Santa Cruz de Tenerife: Development of methods for improving the air quality assessment in urban air

(1)

Casos prácticos:

•Calidad del Aire. Origen de PM₁₀ y PM_2.5 en Santa Cruz de Tenerife

•Calidad del Aire. Partículas ultrafinas y black carbon en ambientes urbanos

(2)

Development of methods for improving the air quality

assessment in urban air

Identification of the processes that influence on the long term

(multi-decadal) evolution of the aerosol properties that

influence on air quality and climate

Izaña (VAG o GAW):

(3)

Development of methods for improving the air quality

assessment in urban air

Identification of the processes that influence on the long term

(multi-decadal) evolution of the aerosol properties that

influence on air quality and climate

Izaña (VAG o GAW):

(4)

(5)

Origen de PM

₁₀

y PM

_2.5

en Santa Cruz de Tenerife

Estudio: caracterización química y contribución de fuentes PM₁₀ y PM_2.5 ¿ Origen PM₁₀ y PM_2.5 ?

Redes de calidad del aire: concentraciones PM₁₀ y PM_2.5

-toma de muestras en filtro -gravimetría

-análisis químico

(6)

AEMET, Agencia Estatal de Meteorología 6

PM_x (µg/m3₎₌_{Mayor elements}₊_{ions (SO}

4=, NO3-, NH4+, Na+, Cl-) + OC + EC + trace elements Major elements (Al, Si, Ca, K, Na, Mg)

Trace elements (P, Li , Be , Sc , Ti , V , Cr , Mn , Co , Ni , Cu , Zn , Ga , Ge , As , Se , Rb , Sr , Y , Zr , Nb , Mo, Cd , Sn , Sb, Cs , Ba , La , Ce , Pr , Nd , Sm , Eu , Gd , Tb , Dy , Ho , Er , Tm , Yb , Lu , Hf , Ta, W, Tl , Pb , Bi , Th , U )

Modelización en receptor: Análisis Factorial, Componentes Principales, Cluster, ...Positive Matrix Factorization (PMF)

1.Identifiaciñon de fuentes (perfil químico) 2.Regresión Lineal

PM₁₀ (µg/m3_{)= fuente-1 (µg/m}3_{) + fuente-2 (µg/m}3) + ………….. fuente_{-n (µg/m}3₎

(7)

2009 2008

2007 2006

2005

(8)

2009 2008

2007 2006

2005

(9)

2009 2008

2007 2006

2005

(10)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

gravimetria - suma de fuentes Marino Mineral Barcos Refineria Automóviles B) 0 20 40 60 80 100 120 140 160 0 10 20 30 40 50 60 70 80 90 100 _Percentil Barcos Refineria V, n g /m 3 C) 0 20 40 60 0 10 20 30 40 50 60 70 80 90 100 _Percentil Barcos Refineria N i, n g /m 3 D) PM₁₀ PM_2.5 0 5 10 15 20 25 30 35 40 45 50 100 96 92 88 84 79 75 71 67 63 59 55 51 47 42 38 34 30 26 22 18 14 10 5 1 P M 2 .5 , µ g /m 3 Percentil

gravimetria - suma de fuentes Marino Mineral Barcos Refineria Vehiculos B) 0 20 40 60 80 100 120 140 0 10 20 30 40 50 60 70 80 90 100 Percentil Barcos Refineria V, n g /m 3 C) 0 20 40 0 10 20 30 40 50 60 70 80 90 100 Percentil Barcos Refineria N i, n g /m 3 D)

Santa Cruz de Tenerife source apportionment study by receptor modeling Rodríguez et al., 2009

(11)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

gravimetria - suma de fuentes Marino Mineral Barcos Refineria Automóviles B) 0 20 40 60 80 100 120 140 160 0 10 20 30 40 50 60 70 80 90 100 _Percentil Barcos Refineria V, n g /m 3 C) 0 20 40 60 0 10 20 30 40 50 60 70 80 90 100 _Percentil Barcos Refineria N i, n g /m 3 D) PM₁₀ PM_2.5 0 5 10 15 20 25 30 35 40 45 50 100 96 92 88 84 79 75 71 67 63 59 55 51 47 42 38 34 30 26 22 18 14 10 5 1 P M 2 .5 , µ g /m 3 Percentil

(12)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

gravimetria - suma de fuentes Marino Mineral Barcos Refineria Automóviles B) 0 20 40 60 80 100 120 140 160 0 10 20 30 40 50 60 70 80 90 100 _Percentil Barcos Refineria V, n g /m 3 C) PM₁₀ PM_2.5 0 5 10 15 20 25 30 35 40 45 50 100 96 92 88 84 79 75 71 67 63 59 55 51 47 42 38 34 30 26 22 18 14 10 5 1 P M 2 .5 , µ g /m 3 Percentil

(13)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

(14)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

1. El polvo Sahara es la fuente q da lugar a los mayores episodios de PM

₁₀

y PM

_2.5

Deteriora la calidad del aire: impacto en la salud (?)

(15)

(16)

Ultrafine Partícles (PUFs)

We already measure PM

₁₀

and PM

_2.5

. Why PUFs?

16

PM_2.5 and cardiovascular deseases: involvement of PUF (ej. Araujo et al., 2009)

80% 40-60% N(number, cm-3₎ 40-60% PM₁₀(mass, µg·m-3₎ PM_2.5(mass, µg·m-3₎ 80-90% PUFs <0.1 µm <10 µm <2.5 µm

PUFs are not properly monitored in terms of PM₁₀ y PM_2.5

(17)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

gravimetria - suma de fuentes Marino Mineral Barcos Refineria Automóviles

B)

(18)

PUFs automóviles:

soot mode (50 - 100 nm): incomplete combustion

nucleation mode (< 30 nm): H₂SO₄ nucleation

PUFs: H₂SO₄– NH₃ - H₂O nucleación ternaria SO₂  H₂SO₄ PUFs: H2SO4 – H2O nucleación binaria

What happens in areas affected by industral SO₂ emisions?

How much does these emissions contribute to PUFs?

Decrease in PM mass  increase in PUFs

Casati et al., 2007

(19)

PUFs automóviles:

soot mode (50 - 100 nm): incomplete combustion

nucleation mode (< 30 nm): H₂SO₄ nucleation

PUFs: H₂SO₄– NH₃ - H₂O nucleación ternaria SO₂  H₂SO₄ PUFs: H2SO4 – H2O nucleación binaria

What happens in areas affected by industral SO₂ emisions?

How much does these emissions contribute to PUFs?

Decrease in PM mass  increase in PUFs

Casati et al., 2007

(20)

PUF and black carbon

measurements in

some air quality

monitoring

stations:

Proyecto EPAU: Evaluación integral del impacto de las emisiones de partículas de los automóviles en la calidad del aire urbano.

Ministerio de Medio Ambiente, B026/2007/3-10.1. 01/01/2007-30/06/2008

(21)

SO

₂

, NOx, CO and O

₃

PM

₁₀

y PM

_2.5

: levels and composition

Ultrafine Particles

(since 2008 - …)

proyecto EPAU

PM

₁₀

PM

_2.5

PUF

BC

SANTA CRUZ CRUZ

DE TENERIFE CITY

(22)

Santa Cruz

de

Tenerife

SCO

N 1km

TC

GL

SANTA CRUZ DE TENERIFE

REFINERY

0

NE

90

SE

180

SW

270

NW

₃₆₀

Refinery

Harbour

B)

SCO: Observatorio S/C

GL: Gladiolos

TC: Tomé Cano

2008- 2010

(23)

23

20

10

30

0 GL

SCO

0

15

30

45

• Santa Cruz

de Tenerife

SCO

N 1km

TC

GL

ships

TC

0

30

60

90 refinery

07 _-

09h

10 _-

17h

SO₂, µg·m-3

(24)

100·10

3

0

6

12

18

0

6

12

18

0

6

12

18

0 barcos

refinería

tráfico

50·10

3

cm

-3 time of day, GMT

vehicle exhaust

ships

refinery

PUF episodies in Santa Cruz:

(25)

100·10

3

0

6

12

18

0

6

12

18

0

6

12

18

0 barcos

refinería

tráfico

50·10

3

cm

-3 times of day

vehicle exhaust

ships

refinery

PUF episodies in Santa Cruz:

(26)

0.1 100 80 60 40 20 percentil 10- 17 UTC N1 vehículos N2 refinería N2 vehículos N2 barcos

N

, c

m

-3 0 100·103 50·103

Source apportionment of PUFs in Santa Cruz:

P90-P55 P100–P90 <P55 99.100-46.700 cm-3 PUFs: 46.700-19.500 19.500-2.300 59% 2% 39% 13% 59% 28% Refinery: Ships: Vehicles: 91% 8% 1% in press

(27)

0 10 20 30 40 50 60 70 80 90 100 100 96 92 88 86 82 78 74 68 64 61 57 53 49 45 41 37 33 29 25 22 18 13 9 5 3 P M 1 0 , µ g /m 3 Percentil

gravimetria - suma de fuentes Marino Mineral Barcos Refineria Automóviles

B)

(28)

Conclusión:

Las mediciones de black carbon en paralelo al PM

_2.5

y PM

₁₀

permiten evaluar

el impacto en la calidad del aire y la contribución a las concentraciones del

PM de las emisiones de los automóviles (combustión de biomasa….)

PM

₁₀

PM

_2.5

Black Carbon

Partículas ultrafinas

SO

₂

NO

_x

CO

O

₃

(29)

In Santa Cruz de Tenerife city:

Hospital Univeristario de Canarias

Exposure to outdoor PUF is

associated with an increase

risk to suffer Hearth Failure

Rev Esp Cardiol 2011: 64 (8): 661-666

Proyecto: Impacto de la contaminación atmosférica sobre la inflamación, estrés oxidativo y pronóstico a 1 año en pacientes ingresados por patología isquémica coronaria aguda.

Financiado por la SOCIEDAD ESPAÑOLA DE CARDIOLOGIA, convocatoria proyectos DAIICHI-SANKYO.

(30)

EPAU project,

Ministry of Environment of Spain B026/2007/3-10.1

PI: Sergio Rodriguez.

AEMET-CIAI, ES CSIC-IDÆA, ES Univ. Huelva, ES

Paul Scherrer Institut, CH EMPA, CH

Univ. of Birmingham, UK

National Physical Laboratory, UK

0 20 40 60 80 0 1000 2000 3000 0 10000 20000 30000 40000 C) B) A) N , c m -3 B C , ng· m -3 N /B C , 10 6/ngB C 4 8 12 16 20 0 times of day LUGANO, N7 HUELVA, N2.5 SANTA CRUZ, N2.5 BARCELONA, N5 LONDON, N7

Central – Northern EU:

vehicle exhaust

Southern EU:

(31)

Transfer of knowledge:

PUF sources

PUF health effects

air quality managers

Sevilla

PUF and black carbon

measurements in

some air quality

monitoring

(32)

(33)

Development of methods for improving the air quality

assessment in urban air

Identification of the processes that influence on the long term

(multi-decadal) evolution of the aerosol properties that

influence on air quality and climate

(34)

(35)

•3λscattering absorción Composición química •Distribución tamaño •APS+SMPS •TSP, PM10, PM2.5 •inlets

Mantenimiento programa VAG (calibraciones, cero, control calidad,…)

Ultrafine particles (CPC 3025A): 1997 – 2009

Chemical composition, TSP: 1987, PM_2.5: 2002, PM₁₀: 2005 ...

Size distribution of fine and ultrafine particles (SMPS): 2008 - ... Size distribution of coarse particles (APS): 2006 - ...

Scattering and backscattering (nephelometer): 2008 - ...

Absorption coefficient (7 l): 2012 - ...

In-situ aerosols GAW program:

(36)

GAW program:

Chemical composition (TSP, PM₁₀, PM_2.5): elemental (ICP-AES+ICP-MS) , ions (SO₄=_{, NO}

3-, NH4+), OC, EC 0 1 2 3 0 1 2 3 4

2003

2004

2002

2005

2006

2007

2008

2009

2010

µ g /m 3 µ g /m 3

PM

2 .5

NO

3 -

PM

2.5

SO

4 =

(37)

GAW program:

0 20 40 60 sc450 sc550 sc700

Optical properties: scattering and absorption

0.0 0.5 1.0 1.5 2.0 abs 637

M

m

-1

M

m

-1

2008

2011

2007

2009

2010

(38)

GAW program:

Size distribution: 10-500 nm (SMPS) + 0.5-20 µm (APS)

06 18 00 12 GMT 100 10 p art icle diame ter , n m

Example: new particle formation by nucleation

(39)

(40)

Long term 1987 - 2012 trends of sulfate, nitrate and

dust mixing in the Saharan Air Layer

40

S. Rodríguez

1

_{, J.M. Prospero}

3

_{, M.I. García}

1,4

_{, A. Alastuey}

5

_,

R.D. García

1,6

_{, J. López-Solano}

1

_{, E. Cuevas}

1

_{, X. Querol}

5

1_{Izaña Atmospheric Research Centre (IARC), Tenerife, Spain.}

3_{Rosenstiel School of Marine and Atmospheric Science, University of Miami, USA.} 4_{University of La Laguna, Tenerife, Spain}

5_{IDAEA, CSIC, Barcelona, Spain.} 6_{University of Valladolid, Spain}

(41)

Izaña, Tenerife Island

2400 m.a.s.l., free troposphere (night)

Teide peak, 3718 m.a.s.l.

Long term aerosols:

Total number concentration 2006

Size distribution 0.5 – 20 µm APS 2007 Scattering total and back 3 l TSI neph 2008 Absorption 1 l MAAP 2007 Aethalometer 7 l 2012

chemical composition 1987

(42)

aerosol chemical composition at Izaña (since 1987): dust (Al, Fe, ...), SO₄=, _NO

3-, NH4+, Na, and Cl-

sample collection on filter

1987-1999 30 m3/h

Dust: ash method (normalized Al/dust – 8%) SO₄=, _NO

3-, NH4+, Cl-: ion chromatography Al, Na, Fe: INAA

cellulose

University of Miami

PM_T: total particulate matter

2002- up to the date 30 m3/h

Dust: elemental composition IPC- AES, ICP-MS (normalized Al/dust – 8%) SO₄=, _NO

3-,,Cl-: : ion chromatography

NH₄+_{: capillary electrophoresis, specific electrode} OC, EC: TOR

Izaña + CSIC

quartz microfibber filter

PM

₁₀

: particulate matter diameter ≤ 10 µm

PM

_2.5

: particulate matter diameter ≤ 2.5 µm

PM

_T

: total particulate matter

1987-2014 27 years aerosol chemistry in the free troposphere

samples collected at night free troposphere

The two data sets were jointed for the firs time

(43)

PM

_T

0.9

elemental carbon 0.2%

none ammonium-sulfate

dust (Al, Fe, Ca, Ti..) Al = 8% dust 91% 2.2% 1.2% 0.4% 1.9% 3.8% ammonium-sulfate ammonium nitrate organic matter 47.3 µg/m3 42.6 1.0 0.5 0.07 0.2 1.8

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

(44)

PM

_T 0.9 0.2% 91% 2.2% 1.2% 0.4% 1.9% 3.8% 47.3 µg/m3 42.6 1.0 0.5 0.07 0.2 1.8

Satellite (Earth Probe, Nimbus 7, Aura):

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

Saharan Air Layer

MDFA: Major Dust Frequency Activity

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

MDFA =

Izaña

(45)

aerosol chemical composition (since 1987): dust (Al, Fe, ...), SO₄=, _NO

3-, NH4+, Na, and Cl-

part-1: long term evolution of dust

part-2: long term evolution of sulfate

(46)

part-1: long term evolution of dust

Sahara

Sahel summer

winter

Which are the large scale processes that influence on long term inter-annual variability in Saharan dust export in summer?

We have focused in summer

scientific question

Why?

•Is the season when maximum dust emissions occurs in North Africa due to the activation of subtropical Saharan sources

•Processes that modulated inter-annual variability in dust export are still unknown

•Winter: North Atlantic Oscillation (Ginoux et al., 2004)

(47)

Bamako – Mali

Morocco

North AFrican Dipole Intensity

d u stT , µ g/m 3 0 120 80 40 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

part-1: long term evolution of dust

summer dust

(48)

Bamako – Mali

Morocco

North AFrican Dipole Intensity

d u stT , µ g/m 3 0 120 80 40 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 -2 -1 +3 -3 +2 0 +1 N AFDI

700 hPa: relevant level for dust export

part-1: long term evolution of dust

Pearson correlation between NAFDI and the dust at Izaña = +0.75

(49)

0 120 80 40 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 -2 -1 +3 -3 +2 0 +1 N A F D I Correlation coefficient (1987-2012) between NAFDI and

precipitation rates zonal wind MDAF dust T , µg /m 3

part-1: long term evolution of dust

(50)

0 120 80 40 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 -2 -1 +3 -3 +2 0 +1 N A F D I Correlation coefficient between NAFDI and

part-1: long term evolution of dust

1987-2012 back trajectories 10 50 400 20 30 40 1987-2013 frequency

(51)

0 120 80 40 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 -2 -1 +3 -3 +2 0 +1 N A F D I Correlation coefficient between NAFDI and

part-1: long term evolution of dust

satellite product

-size distribution of exported dust

-long term evolution of NAFDI and connection to climate -spatial distribution of dust

(52)

Correlation coefficient between NAFDI and

precipitation rates zonal wind

MDAF

part-1: long term evolution of dust

-long term evolution of NAFDI and connection to climate

-spatial distribution of dust

-NAFDI está anti-correlacionado con el El Niño -ENSO -La variabilidad en el NAFDI y ENSO-El Niño

(53)

Correlation coefficient between NAFDI and

precipitation rates zonal wind

MDAF

part-1: long term evolution of dust

-NAFDI está anti-correlacionado con el El Niño -ENSO -La variabilidad en el NAFDI y ENSO-El Niño

(54)

part-1: long term evolution of dust

-influenced on long term Saharan dust export during 25 y

-Variability in the NAFDI:

-long term evolution of NAFDI and connection to climate -spatial distribution of dust

view from Izaña:

subsidence free-troposphere Saharan Air Layer

(55)

Santa Cruz de Tenerife: Development of methods for improving the air quality assessment in urban air

Development of methods for improving the air quality

assessment in urban air

Identification of the processes that influence on the long term

(multi-decadal) evolution of the aerosol properties that

influence on air quality and climate

Izaña (VAG o GAW):

Development of methods for improving the air quality

assessment in urban air

Identification of the processes that influence on the long term

(multi-decadal) evolution of the aerosol properties that

influence on air quality and climate

Izaña (VAG o GAW):

Origen de PM

y PM

en Santa Cruz de Tenerife

1. El polvo Sahara es la fuente q da lugar a los mayores episodios de PM

y PM

Deteriora la calidad del aire: impacto en la salud (?)

Ultrafine Partícles (PUFs)

We already measure PM

and PM

. Why PUFs?

B)

PUF and black carbon

measurements in

some air quality

monitoring

stations:

SO

, NOx, CO and O

PM

y PM

: levels and composition

Ultrafine Particles

proyecto EPAU

PM

PM

PUF

BC

SANTA CRUZ CRUZ

DE TENERIFE CITY

Santa Cruz

de

Tenerife

SCO

TC

GL

SANTA CRUZ DE TENERIFE

REFINERY

NE

SE

SW

NW

360

Refinery

Harbour

B)

SCO: Observatorio S/C

GL: Gladiolos

TC: Tomé Cano

20

10

30

0

GL

SCO

0

15

30

45

•

Santa Cruz

de Tenerife

SCO

TC

GL

ships

TC

0

₃₆₀

_-

_-