel ‐ 7λ • measure attenuation with the A h l Aethalometer • calculate absorption coefficient ‐ b • for pure black carbon: b~1/λ • generalize Angstrom exponent: b 1/λα b~1/λ diesel: α ≈ 1 diesel: α ≈1 wood‐smoke: α ≈ 1.2 to 3.7 J. Sandradewi et al., A study of wood burning and traffic aerosols in an Alpine valley using a multi‐wavelength Aethalometer, Atmospheric Environment (2008) 101–112
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Angstrom coefficient Angstrom coefficient diurnal profile Angstrom coefficient 370 nm ‐ 520 nm diurnal profile ‐ KLAGENFURT urban
2.60
Angstrom coefficient
Angstrom coefficient diurnal profile diurnal profile ‐ rural 370 nm ‐ 520 nm ZELL
2.60
2 0 2.40
2.40
2.20
2.20
weekday Sunday
2.00
2.00
1 80 1.80
1 80 1.80 1.60
22
22:0 00
20:0 00
18:0 00
16:0 00
14:0 00
12:0 00
10:0 00
8:0 00
6:0 00
weekday Sunday
4:0 00
22:0 00
20:0 00
18:0 00
16:0 00
14:0 00
0.80
12:0 00
0.80 10:0 00
1.00
8:0 00
1.00 6:0 00
1 20 1.20
4:0 00
1 20 1.20
2:0 00
1.40
0:0 00
1.40
2:0 00
traffic
0:0 00
1.60
Wood‐smoke vs. diesel in PM10 2.60
PM10 diurnal profile ‐ p urban PM10 diurnal profile ‐ KLAGENFURT PM10 diurnal profile
90000 80000
2.40
WS
70000
2.20
traffic
weekday Sunday
2.00
60000
1 80 1.80
50000
WS
1.60
40000
traffic
1.40
30000
1.20
20000 weekday Sunday
80000
weekday Sunday
70000
2.20
WS
2.00
60000
22:00
18:00
16:00
14:00
12:00
8:00
10:00
2 40 2.40
Angstrom coefficient diurnal profile Angstrom coefficient 370 nm ‐ 520 nm diurnal profile ‐ p rural ZELL 6:00
2.60
2:00
PM10 diurnal profile ‐ rural
22:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
PM10 diurnal profile ‐ ZELL
0:00
0.80
0
20:00
1.00 4:00
10000
1.80
50000
1.60
40000 30000
WS traffic
traffic
0
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
22:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
0.80
23
22:00
1.00 20:00
10000
weekday Sunday
1.20
18:00
20000
1.40
0:00
90000
Angstrom coefficient diurnal profile Angstrom coefficient 370 nm ‐ 520 nm diurnal profile ‐ KLAGENFURT urban
Quantification • measure attenuation with the Aethalometer • calculate absorption coefficient b, and Angstrom coefficient α b = b(wood) + b(traffic) b(470 nm) / b(950 nm) = (470 nm / 950 nm) ‐α
• measure Total Carbon TC 1 b(wood) + c2 TC = c1 b( d) 2 b(traffic) + c3 b(t ffi ) 3
J. Sandradewi et al., Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter, ETS (2008) 3316–3323.
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Quantification (2) Quantification (2)
J. Sandradewi J. Sandradewi et al., Using Aerosol Light Absorption et al., Using Aerosol Light Absorption Measurements for the Quantitative Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter, ETS (2008) 3316–3323.
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Conclusions • we can measure Black Carbon and Wood‐Smoke with the Aethalometer • time resolution is 5 min • we we can investigate time evolution of BC and WS during can investigate time evolution of BC and WS during the day • qualitative qualitative Wood Wood‐Smoke Smoke determination determination – no additional no additional measurements, minimal postprocessing • quantitative Wood‐Smoke determination – q TC, C14, levoglucosan • all this and more automatically in AE‐33, coming 2010 26
AE‐51 AE 51 micro Aethalometer micro Aethalometer – personal exposure personal exposure • • • • • •
Dimensions: 14 cm x 6 cm x 3.5 cm W i ht 275 g Weight: 275 Run time: more than 24 hrs. Fast recharge: 2 ~ 4 hrs. Flow rate: 50 – 150 ml/min Sample filter: Pallflex ‘T60’ media housed in protective strip • Data storage: on‐board flash memory • Data transfer and setup: USB Data transfer and setup: USB • Sample collection: 3 mm spot
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16000
14000
Start driving from Hamburg
Arrive harbour, d i i driving off car
Heavy d iesel fumes from a mo bile h ome enter
12000
Sho rt stop open d oor, smo ker behind the
Stop at g as
Sh ort drive within harbour 3 min utes
8000
500m roadtunnel
6000 Stop at h arbour, o ut from the car to 18.25
4000
Drive in to car d eck on ferry
2000
16000
14000
21:03::00
20:49::00
20:35::00
20:21::00
20:07::00
19:53::00
19:39::00
19:25::00
19:11::00
18:57::00
18:43::00
18:29::00
18:15::00
18:01::00
17:47::00
17:33::00
17:19::00
17:05::00
16:51::00
16:37::00
16:23::00
16:09::00
15:55::00
15:41::00
15:27::00
15:13::00
0
14:59::00
y = 1.042x R² = 0 0.995 995
12000
10000
side‐by‐side comparison id b id i of two AE51
BC, ng/m3, s/n 109
BC (ng/m3)
S1 109 S1-109 Arrive parking
Enter autobahn
10000
6 hour drive from Hamburg to Sweden
S1-110
8000
6000
4000
2000
0 -2000
0
2000
4000
6000
8000
-2000
BC, ng/m3, s/n 110
10000
12000
28
14000
1600
Many thanks to: Dr. Wolfgang Hafner, Franz Weghofer, Mag. Sandra Habib, Dr. Erich Staudegger, many others (Klagenfurt), Dipl.Ing. Gerhard Heimburger, Johannes Maurer, Franz Hohenwarter, many others (Carinthia), , y ( ), Prof. Dr. Hans Puxbaum, Prof. Dr. Heidi Bauer (TUW), Dipl. Ing. Tanja Dobovičnik, Ing. Primož Vidmar, Damjan Zore, Petra Klavčič (Aerosol), everybody proposing PMinter AT‐SI project.
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Thank you for your attention! y y Questions?
Aerosol d.o.o. Kamniška 41 SI 1000 Ljubljana SI‐1000 Ljubljana Slovenia
tel: +386 59 191 220 fax: +386 59 191 221
grisa.mocnik@aerosol.si
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Wood‐smoke Wood smoke is a major local/regional AQ issue is a major local/regional AQ issue Wood‐smoke in winter – largest single contributor to PM! single contributor to PM! 15% to 40% of PM
Don Bosco – Don Bosco urban urban location in Graz
31 source: Aquella, TUW
Wood‐smoke Wood smoke vs. diesel vs. diesel ‐ 7λ
J. Sandradewi et al., A study of wood burning and traffic aerosols in an Alpine valley using a multi‐wavelength Aethalometer, Atmospheric Environment 42 (2008) 101–112
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Examples of ‘Micro’ Examples of Micro Aethalometer data Aethalometer data • Vertical profile from tethered balloon – Milano, Italy Black Carbon Concentration Vertical Profile 500 Ascent
400
Descent
300 200 100 0 0
5000
10000
15000
20000
BC concentration [ng/m3]
. Balloon operations courtesy of Dr. Luca Ferrero & Prof. Ezio Bolzzachini, . Dept. of Environmental Science, University of Milano – Bicocca, Italy
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