An overview of recent OC/EC and

organics aerosol particle

measurements of TROPOS ACD

Hartmut Herrmann, Dominik van Pinxteren, Laurent

Poulain, Konrad Müller and Gerald Spindler

TROPOS Leipzig

Atmospheric Chemistry Department (ACD)

Permoserstr. 15, 04318 Leipzig, Germany

TROPOS Forthcoming Atmospheric Chemistry Lab

Atmospheric Chemistry @ TROPOS ACD

Field studies • Aerosol measurements

in the polluted regime (East middle Europe, China)

under marine conditions (Baltic, northern tropical atlantic), the

SML, concerted measurements (MARPARCLOUD; MARSU)

• Field aerosol-cloud studies (such as FEBUKO or HCCT-2010)

• Biomass burning (such as SEIFFEN)

• Dust-related studies (CVAO, SALTRACE)

• Dedicated case studies (often on regional air quality, with

agencies)

Lab studies • ‚New‘ gas phase chemistry ( Criegees, HOMs, terpenes, isoprene)

• Chamber studies (SOA)

• Aqueous phase chemistry (aqSOA, radicals, kinetics and

photochemistry)

Multiphase Modelling

• Different flavours of CAPRAM

• (1) Leipzig Aerosol 2013-2015 project

PM Sources & Changes

Emphasis on primary emission species

• (2) BB studies in Germany

Seiffen study 2008

Melpitz study 2012 - 2014

• Summary

Outline

Part 1: LfULG Aerosol Project (2013 - 2015)

Tasks and goals ‘Leipzig Aerosol 2013-2015’ project

• Scientific measurements to characterize air quality at 4 sites in/around Leipzig

• Continuous number size distributions + BC ( cf. Alfred)

• Size-resolved chemical particle characterization during selected days

Assess local/regional air quality beyond legislative parameters

• Source apportionment of PM Identify main sources and analyze impact of biomass burning and

long-range transport

• Compare with data from similar project in 1999/2000 Assess changes in air quality

Experimental approach

• 2 Campaigns (summer+winter 2013-2015)

• 4 Sampling sites in parallel • 24h samples with 5-stage Berner

impactor during 21 sampling days per season

• Comprehensive chemical characterisation

inorganic ions OC/EC, WSOC organics: oxalate, mono- saccharides, alkanes, PAHs, hopanes metals

LMI: Traffic EIB: Traffic/Residential

TRO: Urban MEL: Regional

Results: Composition and Sources

Outline

Multidimensional dataset: Season/Site/Species/Size/Sector

Sommer

LMI

Sommer

EIB

Sommer

TRO

Sommer

MEL

Winter

LMI

Winter

EIB

Winter

TRO

Winter

MEL

0

10

20

0

1

2

3

01234

01234

0.00

0.05

0.10

0.15

0.0000.025

0.0500.075

0.100

0.00

0.04

0.08

0.12

0.0000.0250.0500.0750.1000.125

0.000.010.020.030.04

0.0

0.5

1.0

1.5

012345

0.00.51.01.52.0

Masse

Am

moniu

mN

itrat

Sulfa

tC

hlo

ridN

atriu

mK

aliu

mC

alc

ium

Oxala

tO

CE

CW

SO

C

0.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.0

Aerodynamic Diameter (µm)

Ko

nz

en

tra

tio

nµ

gm

3

Anströmung

West

Ost

Sommer

LMI

Sommer

EIB

Sommer

TRO

Sommer

MEL

Winter

LMI

Winter

EIB

Winter

TRO

Winter

MEL

0

10

20

0

1

2

3

01234

01234

0.00

0.05

0.10

0.15

0.0000.025

0.0500.075

0.100

0.00

0.04

0.08

0.12

0.0000.0250.0500.0750.1000.125

0.000.010.020.030.04

0.0

0.5

1.0

1.5

012345

0.00.51.01.52.0

Masse

Am

moniu

mN

itrat

Sulfa

tC

hlo

ridN

atriu

mK

aliu

mC

alc

ium

Oxala

tO

CE

CW

SO

C

0.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.0

Aerodynamic Diameter (µm)

Ko

nz

en

tra

tio

nµ

gm

3

Anströmung

West

Ost

Mean size distributions of main PM constituents per season, site, species, and inflow sector - here OC, EC, WSOC:

Co

nce

ntr

atio

n /

mg

m-3

Sommer

LMI

Sommer

EIB

Sommer

TRO

Sommer

MEL

Winter

LMI

Winter

EIB

Winter

TRO

Winter

MEL

0

10

20

0

1

2

3

01234

01234

0.00

0.05

0.10

0.15

0.0000.025

0.0500.075

0.100

0.00

0.04

0.08

0.12

0.0000.0250.0500.0750.1000.125

0.000.010.020.030.04

0.0

0.5

1.0

1.5

012345

0.00.51.01.52.0

Masse

Am

moniu

mN

itrat

Sulfa

tC

hlo

ridN

atriu

mK

aliu

mC

alc

ium

Oxala

tO

CE

CW

SO

C

0.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.0

Aerodynamic Diameter (µm)

Ko

nz

en

tra

tio

nµ

gm

3

Anströmung

West

Ost

Inflow

West

East

Multicategorial dataset: Season/Site/Species/Size/Sector

Sommer

LMI

Sommer

EIB

Sommer

TRO

Sommer

MEL

Winter

LMI

Winter

EIB

Winter

TRO

Winter

MEL

0.0

2.5

5.0

7.5

0

100

200

300

0

2

4

6

05

10152025

0.000.250.500.751.00

0

10

20

01234

0.00

0.25

0.50

0.75

0100200300400

Zuckera

lk.

Anhydro

z.

Zucker

Alk

ane

Alk

anone

PA

Ks

OxyP

AK

sH

opane

Meta

lle

0.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.0

Aerodynamic Diameter (µm)

Ko

nz

en

tra

tio

nn

gm

3

Anströmung

West

Ost

Sugaralcohols (arabitol)

Anhydrosugars (levo)

Sugars

Alkanes

Alkanones

PAHs

Oxy-PAHs

Hopanes (traffic/coal burning)

Metals

Sommer

LMI

Sommer

EIB

Sommer

TRO

Sommer

MEL

Winter

LMI

Winter

EIB

Winter

TRO

Winter

MEL

0

10

20

0

1

2

3

01234

01234

0.00

0.05

0.10

0.15

0.0000.025

0.0500.075

0.100

0.00

0.04

0.08

0.12

0.0000.0250.0500.0750.1000.125

0.000.010.020.030.04

0.0

0.5

1.0

1.5

012345

0.00.51.01.52.0

Masse

Am

moniu

mN

itrat

Sulfa

tC

hlo

ridN

atriu

mK

aliu

mC

alc

ium

Oxala

tO

CE

CW

SO

C

0.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.00.1 1.0 10.0

Aerodynamic Diameter (µm)

Ko

nz

en

tra

tio

nµ

gm

3

Anströmung

West

Ost

Inflow

West

East

Co

nce

ntr

atio

n /

mg

m-3

Source apportionment approaches I: Lenschow

Positive Matrix Factorization (PMF)

Macrotracer

Lenschow et al., 2001: PM as superposition of sources in different regions

traffic increment = c(LMI, EIB) - c(TRO)

urban background increment = c(TRO) - c(MEL)

regional background = c(MEL)

Factor analysis method to extract underlying sources in multidimensional datasets

PMBiomassBurning = c(Levoglucosan) x 11 (Schmidl et al., 2008) PMFungalSpores = c(Arabitol) x 28 (Bauer et al., 2008)

Use established ratios of characteristic constituents to derive source conc.

Lenschow: Local vs. regional PM mass contributions at LMI

LMI site Typical regional (transported) contributions: - 20-30% for ultrafines - 60-70% for accumulation

mode particles - 20-30% for coarse particles

PM10: 50 – 60% regional contribution

PM1.2: 60 – 70% regional contribution

Comparison of source apportionment approaches

PM10 Traffic Source Lenschow: 6.5 µg m-3 (23 %)

PMF: 5.3 µg m-3 (30 %)

PM10 Biomass Combustion Macrotracer: 1.3 µg m-3 (5 %)

PMF: 1.4 µg m-3 (6 %)

Generally good agreement between approaches Confidence in PMF solution (more sources than other approaches)

PM10 SIA Macrotracer: 6.6 µg m-3 (27 %)

PMF: 8.2 µg m-3 (37 %)

PM10 Fungal Spores Macrotracer: 0.08 µg m-3 (0.5 %)

PMF: 0.18 µg m-3 (1.2 %)

Project mean concentrations and mass fractions

Positive Matrix factorisation (PMF)

Variant of factor analysis (Paatero und Taper, 1994)

PMF in this project

• EPA PMF 5.0 • „unconstrained“ solutions • all sites pooled into 1 dataset • Five separate PMF solutions for 5 particle size intervals • 7 – 9 factors used per size interval, based on

- mathematical parameters (Q/Qexp, distribution of normalised residuals) - correlation of modelled vs. measured mass concentrations, - plausible chemical source profiles, - plausible time series of source contributions at different sites

𝑋 = 𝐺 ∙ 𝐹 + 𝐸 with X: matrix of samples (n observations x m species) G: matrix of source contributions (n observations x p sources) „weights“ F: matrix of source profiles (p sources x m species) factors, emission profiles E: matrix of residuals (difference between measured data X and modelled data Y (=GF))

𝐹𝑖𝑗 , 𝐺𝑖𝑗 ≥ 0

PMF: Example of source attribution

0.00

0.25

0.50

0.75

1.00

mass

am

moniu

m

nitra

tesulfate

WS

OC

WIS

C

oxala

tele

voglu

cosan

C22

C23

C24

C25

C26

C27

C28

C29

C30

C31

C32

C33

C34

FLU

PY

R

RE

TB

NT

HIO

CC

PY

R

BkF

LU

BeP

YR

BaP

YR

BghiP

ER

NH

OP

abH

OP

ab22S

HH

OP

ab22R

HO

P

K Ca Ti

Mn

Fe

Cu

Zn

As

Se

Ba

Pb

ma

ss

co

ntr

ib.

ba

rss

pe

cie

sc

on

trib

.d

ots Factor profile

LMI EIB TRO MEL

0.0

0.5

1.0

1.5

2.0

2.5

10

20

30

40

50

10

20

30

40

50

10

20

30

40

50

10

20

30

40

50

Sample ID

Co

nc

en

tra

tio

nµ

gm

3

Season

Summer

Winter

LMI EIB TRO MEL

0.0

0.5

1.0

1.5

2.0

2.5

10

20

30

40

50

10

20

30

40

50

10

20

30

40

50

10

20

30

40

50

Sample ID

Co

nc

en

tra

tio

nµ

gm

3

Season

Summer

Winter

Mass contribution (bars): Species = 1

Species contribution (dots): Factors = 1

Factor concentrations

„Time series“ during seasons

90% of source mass is water-insoluble carbon

80 % of hopane conc. in this source

traffic exhaust or coal?

Concentrations high only at traffic sites

no seasonal trend

Traffic exhaust

Hopanes Water-insoluble carbon

PMF: Identified sources

Source Size range Main constituents Marker compounds

Traffic exhaust ultrafine coarse

WISC Hopanes, <C25 n-

Alkanes

Traffic ultrafine

fine coarse

WISC, (Fe) Copper, Barium

Coal Combustion ultrafine

fine (coarse)

WISC, Sulfate PAHs, Arsenic,

(Hopanes)

Biomass Combustion ultrafine

fine coarse

WISC,WSOC Levoglucosan,

Potassium

Photochemistry ultrafine

fine Sulfate, WSOC Oxalate

Secondary (inorganic) aerosol

fine (coarse)

Nitrate, Ammonium, Sulfate

WSOC

Cooking ultrafine

fine WISC odd n-Alkanes

Crust material (urban) coarse Nitrate, WSOC odd n-Alkanes,

Magnesium, Calcium, Oxalate

Fungal spores coarse WISC, WSOC Arabitol

Fresh sea salt and road salt coarse Chloride, Sodium Magnesium

Aged sea salt coarse Nitrate Sodium, Magnesium

Sources in ultrafine particles (0.05 – 0.14 µm)

Traffic at traffic sites: ca. 0.2 – 1 µg m-3, 20 – 70 % of stage 1 mass (means)

Photochem. at urban sites in summer: ca. 0.2 – 0.6 µg m-3, 20 – 50 %

Solid fuel combustion in winter: ca. 0.2 – 0.9 µg m-3, 20 – 70 %

Impactor Stage 1

Su West Su East Wi West Wi East All

0.9

30

.93

0.9

30

.93

0.9

30

.93

0.7

50

.75

0.7

50

.75

0.7

50

.75

0.4

20

.42

0.4

20

.42

0.4

20

.42

0.0

92

0.0

92

0.0

92

0.0

92

0.0

92

0.0

92

41

13

30

13

17

37

36

22

57

14

39

17

301

.41

.41

.41

.41

.41

.4

1.4

1.4

1.4

1.4

1.4

1.4

0.7

80

.78

0.7

80

.78

0.7

80

.78

0.3

90

.39

0.3

90

.39

0.3

90

.39

21

20

44

11

15

39

28

27

10

50

30

11

50

1.5

1.5

1.5

1.5

1.5

1.5

0.9

0.9

0.9

0.9

0.9

0.9

0.5

70

.57

0.5

70

.57

0.5

70

.57

0.3

60

.36

0.3

60

.36

0.3

60

.36

49

28

16

29

24

29

11

10

29

12

39

21

24

37

17

0.9

40

.94

0.9

40

.94

0.9

40

.94

1.3

1.3

1.3

1.3

1.3

1.3

1.1

1.1

1.1

1.1

1.1

1.1

0.9

30

.93

0.9

30

.93

0.9

30

.93

31

40

28

18

34

12

23

46

19

12

38

30

19

1.2

1.2

1.2

1.2

1.2

1.2 1

1

1

1

1

1

0.6

60

.66

0.6

60

.66

0.6

60

.66

0.3

80

.38

0.3

80

.38

0.3

80

.38

34

21

15

17

21

14

19

19

21

19

13

30

28

15

26

30

22

0

25

50

75

100

0.0

5-0

.14µ

m

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

Ma

ss

Fra

ctio

n%

Sources

Sea Salt

urban Dust

Spores

Cooking

Photochem.

Sec. Aer.

Biom. Comb.

Coal Comb.

Traffic

Tr. Exhaust

Detailed chemical UFP composition and source apportionment can be done this way !

Traffic: ca. 2 – 7 µg m-3, 20 – 40 % of PM10 mass (8 % Wi East) Combustion particles: 0.4 – 1.3 µg m-3, 3 – 12 % (summer), 2- 18 µg m-3, 10 – 45 % (winter) Sec. material: ca. 4 – 18 µg m-3, 30 – 45 %

Exceedings days at traffic sites:

Traffic: 15 %, Regional combustion aerosol: 40 %, Sec. aerosol: 30 %

Su West Su East Wi West Wi East All

13

13

13

13

13

13

13

13

13

13

13

13

13

13

13

13

13

13

13

13

10

10

10

10

10

10

10

10

10

10

6

6

6

6

6

6

6

6

6

6

24

15

15

15

15

16

17

15

17

21

21

20

29

34

10

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

15

12

12

12

12

12

12

12

12

12

12

6.7

6.7

6.7

6.7

6.7

6.7

6.7

6.7

6.7

6.7

25

11

28

12

21

25

23

13

42

14

58

18

18

18

18

18

18

18

18

18

18

16

16

16

16

16

16

16

16

16

16

12

12

12

12

12

12

12

12

12

12

8

8

8

8

8

8

8

8

8

8

32

34

10

15

13

38

13

11

15

12

47

14

10

58

12

36

36

36

36

36

36

36

36

36

36

39

39

39

39

39

39

39

39

39

39

32

32

32

32

32

32

32

32

32

32

28

28

28

28

28

28

28

28

28

28

30

39

34 11

30

30

11

44

43

13

29 11

21

21

21

21

21

21

21

21

21

21

20

20

20

20

20

20

20

20

20

20

16

16

16

16

16

16

16

16

16

16

11

11

11

11

11

11

11

11

11

11

20 11

32

11

13

28 10

14

13

37

14

20

39

18

0

25

50

75

100

PM

10

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

LM

I

EIB

TR

O

ME

L

Ma

ss

Fra

ctio

n%

Sources

Sea Salt

urban Dust

Spores

Cooking

Photochem.

Sec. Aer.

Biom. Comb.

Coal Comb.

Traffic

Tr. Exhaust

Summary: Total source contributions for PM10

Main sources at most polluted sites:

Implications for air pollution abatement strategies

Traffic - Further development of low emission zone, e.g. include construction machines - Further promotion of public transportation systems - Further improvements in bicycle roads - Optimize car traffic flow through city

Secondary aerosol - Larger scale reductions of SO2 and NOx

- Reduction of Diesel NOx

- Reduction of agricultural NH3 emissions - Promotion of alternative energy systems - Promotion of energy efficiency (e.g. heat insulation, modern heating systems)

Combustion aerosol - Limitîng values for small scale heating stoves (1. BImSchV 2010): Filters ? - Further emission reductions in Eastern Europe, esp. solid fuel burning for heating

Comparison with year 2000

Summer

Traffic

Summer

Urban

Summer

Regional

Winter

Traffic

Winter

Urban

Winter

Regional

02468

0.00.20.40.60.8

0.00.5

1.0

1.5

Masse

OC

EC

0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0

Aerodynamic Diameter (µm)

Inc

rem

en

tµ

gm

3

Year

2000

2013

2013/14/15

Project in 2000: - 3 sites (LMI, TRO, MEL) - 8 samples per season - only West sector sampled - only 4 stages analysed (PM3.5)

Compare only „West“ days Conc. at all sites decreased Calculate Lenschow increments

Part 2: (A) LfULG Seiffen Biomass burning study

(2008)

Seiffen – population 2,600

Production of wood decoration

Seiffen (Erzgebirge)

Overview of the Seiffen 2008 measurements

1.0

0.8

0.6

0.4

0.2

0.0

ma

ss

fra

cti

on

13.01.2008 17.01.2008 21.01.2008 25.01.2008 29.01.2008 02.02.2008

date and time

25

20

15

10

5

0

ma

ss

co

nc

en

tra

tio

n

(µ

g m

-3)

Organics Nitrate Sulfate Ammonium Chloride

BBOA

HOA

OOA

HOA = liquid fuel (car

exhaust and house heating)

BBOA = solid fuel (biomass

burning)

OOA = oxygenated Organic

(aged, transported OA)

3.0

2.5

2.0

1.5

1.0

0.5

0.0ma

ss

co

nc

en

tra

tio

n (

µg

m-3

)

20151050

Diurnal Hour

Week #4 BBOA HOA OOA

1.0

0.8

0.6

0.4

0.2

0.0ma

ss

co

nc

en

tra

tio

n (

µg

m-3

)

20151050

Diurnal Hour

Weekend #2

Poulain et al., 2011

15

10

5

0

mass c

oncentr

ation (

µg m

-3)

11.01.2008 16.01.2008 21.01.2008 26.01.2008 31.01.2008

dat

OOA BBOA HOA

●Based on the meteorological data, back trajectories, and tracer concentrations,

these three periods are identified as ‘wood smoke’ episodes.

●These periods are characterised by at least eight times higher average

concentrations of levoglucosan than the background periods.

Seiffen PM10 Time Series

October 07 November 07 December 07 January 08 February 08 March 08 April 08

Monosaccharide a

nhydrides [ng m

-3]

0

200

400

600

800

1000

1200

1400

1600P

M10 [m

g m

-3]

0

10

20

30

40

50

WS

OC

[mg m

-3]

0

1

2

3

4

5

Sum monosaccharide anhydrides

Mass PM10

WSOC

Relative Contribution of Wood smoke to PM10 and WSOC at Seiffen

BBOA and PAHs

AMS PAHs mass concentration estimated using Dzepina et al. (2007)

agrees quite well with the total PAHs identified on PM1 filters.

Influence of wood burning to total PAHs

MA = monosaccharides anhydrides

Using the off-line

measurements, ratio

PAHs/MA = 0.03 in agreement

with literature

A similar ratio was considered

for the online PAH/BBOA

< 0.03 PAH wood burning

(PAHwb in blue)

>0.03 other PAHs sources

(traffic, house heating…)

(PAHno-wb in red)

PAHwb = 1.5% of the

emitted mass of BBOA

PAHwb = 62% of total

PAH mass conc.

More info here

Part 2: (B) Melpitz POA AMS study (2012 - 2014)

29/17

Overview of the aerosol composition

Average mass concentration of 10.7 µg m-3

13.9%

40.9%

25.2% 11.9%

7.2%

0.8%

30

25

20

15

10

5

0

ma

ss

co

nce

ntr

ati

on

(µg

m-3

)

01.0

6.20

12

01.0

7.20

12

01.0

8.20

12

01.0

9.20

12

01.1

0.20

12

01.1

1.20

12

01.1

2.20

12

01.0

1.20

13

01.0

2.20

13

01.0

3.20

13

01.0

4.20

13

01.0

5.20

13

01.0

6.20

13

01.0

7.20

13

01.0

8.20

13

01.0

9.20

13

01.1

0.20

13

01.1

1.20

13

01.1

2.20

13

01.0

1.20

14

01.0

2.20

14

01.0

3.20

14

01.0

4.20

14

01.0

5.20

14

01.0

6.20

14

time

1.0

0.8

0.6

0.4

0.2

0.0

ma

ss

fra

cti

on

0.80.40.0

Summer Autumn Winter Spring

organic nitratre sulfate ammonium chloride BC

30/17

Organic aerosol source apportionments

0.10

0.05

0.00

12010080604020

m/z

0.10

0.05

0.00

rela

tiv

e c

on

trib

uti

on

0.10

0.05

0.00

0.2

0.1

0.0

0.3

0.2

0.1

0.0

0.15

0.10

0.05

0.00

HOA

BBOA

Coal Combustion OA

LVOOA-1

LVOOA-2

SVOOA

6

4

2

0

21.1

2.20

1210

.01.

2013

30.0

1.20

1319

.02.

2013

11.0

3.20

1331

.03.

2013

time

6

4

2

0

rela

tiv

e c

on

trib

uti

on

4

2

010

86420

1086420

1086420

21.1

2.20

1310

.01.

2014

30.0

1.20

1419

.02.

2014

11.0

3.20

1431

.03.

2014

HOA

BBOA

Coal Combustion OA

LVOOA-1

LVOOA-2

SVOOA

31/17

Hydrocarbon-like OA (HOA)

- HOA has quite

similar time

variation as NOx

- Although HOA-MS

was constrained by

reference HOA

from fall 2008, the

resulting HOA-MS

agrees also well to

the other HOA-MS

from Melpitz

10

8

6

4

2

0

HO

A (

µg

m-3

)21

.12.

2012

04.0

1.20

13

18.0

1.20

13

01.0

2.20

13

15.0

2.20

1301

.03.

2013

15.0

3.20

13

29.0

3.20

13

time

20.1

2.20

1303

.01.

2014

17.0

1.20

14

31.0

1.20

14

14.0

2.20

1428

.02.

2014

14.0

3.20

14

28.0

3.20

14

120

100

80

60

40

20

0

NO

x (µ

g m

-3)

0.12

0.10

0.08

0.06

0.04

0.02

0.00

rela

tiv

e c

on

trib

uti

on

12010080604020

m/z

this study HOA May - June 2008 HOA Feb. - March 2009

32/17

Coal Combustion OA

0.10

0.08

0.06

0.04

0.02

0.00

arb

itra

ry v

alu

es

1201101009080706050403020

m/z

0.10

0.08

0.06

0.04

0.02

0.00

Coal Combustion OA reference Dall'Osto et al. (2013)

2.0

1.5

1.0

0.5

0.0

Co

al co

mb

usti

on

OA

(µg

m-3

)

01.0

1.20

13

01.0

2.20

13

01.0

3.20

13

01.0

4.20

13

time

30

25

20

15

10

5

0

SO

2 (µg

m-3)

01.0

1.20

14

01.0

2.20

14

01.0

3.20

14

01.0

4.20

14

- MS agrees well with previously reported Coal Combustion OA (Dall’Osto et al. 2013, Cork, Ireland)

- Good correlation with SO2

and Pb

m/z 115 m/z 77

m/z 91

m/z 105

2.0

1.5

1.0

0.5

0.0

Co

al O

A (

µg

m-3

)

2520151050

Pb (ng m-3

)

r² = 0.88

33/17

Summary primary OA (POA)

10

8

6

4

2

0

PO

A (

µg

m-3

)

1086420

BC (µg m-3

)

r² = 0.86

POA (sum HOA + BBOA + Coal Combustion OA) correlates well with BC POA contribution: BBOA > HOA > Coal Combustion OA

BBOA49.4%

HOA28.1%

Coal Combustion OA22.5%

8

6

4

2

0

HO

A (

µg

m-3

)

1086420

BC (µg m-3

)

r²=0.63

10

8

6

4

2

0

Co

al

Co

mb

us

tio

n O

A

(µg

m-3

)

1086420

BC (µg m-3

)

r²=0.78

10

8

6

4

2

0

BB

OA

(µ

g m

-3)

1086420

BC (µg m-3

)

r²=0.65

6

5

4

3

2

1

0PO

A (

HO

A,

BB

OA

, C

oa

l C

om

bu

sti

on

OA

)

(µg

m-3

)

01.0

2.20

13

01.0

3.20

13

01.0

4.20

13

time

01.0

1.20

14

01.0

2.20

14

01.0

3.20

14

01.0

4.20

14

8

6

4

2

0

BC

(µg

m-3)

34/17

Identification of the potential locations of POA sources

Potential Source Contribution Function (PSCF) Malm et al. (1985), Pekney et al. (2006)

=> estimate the most probable emission area of the pollutant Available in „openair“ R package (Carslaw and Ropkins (2012))

HYSPLIT: hourly backward trajectories (96h)

70

60

50

40

30

20

latitu

de

-40 -20 0 20 40

longitude

17.01.2014

19.01.2014

21.01.2014

23.01.2014

25.01.2014

27.01.2014

29.01.2014

31.01.2014

date

35/17

POA potential sources

- Coal Combustion OA sources mostly locate in Eastern European

countries

- In agreement with recent measurements made in 2 cities located at

both sites of the German-Czech Republic border where domestic

brown coal combustion can represent 30-40% of PM1 OC in winter

(Schladitz et al. Atm Env, 2015)

- BBOA covers a larger area

36/17

Summary of winter OA source apportionment

-2 years of measurements provides deep details on seasonal change of aerosol composition (especially Organics)

- in winter, 3 different POA were identified (HOA; BBOA, Coal Combustion OA)

- POA represents near 30% of total

- Potential Source Contribution Function (PSCF) was applied to provide a statistical location of POA sources

-Long term measurements are a unique opportunity to follow change in aerosol composition and impacts of mitigation strategies on anthropogenic emissions

Crippa et al. (2014)

HOA8.2%

BBOA14.4%

Coal Combustion OA6.5%

LVOOA-122.8%

LVOOA-235.0%

SVOOA13.1%

HOA 8%

BBOA 14%

LVOOA 43%

SVOOA 35%

Sept. - Nov. 2008

HOA 9%

BBOA 11%

LVOOA 52%

SVOOA 28%

Feb. - March 2009

Summary

Outline

Specific Summary LfULG Leipzig Aerosol

Summer Winter

0

1

2

3

Incre

men

tµ

gC

m3

Year

20002013/14/15

Traffic Increment PM3.5 EC• Air quality in Leipzig has improved

• EC traffic increment approx. 50 %

of year 2000

• Traffic at LMI 30 – 40 % of PM10 mass concentration

• Only 10 % in „Winter East“

• „Winter East“: 40 % of PM10 mass is trans-boundary combustion pollution

• Continued emission reductions in Eastern Europe necessary

van Pinxteren et al., Faraday Discussions, 2016

30%

2%

30%

10%

13%

15%

35%

5%40%

10%1%

9%

40%

2%6%30%

12%

10% 10%

30%

10%

40%

2%8%

Summer West Summer East

Winter West Winter East

Source categoryTrafficCoal.Comb.Biomass.Comb.SecondaryCrustSaltRest

LMI PM10 Composition

Specific Summary BB

Seiffen (near source)

Fractions of wood smoke to local PM10 loadings during the “wood

smoke” periods are estimated based on the available levoglucosan to PM

ratios.

– PM10: approx. 18%, max 27%

– PM1: approx. 28%, max 61%

These numbers are only for primary wood smoke contributions. Actual

domestic contributions may be higher due to the SOA formation from the

oxidation of biomass burning VOCs.

Melpitz (regional background)

POA represents near 30% of total OC, one third BBOA (PM 1)

40/19

Thank you for your kind attention

Surplus

Outline

Summary - Primary air pollutants

Soot is a problem in urban environments, especially when traffic-dominated

Soot and the associated individual organics have adverse health effects

The Diesel engine is thermodynamically great but causes Diesel soot. Its abatement causes a NO2 problem in cities

Wood burning is important in Germany in wintertime and is a desaster for air hygiene - it overcompensates PM reduction from the traffic sources

POA constitutes 1/3 of particle OC, half of this fm BB

Source: DIE NEBENWIRKUNGEN DER BEHAGLICKEIT: FEINSTAUB AUS KAMIN UND HOLZOFEN, Umweltbundesamt, 2006

● A constant increase in PM10 emissions from wood fuel combustion.

This trend is expected to continue.

● In contrast, the emissions from road traffic exhaust are expected to go

down dramatically (e.g. 25.4 kt/year in 2002 → 22.7 kt/year in 2003).

PM10 Emissions from Various Combustion Sources in Germany

44/17

Biomass Burning OA (BBOA)

- Pretty good correlation between BBOA and non-sea-salt chloride (ACSM) as well as levoglucosan (PM1.2 from Berner impactor)

- BBOA mass spectra agrees

also well with other BBOA-MS reported at Melpitz

6

5

4

3

2

1

0

BB

OA

(µ

g m

-3)

20.1

2.20

13

27.1

2.20

13

03.0

1.20

14

10.0

1.20

14

17.0

1.20

14

24.0

1.20

14

31.0

1.20

14

07.0

2.20

14

14.0

2.20

14

21.0

2.20

14

28.0

2.20

14

07.0

3.20

14

14.0

3.20

14

21.0

3.20

14

28.0

3.20

14

time

600

500

400

300

200

100

0

lev

og

luc

os

an

(ng

m-3)

3.0

2.5

2.0

1.5

1.0

0.5

0.0

BB

OA

(µ

g m

-3)

4003002001000

Levoglucosan (ng m-3

)

r² = 0.86

6

5

4

3

2

1

0

BB

OA

(µg

m-3

)01

.01.

2013

01.0

2.20

13

01.0

3.20

13

01.0

4.20

13

time01

.01.

2014

01.0

2.20

14

01.0

3.20

14

01.0

4.20

14

1.0

0.8

0.6

0.4

0.2

0.0

CH

L (µ

g m

-3)

0.12

0.10

0.08

0.06

0.04

0.02

0.00

rela

tive c

on

trib

uti

on

12010080604020

m/z

this study BBOA Feb. - March 2009