Ground observation of dust

(1)

1

Ground observation of dust

Sergio Rodríguez

[email protected]

Izaña Atmospheric Research Centre, Tenerife

(2)

• July

Biomass burning

(3)

Standarization of PM

₁₀

y PM

_2.5

in a regional network

(4)

•AEMET, Agencia Estatal de Meteorología •4

Viana et al., Atmospheric Environment , 2002

SeaWifs

TOMS

Feb 26, 2002

Air quality stations at Tenerife Island

In-situ dust characterization

The WHO

recommend PM

₁₀

(24-h) do not

exceed

(5)

Standarization of PM

₁₀

y PM

_2.5

in a regional network

EXAMPLE, Canary Islands

beta

teom

(6)

samplers of PM

₁₀

and PM

_2.5

room of conditioning and weighting filters

1 month in summer (30 days) sampling

(7)

(8)

ARAFO

GLADIOLOS

TOME CANO

MERCADO CENTRAL

TELDE

(9)

QUALITY CONTROL

SAMPLER

PM monitor

airflow accuracy

cleaning

(10)

PM10 y = 0.7981x R² = 0.9953

y = 0.8208x - 1.5301 R² = 0.9966

0 20 40 60 80 100 120

0 20 40 60 80 100 120 140

CA PT A D O R, ug /m 3

KIMOTO, ug/m3

PM2.5 y = 0.6499x

R² = 0.9554

y = 0.7033x - 1.4673 R² = 0.9637

0 10 20 30 40

0 10 20 30 40 50 60

KIMOTO, ug/m3

UNIDAD MOVIL

y = 0.678x R² = 0.8754

y = 0.5986x + 2.6596 R² = 0.8957

0 20 40 60 80 100 120

0 20 40 60 80

TEOM, ug/m3

(11)

TOME CANO

GLADIOLOS

A) PM10 y = 0.7111x R² = 0.9822

y = 0.76x - 1.4257 R² = 0.9874

0 10 20 30 40 50

0 10 20 30 40 50 60 70

TEOM, ug/m3

B) PM2.5 y = 0.6573x

R² = 0.8367

y = 0.9285x - 3.1282 R² = 0.9327

0 10 20 30

TEOM, ug/m3

y = 0.8962x + 0.9895 R² = 0.8782 A) PM10 y = 0.9523x

R² = 0.8502

0 10 20 30 40 50

C A P TA D O R , ug /m 3

(12)

(13)

Y=ax+b

(14)

(15)

Model validation with standardized

(16)

Model validation with standardized

(17)

Model validation with standardized

(18)

dust air quality

1. PM

₁₀

and PM

_2.5

levels

-method-01: reference – manual gravimetry

advantage: reference method

poor time resolution, 24-h average

manual work

takes 3 days to know PM

₁₀

disadvantage:

Manual gravimetry

high time resolution, 1h

Needs validation

automatic

-method-02: automatic

we recommend to use the two methods:

-automatic, continuously

(19)

(20)

• July

Biomass burning

(21)

21

Rodríguez et al., 2012

• aerosol dust- radiation interaction

• aerosol dust- cloud interaction negative forcing

negative forcing magnitude (?)

Radiative Forcing:

Dust deposition:

droplets and ice clouds formation clouds optical properties

light scattering and absorption

Dust and climate

-fertilization (P and Fe) of the ocean implications on CO₂ budget

Mineralogy Composition

Mixing with pollutants Size distribution

Key properties

in-situ techniques

(22)

dust belt

Air Quality

Europe & North America

Air Quality

‘cities in the dust belt’

(23)

23

Outline

-dust

-dust mixing with pollutants

-dust composition

long term variability in the Saharan Air Layer...

Mineralogy Composition

Mixing with pollutants Size distribution

Key properties

in-situ techniques

Ground based observation of:

(24)

24

-North Africa: 50-70% of global dust emissions

-dusty Saharan Air Layer

-summer:

-activation of subtropical Saharan sources

-max dust emissions

-SAL is exported 1- 5 km.a.s.l. to subtropic

A) summer

Aerosol Index

Izaña

dust

(25)

Izaña: 30 years aerosol observations

number concentration 2006

scattering total- and back- 3 l 2008

absorption 1 l 2007

aethalometer 7 l 2012

aerosol chemistry

1987

size distribution 10nm – 20 μm 2008

view from Izaña:

regular dust-free conditions

summer Saharan Air Layer

aerosol physic

1987 - 2016

(26)

Elements: Al, Fe, Ca, Mg, Na, Mn, Ti, ...



dust

Salts: SO

₄=,

_NO

3-,

NH

4+,

and Cl

-

OC, EC

sample collection on filter

PM

₁₀

and PM

_2.5

Quartz filters

Gravimetry, 20ºC 30-35% HR

Lab chemical analysis

IPC-AES, -MS

(27)

27 1987-2015

(28)

PM

_2.5 0.2 elemental carbon 0.4% none ammonium-sulfate dust 85% 3.0% 2.7% 1.0% 1.1% 5.8% ammonium-sulfate ammonium nitrate organic matter

18.5 µg/m3

15.8 0.6 0.5 0.07 0.2 1.1

PM

₁₀ 0.6 elemental carbon 0.2% none ammonium-sulfate dust 91% 2.2% 1.2% 0.4% 1.3% 3.4% ammonium-sulfate ammonium nitrate organic matter

42.0 µg/m3

38.3 0.9 0.5 0.07 0.2 1.4

PM

_x

composition in the Saharan Air Layer

(29)

29

Outline

-

dust

-dust mixing with pollutants

-dust composition

(30)

summer dust at Izaña: 1987 - 2015 0 120 80 40 dust T , µg /m 3

1988 1989 1990 1992

1987 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2015

Max: 133 μg/m32012

Min: 17 μg/m31997

Saharan Air Layer

Izaña

= fraction of summertime AI>1

Satellite (Earth Probe, Nimbus 7, Aura):

Total Ozone Monitor Spectrometer (1987-2001) Ozone Monitor Instrument (2005-2012)

MDFA: Major Dust Frequency Activity

number days UV Absorbing Aerosol Index > 1 total number of days in the month

MDFA =

UV Absorbing Aerosol Index = sensitive to iron oxides in dust

(31)

Summer North African meteorological scenario

-Subtropical high pressures, ≈35ºN

Complex puzzle from the tropic to the Mediterranean:

Inflow, 5-20ºN

-Harmattan (≈trade) winds 25-30 ºN -ITCZ, 20ºN

-Saharan heat low, 19ºN

-Tropical low monsoon, 7-12ºN

rain band, 5-12ºN

850hPa geop 1987-2014

H

L

(32)

Morocco

North AFrican Dipole Intensity

700 hPa: relevant level for dust export

large scale meteorology in North Africa Saharan dust export, connection to…

0 120

80

40

dust

T

,

µg

/m

3

1988 1989 1990 1992

(33)

Pearson correlation between NAFDI and the dust at Izaña = +0.71

Variability in dust export is associated with variability in NAFDI

North AFrican Dipole Intensity

700 hPa: relevant level for dust export

-2 +2 -3 -1 0 +3

+1 I N_AFD

Morocco Nigeria 0 120 80 40 dust T , µg /m 3

1988 1989 1990 1992

(34)

1987, 1996, 1997 and 2006

-2.79, -2.04, -3.19 and -1.54 1988, 2000, 2008 and 2012 _{+0.68, +0.83, +1.01 and +2.29}

(35)

0 120

80

40

-2 +2

-3 -1 0 +3

+1

SSS

correlation of NAFDI with… zonal wind

(36)

Correlation coefficient between NAFDI and

precipitation rates zonal wind

MDAF

1987-2014 back trajectories

10 50 400 20 30 40 1987-2014 frequency large scale meteorology in North Africa

Saharan dust export, connection to…

(37)

0 120

80

40

-2 +2

-3 -1 0 +3

+1

SSS

r=0.79

Variability in the summer dust export has been

mainly controlled by winds

r=0.71

(38)

1987, 1996, 1997 and 2006

-2.79, -2.04, -3.19 and -1.54 1988, 2000, 2008 and 2012 _{+0.68, +0.83, +1.01 and +2.29}

(39)

0 120

80

40

-2 +2

-3 -1 0 +3

+1

SSS

Wet Sahel Portion: portion of Sahel region (14-18°N to 17°W-22°E) that experienced a precipitation rate ≥ 3 mm/day

r=0.71

r=0.74

precipitation rates

correlation of NAFDI with…

(40)

0 120

80

40

-2 +2

-3 -1 0 +3

+1

SSS

correlation of NAFDI with…

Dust Activity zonal wind

Wet Sahel Portion: portion of Sahel region (14-18°N to 17°W-22°E) that experienced a precipitation rate ≥ 3 mm/day

precipitation rates

r=0.71

r=0.74

(41)

41

Outline

-dust

-dust mixing with pollutants

-dust composition

(42)

PM

_2.5 0.2 elemental carbon 0.4% none ammonium-sulfate dust 85% 3.0% 2.7% 1.0% 1.1% 5.8% ammonium-sulfate ammonium nitrate organic matter

18.5 µg/m3

15.8 0.6 0.5 0.07 0.2 1.1

PM

₁₀ 0.6 elemental carbon 0.2% none ammonium-sulfate dust 91% 2.2% 1.2% 0.4% 1.3% 3.4% ammonium-sulfate ammonium nitrate organic matter

42.0 µg/m3

38.3 0.9 0.5 0.07 0.2 1.4

(43)

0 1 2 3

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

ammonium-sulfate in the Saharan Air Layer

< 10 µm total

< 2.5 µm

a-SO4

=,

µg

/m

3 y = -0.0545x + 110.29

SO₄=_{as ammonium-sulfate}

August 1987-2012, dust days

T

gS

no significant change

55% decrease 55% decrease

(1) air laden in Saharan dust has previously passed over the Mediterranean and Europe

(2) North African emissions of SO₂ did not changed significantly during the 1990s

SO

2

emi

ss

ions

(44)

a-SO 4 =, µg /m 3 0 10 20 30 40 50 60 0.0 0.5 1.0 1.5

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Eu ro pe an Unio n emis sio ns , TgS 0.0 0.5 1.0 1.5

0 20 40 60

a-SO4 =, µg /m 3

European Union SO₂ emissions, TgS

SAL

decrease in a-sulfate in the Saharan Air Layer is correlated with the

decrease in European SO₂ emissions

August 1987-2012, dust days

air laden in Saharan dust has previously passed over the

Mediterranean and Europe

Saharan Air Layer, summer

(45)

0 5 10 15 20 25

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Eu ro pe an Unio n SO 2 emis sio ns , TgS a-SO 4 = (SAL) , µ g/ m 3 0 10 20 30 40 50 60 0.0 0.5 1.0 1.5

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Eu ro pe an Unio n emis sio ns , TgS a-SO 4 =, µg /m 3 SAL other sources contributing

industrial emissions North Africa

A) NO3-

(46)

0 5 10 15 20 25

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

a-SO 4 =, µg /m 3 SAL Eu ro pe an Unio n SO 2 emis sio ns , TgS a-SO 4 = (SAL) , µ g/ m 3 0 10 20 30 40 50 60 0.0 0.5 1.0 1.5

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Eu ro pe an Unio n emis sio ns , TgS a-SO 4 =, µg /m 3 SAL other sources contributing

ship emissions in the Mediterranean (?) north African emissions (?)

Cobett et al., 2007

(47)

47

dust and ice nucleation

CALIMA campaigns in August 2013 and 2014

dust mixing with pollutants

& implications

(48)

48

ice nuclei activated at -32ºC

dust

₁₀

dust and ice nucleation

dust mixing with pollutants

& implications

PINC measurements

(49)

49

dust and ice nucleation

dust mixing with pollutants

& implications

ice

nu

cleating

par

ticl

es

at

-32º

C

minimum number of ice crystals per by each μg of dust

maximum number of ice crystals per by each μg of dust

S

_min

mineralogy feldpars (?)

(50)

50

dust and ice nucleation

SAL 2014

SAL 2013

Atlantic airflows

the presence of ammonium – sulphate

mixed with dust

is favouring the formation of ice crystals in

the condensation freezing regime

condensation freezing

Regime ice formation

deposition

regime ice

formation

(51)

dust mixing with pollutants

& implications

dust, iron and ocean fertilization

(52)

52

Fr

actional

Fe

Solubilit

y

So

lubl

e

-F

e

to

tot

al F

e

ammonium-sulphate to

dust

dust mixing with pollutants

& implications

dust, iron and ocean fertilization

(53)

53

Outline

-dust

-dust mixing with pollutants

-

dust composition

(54)

54

Ca / Al

Mg / Al

Fe / Al

(55)

coarse total

fine

d

ust

,

μg

/m

3

0 100 200

A)

25 26 27 28 29 30 31 24

August

(56)

fine

d

ust

,

μg

/m

3

0 100 200

A)

25 26 27 28 29 30 31 24

August

(57)

fine

d

ust

,

μg

/m

3

0 100 200

A)

25 26 27 28 29 30 31 24

August

(58)

2.05

2.00 2.10

A) Si / Al B) Ca / Al

E) Mn / Al F) Sr / Al

(59)

2.05

2.00 2.10

G) Na / Al H) Mg / Al

K) K / Al L) Cu / Al

(60)

Sahara

Sahel

Sahara

Rio Niger

bajas tropográficas

Wakis: barrancos con inundaciones estacionales

chots, sabkas: lechos salados de lagos ecos

(61)

Sahara

Sahel

Sahara

Rio Niger

Ouargla basin

100 km

Chot El

Jerid

bajas tropográficas

Wakis: barrancos con inundaciones estacionales

chots

(62)

PSA1

PSA2

PSA3

PSA4

PSA5

B) A)

PSA1

PSA2

PSA3

PSA4

PSA5

watershed topography

PSA1 rich in Ca, S, Sr, K and Mg

PSA2 rich in Na and Cl

PSA3 rich in Si, Fe and Mn

Scheuvens et al. (2013)

(63)

PSA1 PSA2 PSA3 PSA4 PSA5 B) A) D) PSA1 PSA2 PSA3 PSA4 PSA5 C) 2 14 12 10 8 6 4 2 8 7 6 5 4 3

m/s m/s

watershed topography

surface winds _{700hPa winds}

PSA1 rich in Ca, S, Sr, K and Mg

PSA2 rich in Na and Cl

PSA3 rich in Si, Fe and Mn

(64)

long term in-situ aerosol dust programmes

Rodríguez et al., 2012. A review of methods for long term in situ characterization of aerosol dust. Aeolian Research, 6, 55–74

at least 4 years

at least 30 years

Highlights:

deficit of multidecadal in-situ observations

Mineralogy Composition

Mixing with pollutants Size distribution

Key properties