Saharan Dust Impact on

PM2.5 in central Italy

S. Nava, G. Calzolai, M. Chiari, M. Giannoni, F. Lucarelli

Physics Dept. - University of Florence and INFN, Italy

S. Becagli, M. Marconi, R. Traversi, R. Udisti

Chemistry Dept., University of Florence, Italy

http://labec.fi.infn.it - nava@fi.infn.it

Introduction

Mineral dust is mainly present in the PM coarse mode;

however after long large transport an important fraction of the

distribution may affect also PM2.5

As the arrival of desert dust is characterised by an increase of

all soil related elements, field campaigns followed by

elemental analysis, and in particular by PIXE (Particle Induced

X-ray Emission) measurements, can be very useful to study

the desert dust contribution to PM at the ground level

This presentation will show results obtained by a review of

elemental data-sets determined by PIXE analysis of PM

samples collected in Tuscany (Italy) during extensive field

campaigns

Field campaigns

Samples collected on a daily basis (from midnight to midnight) by CEN-eq.

LV samples equipped with PM10 and PM2.5 inlets

Montelupo

Fiorentino

Montelupo Fiorentino

Sept. 02 – June 03

180 samples of PM10

1-20 Nov: PM10-PM2.5

Firenze

Prato Lucca

Livorno

Grosseto

Arezzo

PATOS 1

Sept. 05 – Sept. 06

1034 samples of PM10

Fl-Bassi Jun-Jul: PM10-PM2.5

PATOS 2

1 year (Mar.09 – Mar.10) every 2nd day

2 sampling sites in Florence and 1 in Livorno

500 samples of PM2.5 Firenze

Livorno

Ion Chromatography

inorganic ions

HR-ICPMS

soluble

component

of metals

PIXE

Elemental

concentrations

QUARTZ

FILTER

GC & GC-MS

PAHs and n-

alkanes

Thermo-optical

analysis

TC, EC, OC

TEFLON

FILTER

Analitical techniques

PIXE (Particle Induced X-ray Emission)

Simultaneous detection of all the elements with Z>10

(no pre-treatment and samples are not destroyed)

PIXE-PIGE set-up at LABEC

two X-ray detectors

automatic sample handling

by a multi-target holder

~ few minutes/sample

1 year of daily

samples

1 day of beam

time

High sensitivity for mineral dust

elements (Na, Mg, Al, Si, K, Ca, Ti,

Mn, Fe, Sr)

~ half minute/sample is sufficient

SDD detector

30 sec.

Data analysis

Backward trajectory calculations for all the sampling days (every 6

hours), at different arrival altitudes by HYSPLIT transport model by

NOAA Air Resource Laboratory

Analysis of elemental concentrations and elemental ratios of crustal

elements (in relationship with HYSPLIT data)

Soil dust component calculation:

African dust obtained by subtraction of an estimated background,

calculated as the soil dust monthly moving average (excluding

Saharan episodes)

Similar to EU guideline approach, but on DUST (not PM)

Positive Matrix Factorization (PMF) analysis (PATOS2)

1.35 Na + 1.66 Mg + 1.89 Al + 2.14 Si + 1.40 Ca + 1.43 Fe + 1.67 Ti + 1.21 K

- K, Ca and Fe corrected for anthropogenic contributions by EF calculations

- Na and Mg corrected for sea-salt contributions

1-year campaign (PATOS2, 2009-2010)

PM2.5

0

20

40

60

80

20-M

ar

19-A

pr

19-M

ay

18-J

un

18-J

ul

17-A

ug

16-S

ep

16-O

ct

15-N

ov

15-D

ec

14-J

an

13-F

eb

15-M

ar

g/m

3

GRAMSCI - U.T. BASSI - U.B. LIVORNO - R.B.T.S.

Mean PM2.5 (mg/m3)

FIRENZE-GRAMSCI - Traffic site 24

FIRENZE-BASSI - Urban background 16

LIVORNO - Rural background 10

Elemental time trends

Al

ng

/m3

ng

/m3

Si

Back-trajectories

from Africa

14 simultaneous Al-Si peaks in the 3 sampling sites

14 peaks (32 days out of 180 sampling days) due to desert dust

- T.S. - R.B. - U.B.

Elemental time trends n

g/m

3

Al

ng

/m3

ng

/m3

K

Fe

- T.S. - R.B. - U.B.

Elemental ratios

Si/Al Al/Ca Si/Ca Al/Fe Si/Fe

Livorno

rural

Sahara 2.5 1.05 2.6 1.37 3.4

Backgr. 3.7 0.55 1.9 0.70 2.4

Bassi

urban

Sahara 2.6 0.71 1.8 0.91 2.4

Backgr. 3.5 0.36 1.2 0.43 1.3

Gramsci

traffic

Sahara 2.7 0.60 1.5 0.42 1.1

Backgr. 3.5 0.27 0.9 0.16 0.5

Si/Al lower in desert dust (with very similar values in the 3

sampling sites)

Ratios to Ca and Fe higher in desert dust, due to Ca and Fe

enrichment in local soil dust (more site-dependent)

Similar behaviour than that observed for PM10 in Tuscany

[Nava et al., 2012] and in other Italian sites [Bonelli 96,

Marenco 06]

Sahara Backgr.

Soil dust concentration

Soil dust concentration during non-Saharan days is ~ 0.5 mg/m3 and,

except for few cases, it is always below 1 mg/m3

(av. Livorno: 0.35 mg/m3, Bassi: 0.45 mg/m3, Gramsci:0.61 mg/m3)

Soil dust concentration during Saharan episodes ranges from

~ 1 mg/m3 up to ~ 4 mg/m3 (during the most intense episode)

Soil dust

Contribution to PM2.5

Quite small soil dust contribution (~3% during non-Saharan days)

Up to ~20% of PM2.5 during the most intense Saharan episode

Desert dust

Bassi - UB

Desert dust contribution, detected in 32 days out of 180 sampling

days, shows an high variability:

it ranges from few tenths of ng/m3 up to 3-4 mg/m3

Desert dust contribution > 1 mg/m3 in 5 episodes

(corresponding to 8 days out of 180 sampling days)

Positive Matrix Factorization (PMF)

2 dust sources identified in Florence with similar profiles

so

urc

e p

rofi

le (

ng

/ng

) P

erc

en

tag

e o

f sp

ecie

s c

on

c. (%

)

DUST 2

DUST 1

Advanced receptor model based on a weighted

least square fit approach k

kjikij fgx

DUST 1

DUST 2

Upper crust profile (Mason)

Source time trends

0

2

4

6

8

Feb-09

Mar-09

Apr-09

May-09

Jun-09

Jul-09

Aug-09

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

UB TS

DUST 1

DUST 2

PATOS1: PM10-PM2.5

PM2.5/PM10

Saharan episode

(20-28 June)

0.59 (±0.09)

background days

(20-30 July)

0.74 (±0.05)

Soil dust

(mg/m3)

in PM2.5

Soil dust

(mg/m3)

in PM10

Saharan episode

(20-28 June)

7.7 (28%) 22 (46%)

background days

(20-30 July)

1.4 (6%) 6.1 (21%)

Sahara/backgr. 5.5 3.6

Sahara

PATOS1: elements

Sahara Sahara

PM2.5 Si/Al Al/Ca Al/Fe Si/Fe

Saharan days 2.6 0.46 1.0 2.6

Other days 3.0 0.35 0.5 1.7

PM10 Si/Al Al/Ca Al/Fe Si/Fe

Saharan days 2.6 0.33 0.92 2.4

Other days 3.0 0.28 0.45 1.3

Montelupo Fiorentino: PM10-PM2.5

0

20

40

60

80

01

/11

/02

03

/11

/02

05

/11

/02

07

/11

/02

09

/11

/02

11

/11

/02

13

/11

/02

15

/11

/02

17

/11

/02

19

/11

/02

21

/11

/02

PM10 PM2.5

PM2.5/PM10

Saharan episode (15-16 Nov) 0.34 (±0.08)

Background days (1-8 Nov) 0.63 (±0.08)

Sahara

rain

0

10

20

30

1/1

1/0

2

3/1

1/0

2

5/1

1/0

2

7/1

1/0

2

9/1

1/0

2

11

/11

/02

13

/11

/02

15

/11

/02

17

/11

/02

19

/11

/02

21

/11

/02

soil PM10 soil PM2.5 Soil dust

(mg/m3)

in PM2.5

Soil dust

(mg/m3)

in PM10

Saharan episode

(15-16 Nov)

5.5 (27%) 29 (38%)

Background days

(1-8 Nov)

0.8 (4%) 4.7 (14%)

Sahara/backgr. 6.9 6.2

Montelupo Fiorentino: elements

0

1000

2000

3000

4000

5000

6000

7000

1/1

1/0

2

3/1

1/0

2

5/1

1/0

2

7/1

1/0

2

9/1

1/0

2

11

/11

/02

13

/11

/02

15

/11

/02

17

/11

/02

19

/11

/02

21

/11

/02

Mg

Al

Si

K

Ca

Tix20

Fe

Srx70

PM10

0

200

400

600

800

1000

1200

1400

1600

1800

1/1

1/0

2

3/1

1/0

2

5/1

1/0

2

7/1

1/0

2

9/1

1/0

2

11

/11

/02

13

/11

/02

15

/11

/02

17

/11

/02

19

/11

/02

21

/11

/02

Mg

Al

Si

K

Ca

Tix20

Fe

PM2.5

0.0

0.1

0.2

Al Si Ca

PM2.5/PM10

sahara background PM2.5 Si/Al Al/Ca Al/Fe Si/Fe

Saharan days 2.2 0.81 1.6 3.5

Other days 3.7 0.40 0.3 1.2

PM10 Si/Al Al/Ca Al/Fe Si/Fe

Saharan days 2.3 0.53 1.3 3.1

Other days 3.2 0.28 0.6 1.9

Sahara Sahara

rain rain

Conclusions

The impact of several desert dust intrusions to PM2.5 at the

ground level has been detected in Tuscany, by mean of field

campaigns followed by elemental analysis

Their contributions show a very high variability: the estimated

desert dust net concentration ranges from few tenths up to 5-10

μg/m3 during the most intense events

The high sensitivity-rapidity of PIXE makes the use of this

technique a feasible approach to study the impact of desert dust

over representative long periods and for extended monitoring

networks

Together with atmospheric/meteorological data analysis, it may

provide an effective method to trace the contribution of desert

dust to PM quality standards, which may be used to validate

cheaper approaches that do not require speciation measurements

Elemental time trends n

g/m

3

Al

ng

/m3

ng

/m3

Mg

Na

- T.S. - R.B. - U.B.

Elemental time trends n

g/m

3

Al

ng

/m3

ng

/m3

Si

Ca