M. Schaap, A. Apituley, R. Koelemeijer, R. Timmermans, G. de Leeuw

Mapping the PM2.5 distribution in the Netherlands using MODIS AOD

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD2

Introduction

• Satellite derived AOD may be used to gain insight in the regional PM2.5 distribution

• Goal: To assess the relationship between AOD and PM2.5 in the Netherlands.

• Can we use AOD data at all?• If yes, what is the relation?• When does it apply?• Can we extrapolate the relation at Cabauw to the Netherlands

and estimate PM2.5 concentrations from satellite data?

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD3

Cabauw

The combination of the instrumentation at Cabauw provides an unique opportunity to study the AOD-PM2.5 relationship in the Netherlands.

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD4

Instruments used in this study

• AOD: Sun-photometer (CIMEL)AERONET Level 1.5

• PM2.5: TEOM-FDMS• Backscatter profile: aerosol LIDAR• Clouds: combination of Cabauw

instrumentation

Period: 1 August 2006 – 31 May 2007

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD5

AOD and PM2.5: Timeseries

0

20

40

60

80

100

120

140

160

0

0,5

1

1,5

2

1-Aug 11-Aug 21-Aug 31-Aug 10-Sep 20-Sep 1-Oct

PM2.5 AOD

PM

2.5 A

OD

Date

0

20

40

60

80

100

120

140

160

0

0,5

1

1,5

2

22-Mar 2-Apr 12-Apr 22-Apr 2-May 12-May

PM2.5 AOD

PM

2.5 A

OD

Date

2006

2007

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD6

Typical vertical profile in periods with good correlation

• Stable nice weather conditions typical for smog conditions• Continental airmasses• Cloud free• Well mixed boundary layer

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD7

Typical vertical profile in periods with bad correlation

A need for improved cloud detection!

AOD meas.

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD8

Cloud detection using the LIDAR and Angstrom coef.

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD9

Influence of cloud screening

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1 1.2

All data

LIDARy = 10.778 + 40.001x R2= 0.14572

y = 2.8901 + 97.826x R2= 0.40544

PM

2.5

(g

/m3 )

AOD

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD10

Time of day

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1

All11-15 Hr

PM

2.5

AOD

Time window PM2.5 = a * AOD + b R2 PM2.5 = a * AOD R2

a b a

0-24 97.5 2.93 0.40 107.3 0.40

9-17 111.7 0.55 0.50 113.0 0.50

10-16 106.8 0.96 0.47 110.2 0.47

11-15 124.5 - 0.34 0.57 123.3 0.57

12-14 156.1 - 6.92 0.72 127.8 0.71

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD11

Air mass origin

0

10

20

30

40

50

60

70

80

90

100

0 0.2 0.4 0.6 0.8 1AOD

PM

2.5

(

g/m

3 )

East

North

West

South

PM2.5 Average PM2.5 level associated with AOD

measurements

TEOM-FDMS MODIS AERONET LIDAR

Average 18.2 25.3 28.0

N 3946 35 226

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD12

Application to MODIS data…

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

y = -0.051669 + 0.93391x R2= 0.7951

y = -0.057545 + 0.98873x R2= 0.81955

MO

DIS

AO

D

AERONET AOD

0

20

40

60

80

100

0 0.1 0.2 0.3 0.4 0.5

y = 5.0926 + 120.01x R2= 0.51781

PM

2.5

(m

g/m

3 )

MODIS AOD

MODIS validation MODIS AOD-PM2.5

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD13

Estimated PM2.5 distribution over the Netherlands

There appear to be unrealisitc gradients in the AOD distribution within the Netherlands

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD14

Conclusions

• First inspection of the AERONET (L1.5) AOD and PM2.5 data yields a low correlation between the two properties

• AOD correlates well with PM2.5 under stable fair weather conditions with continental air masses

• AERONET L1.5 contains significant cloud contamination• Improved cloud detection using LIDAR eliminates many “outliers”• Comparison to L2.0 provides confidence in our cloud-screening

method and that of AERONET• Strength of correlation increases when focusing around noon.• Mapping of the regional PM2.5 distribution yields concentrations

that are about 45% higher than the long term average• The uncertainty associated with the AOD data may be higher or

of similar magnitude as the spatial variability within the country.• The good temporal correlation shows that AOD can be used for

monitoring PM2.5 changes in time

AT2, 01 October 2008, HelsinkiMapping the PM2.5 distribution in the Netherlands using MODIS AOD15

Comparison to L2.0 provides confidence in our cloud-screening method and that of AERONET

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1

L1.5 All data

L1.5 LIDARy = 13.152 + 35.611x R2= 0.13175

y = 6.5253 + 94.132x R2= 0.43922

PM

2.5

(g

/m3 )

AOD

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1

L2.0 All data

L2.0 LIDARy = 5.8154 + 75.881x R2= 0.38129

y = 6.6635 + 98.674x R2= 0.48358

PM

2.5

(g

/m3 )

AOD