previsiones de transporte atmosfÉrico de polen: … 2018/46… · 14º congreso nacional del medio...

Congreso Nacional del Medio AmbienteMadrid del 26 al 29 de noviembre de 2018

PREVISIONES DE TRANSPORTE ATMOSFÉRICO DE POLEN: ESTUDIO SOBRE LAS ESPECIES DE PLATANUS Y PINUS EN BARCELONA

Michaël SicardBloque temático Calidad Ambiental y Salud#conama2018

0102030405

Motivation and objectives

Airborne pollen and instruments

The 27-31 March, 2015, pollination event

Platanus and Pinus transport simulation

Conclusions

Índice de contenidos

Congreso Nacional del Medio Ambiente. #Conama2018

MOTIVATION AND OBJECTIVES

01

14º Congreso Nacional del Medio Ambiente. #Conama2018



• Many people living in large cities suffer from allergies linked to the presence of airborne pollen

• Very little is known on pollen vertical distribution• Atmospheric pollen forecast are scarce or inexistent

Continuation of anongoing collaborationstarted in 2015

Rebeca Izquierdo, Marta Alarcón, Jordina Belmonte, Adolfo Comerón + Oriol Jorba



• Investigate with an air quality forecast system the factors responsible of:• Pollen release• Pollen dispersion/transport

• By evaluating the model performances against near-surface concentrationand backscatter coefficient profile measurements

AIRBORNE POLLEN AND INSTRUMENTS02




Pinus anemophily (=wind pollination) Platanus

Pollen source



Pollen grains

Pinus

From 60 to >100 μm

20 μm

Platanus



Vane Rain protection

Air entry hole

Suction Motor

Vacuum pump

Sealed removable head

Connection

mains

Airborne pollen concentration: HIRST collector



Airborne pollen concentration: methodology

1 week of data are collected

on 1 drum covered by a

Melinex film coated with a 2%

silicon solution as trapping

surface

The Melinex film is cut on 7 portions

each portion is 48 mm long and corresponds to a day (24h)

Pollen deposition on

drums by impact on

a suction system

Preparation of daily

and hourly samples

Observation with

optical microscope

Pollen identification

and counting

MA

N 6

/05 d

2M

AN

6/0

5 d

2

MA

N 6

/05 d

2M

AN

6/0

5 d

2

Hourly data

24 verticals lines every 2 mm

Daily data

4 horitzontal lines



Pollen columnar properties: MPL lidar

MPL (part of MPLNet)

Wavelengths (emission) 532 nm

Wavelengths (reception) 532 nm+ 532 nm cross pol.

PRF 2500 Hz

Typical temporal resolution

30 s

Vertical resolution 15, 30 or 75 m

Max. adquisition range > 20 km

Products β532, δp,532



Pollen columnar properties: methodology

Principle:

δp

z( ) 1

( )1 ( )

p

bck pol

pol p

pol bck

zCR z

z

( ) ( ) ( )p

pol polz CR z z

δp(z)

Pollen contribution ratio (Shimuzu et al., 2004)

If δp(z)=δpbck(z), CRpol(z)=0

If δp(z)=δppol(z), CRpol(z)=1

Pollen backscatter coefficient:

THE 27-31 MARCH, 2015, POLLINATION EVENT03




0

1000

2000

3000

4000

5000

6000

0 12 0 12 0 12 0 12 0 12

Po

llen

co

nce

ntr

atio

n (

m-3

h-1

)Platanus

Pinus

Total

27 28 29 30 31

Airborne pollenconcentration

Profiles of total volume

depolarizationratio



27 28 29 30 31Total

Total

r = 0,81 0,45 0,68 0,00 0,14

Difference between δV and δp r-values< 0,12



• Strong correlation of total pollen concentration with wind speed, temperature

• Anti-correlation with RH

r=-0.18

r=0.95

r=0.82

PLATANUS AND PINUSTRANSPORT SIMULATION04




NMMB/MONARCH: online air quality forecast system developed at BSC (Pérez et al., 2011; Jorba et al., 2012; Spada et al., 2013; Badia et al., 2017)

• Meteorology: NMMB (Janjic and Gall, 2012)• Aerosol scheme: bulk Pinus and Platanus aerosols• Transport: online scheme, same advection-diffusion as NMMB• Emission data: Pinus: Cartography of habitats of Catalonia (CREAF)

Platanus: Barcelona's City Hall Open Data Service

• Horizontal Resolution: 1 km x 1 km• Vertical Resolution: 48 hybrid sigma-pressure• Top Atmosphere: 50 hPa• Time step: 2 s• Meteorological initial conditions: NCEP Final Analysis



Pollen properties:

Pinus Platanus

Grain diameter (μm) 59 19Jackson & Lyford, 1999 Zhang et al., 2014

Density (kg/m3) 560 920 Jackson & Lyford, 1999 Jackson & Lyford, 1999

Zhang et al., 2014



V0 (first guess) V1 (low) V2 (high)

Emission factor(g/day/tree)

Model

Pinus

Platanus

Constant

81(Parker and Blush, 1996)

4,9(Brichi et al., 2000)

Wind effect scale factor

Zhang et al., 2014

81(Parker and Blush, 1996)4,9(Brichi et al., 2000)

Wind effect scale factor

Zhang et al., 2014

252,22(Tormo et al., 1996)

120(Tormo et al., 1996)

Sedimentationspeed(cm/s)

Ref.

PinusPlatanus

Pérez et al., 20112,170,62

Jackson & Lyford, 1999

Zhang et al., 20142,81

Jackson & Lyford, 1999

Zhang et al., 2014

2,81

Initialization Day0 Day-7 Day-7

σ*=0,6 m2/gLR=50 sr



-

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

27-3 28-3 29-3 30-3 31-3 1-4

Co

nce

ntr

atio

n (μ

g/m

3)

2015

Obs

V0

-

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

27-3 28-3 29-3 30-3 31-3 1-4

Co

nce

ntr

atio

n (μ

g/m

3)

2015

Obs

V0

V2



Platanus – 27MPinus – 27M

0 5 10 15 20 250

10

20

30

40

50

60

Time (UT)

Co

ncen

tra

tio

n (

g·m

-3)

Pinus

Obs

V0

V1

V2

10-1

100

101

102

10-1

100

101

102

Pinus

Observed Pinus conc. (g·m-3)

Mo

de

led

Pin

us c

onc.

(g

·m-3

)

V0

V1

V2

10-1

100

101

102

10-1

100

101

102

Pinus


Mo

de

led

Pin

us c

onc.

(g

·m-3

)

V0

V1

V2

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

Time (UT)

Co

ncen

tra

tio

n (

g·m

-3)

Platanus

Obs

V0

V1

V2



Platanus – 29MPinus – 29M

0 5 10 15 20 250

10

20

30

40

50

60

Time (UT)

Co

ncen

tra

tio

n (

g·m

-3)

Pinus

Obs

V0

V1

V2

10-1

100

101

102

10-1

100

101

102

Pinus


Mo

de

led

Pin

us c

onc.

(g

·m-3

)

V0

V1

V2

0 5 10 15 20 250

1

2

3

4

5

6

7

8

Time (UT)

Co

ncen

tra

tio

n (

g·m

-3)

Platanus

Obs

V0

V1

V2

10-4

10-3

10-2

10-1

100

101

102

10-4

10-3

10-2

10-1

100

101

102

Platanus

Observed Platanus conc. (g·m-3)

Mo

de

led

Pla

tan

us c

on

c. (g

·m-3

)

V0

V1

V2



Total– 27M

V0 V1 V2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Pollen back. coef. (Mm-1sr-1)

Altitu

de (

km

ag

l)

Obs

V0

V1

V2

Time (hour)

Altitu

de (

km

ag

l)

QL - Model-Obs. difference (Mm-1·sr-1)

0 5 10 15 20

0.5

1

1.5

2

-1

-0.5

0

0.5

1

Time (hour)

Altitu

de (

km

ag

l)


0 5 10 15 20

0.5

1

1.5

2

-1

-0.5

0

0.5

1

Time (hour)

Altitu

de (

km

ag

l)


0 5 10 15 20

0.5

1

1.5

2

-1

-0.5

0

0.5

1

Total– 29M

V0 V1 V20 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Pollen back. coef. (Mm-1sr-1)

Altitu

de (

km

ag

l)

Obs

V0

V1

V2

Time (hour)

Altitu

de (

km

ag

l)


0 5 10 15 20

0.5

1

1.5

2

-1

-0.5

0

0.5

1

Time (hour)

Altitu

de (

km

ag

l)


0 5 10 15 20

0.5

1

1.5

2

-1

-0.5

0

0.5

1

Time (hour)

Altitu

de (

km

ag

l)


0 5 10 15 20

0.5

1

1.5

2

-1

-0.5

0

0.5

1




27 March /27 March /27 March /27 March /28 March /28 March /28 March /28 March /29 March /29 March /29 March /29 March /30 March /30 March /30 March /30 March /31 March /31 March /31 March /31 March /

CONCLUSIONS

05



Conclusions

• Extremely strong pollination event (Platanus peak > 4700 m-3)• δV is a good proxy for conc., almost as good as δp → simple 1β+1δ

systems can do the job to track airborne pollen (not mixed)

• Key factors for pollen modelisation:• Emission maps (position and tree density)• Emission flux for Pinus and Platanus (high uncertainty)• Emission scheme: constant vs. wind driven, dependency of other

meteorological variables (T, RH)• Sedimentation speed• Size of the regional domain considered → recirculation, nesting

domains• Different pollen types respond differently to these factors

14º Congreso Nacional del Medio Ambiente

¡Gracias!#conama2018



• Investigate with an air quality forecast system the factors responsible of:• Pollen release• Pollen dispersion/transport

• By evaluating the model performances against near-surface concentrationand backscatter coefficient profile measurements

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