Casos prácticos:
•Calidad del Aire. Origen de PM10 y PM2.5 en Santa Cruz de Tenerife
•Calidad del Aire. Partículas ultrafinas y black carbon en ambientes urbanos
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):
Casos prácticos:
•Calidad del Aire. Origen de PM10 y PM2.5 en Santa Cruz de Tenerife
•Calidad del Aire. Partículas ultrafinas y black carbon en ambientes urbanos
Origen de PM
10y PM
2.5en Santa Cruz de Tenerife
Estudio: caracterización química y contribución de fuentes PM10 y PM2.5 ¿ Origen PM10 y PM2.5 ?
Redes de calidad del aire: concentraciones PM10 y PM2.5
-toma de muestras en filtro -gravimetría
-análisis químico
AEMET, Agencia Estatal de Meteorología 6
PMx (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/m3) + fuente-2 (µg/m3) + ………….. fuente-n (µg/m3)
2009 2008
2007 2006
2005
2009 2008
2007 2006
2005
2009 2008
2007 2006
2005
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 PM2.5 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
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 PM2.5 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
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 PM2.5 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
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 PM2.5 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
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 PM2.5 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
1. El polvo Sahara es la fuente q da lugar a los mayores episodios de PM
10y PM
2.5Deteriora la calidad del aire: impacto en la salud (?)
Casos prácticos:
•Calidad del Aire. Origen de PM10 y PM2.5 en Santa Cruz de Tenerife
•Calidad del Aire. Partículas ultrafinas y black carbon en ambientes urbanos
Ultrafine Partícles (PUFs)
We already measure PM
10and PM
2.5. Why PUFs?
16
PM2.5 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) PM2.5 (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 PM2.5
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)
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
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
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
SO
2, NOx, CO and O
3PM
10y PM
2.5: levels and composition
Ultrafine Particles
(since 2008 - …)proyecto EPAU
PM
10PM
2.5PUF
BC
SANTA CRUZ CRUZ
DE TENERIFE CITY
Santa Cruz
de
Tenerife
SCO
N 1kmTC
GL
SANTA CRUZ DE TENERIFE
REFINERY
0NE
90SE
180SW
270NW
360
Refinery
Harbour
B)
SCO: Observatorio S/C
GL: Gladiolos
TC: Tomé Cano
2008- 201023
20
10
30
0
GL
SCO
0
15
30
45
•
Santa Cruz
de Tenerife
SCO
N 1kmTC
GL
ships
TC
0
30
60
90
refinery
07
-
09h
10
-
17h
SO2, µg·m-3100·10
30
6
12
18
0
6
12
18
0
6
12
18
0
barcos
refinería
tráfico
50·10
3cm
-3 time of day, GMTvehicle exhaust
ships
refinery
PUF episodies in Santa Cruz:
100·10
30
6
12
18
0
6
12
18
0
6
12
18
0
barcos
refinería
tráfico
50·10
3cm
-3 times of dayvehicle exhaust
ships
refinery
PUF episodies in Santa Cruz:
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 50·103Source 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
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)
Conclusión:
Las mediciones de black carbon en paralelo al PM
2.5y PM
10permiten 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
10PM
2.5Black Carbon
Partículas ultrafinas
SO
2NO
xCO
O
3In Santa Cruz de Tenerife city:
Hospital Univeristario de CanariasExposure to outdoor PUF is
associated with an increase
risk to suffer Hearth Failure
Rev Esp Cardiol 2011: 64 (8): 661-666Proyecto: 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.
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:
Transfer of knowledge:
PUF sources
PUF health effects
air quality managers
Sevilla
PUF and black carbon
measurements in
some air quality
monitoring
Casos prácticos:
•Calidad del Aire. Origen de PM10 y PM2.5 en Santa Cruz de Tenerife
•Calidad del Aire. Partículas ultrafinas y black carbon en ambientes urbanos
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
•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, PM2.5: 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:
•3λscattering absorción Composición química •Distribución tamaño •APS+SMPS •TSP, PM10, PM2.5 •inlets
GAW program:
Chemical composition (TSP, PM10, PM2.5): 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 3PM
2 .5NO
3 -PM
2.5SO
4 =•3λscattering absorción Composición química •Distribución tamaño •APS+SMPS •TSP, PM10, PM2.5 •inlets
GAW program:
0 20 40 60 sc450 sc550 sc700Optical properties: scattering and absorption
0.0 0.5 1.0 1.5 2.0 abs 637
M
m
-1M
m
-12008
2011
2007
2009
2010
•3λscattering absorción Composición química •Distribución tamaño •APS+SMPS •TSP, PM10, PM2.5 •inlets
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
Casos prácticos:
•Calidad del Aire. Origen de PM10 y PM2.5 en Santa Cruz de Tenerife
•Calidad del Aire. Partículas ultrafinas y black carbon en ambientes urbanos
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
51Izaña Atmospheric Research Centre (IARC), Tenerife, Spain.
3Rosenstiel School of Marine and Atmospheric Science, University of Miami, USA. 4University of La Laguna, Tenerife, Spain
5IDAEA, CSIC, Barcelona, Spain. 6University of Valladolid, Spain
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
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
PMT: 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
PM
T0.9
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 1.1PM
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 1.4PM
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 1.8Satellite (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
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
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)
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
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
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
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
precipitation rates zonal wind MDAF dust T , µg /m 3
part-1: long term evolution of dust
1987-2012 back trajectories 10 50 400 20 30 40 1987-2013 frequency
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
precipitation rates zonal wind MDAF dust T , µg /m 3
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
Correlation coefficient between NAFDI and
precipitation rates zonal wind
MDAF
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
-NAFDI está anti-correlacionado con el El Niño -ENSO -La variabilidad en el NAFDI y ENSO-El Niño
Correlation coefficient between NAFDI and
precipitation rates zonal wind
MDAF
part-1: long term evolution of dust
satellite product
-NAFDI está anti-correlacionado con el El Niño -ENSO -La variabilidad en el NAFDI y ENSO-El Niño
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
-size distribution of exported dust
view from Izaña:
subsidence free-troposphere Saharan Air Layer