Quantifying the influence of local meteorology on air ...

Quantifying the influence of localmeteorology on air quality in Zagreb

using generalized additive models

Andreina Belusic 1, Ivana Herceg Bulic 1 and RachelLowe 2

1AMGI, Department of Geophysics, Faculty of Science, University of Zagreb, Zagreb,Croatia

2The Catalan Institute of Climate Sciences (IC3), Barcelona, Spain

November 3, 2014

A.Belusic (AMGI) November 3, 2014 1 / 27

1. Motivation and introduction

2. Measuring sites

3. Method

4. Results and discussions

5. Summary and conclusions


Motivation

• Elevated levels of concentration result of suddenemission and meteorological conditions

• A new approach for the urban area of Zagreb


Introduction

• GAMs well suited for quantifying and visualizing thepollutant- meteorological relationship

• The logarithm of hourly concentration modelled as asum of non-linear functions of meteorological andtime variables

• Partial effects


Figure 1 : Satellite view of the town Zagreb (source: Google Maps).Red bubbles indicate air pollution measuring sites and blue bubbleindicates meteorological parameters measuring site.


Dataset

• Pollutants: CO, SO2, NO2 and PM10

• Meteorological data: temperature, pressure, relativehumidity, precipitation last 4 h, precipitation lastweek, surface wind speed and direction

• Temporal variables: hour of day, day of week, daynumber

• Period: 1 Jan 2006 - 31 Dec 2012

• Sampling: Hourly


GAMs

• Regression models where smoothing splines are usedinstead of linear coefficients for covariates

• The logarithmic transformation

log(yi) = s0 + s(hd , k) + s(dw , k)

+ s(dn, k) + s(temp, k) + s(press, k)

+ s(rel .hum, k) + s(speed , k) + s(dir , k)

+ s(prec .4h, k) + s(prec .week , k) + εi

(1)


R2

r 2

CO (mg/m3) ZG3 0,60SO2 (µg/m3) ZG3 0,51NO2 (µg/m3) ZG3 0,43PM10 (µg/m3) ZG3 0,41

Table 1 : The explained variation, r 2, for each model on log scale atZagreb-3.


Comparison

0.0

0.5

1.0

1.5

Modelled and measured values

CO− comparison, Zagreb 3

2012

Conc. (m

g/m

^3)

Measured values

GAM

Figure 2 : The difference between the measured values ofconcentration (black) and modelled values (red) for CO at Zagreb-3,2012.


Temperature

−10 0 10 20 30 40

10

03

00

50

07

00

Temperature (°C)

Pa

rtia

l e

ffe

ct

(%)

−10 0 10 20 301

00

30

05

00

70

0

Temperature (°C)

Pa

rtia

l e

ffe

ct

(%)

Figure 3 : CO and PM10


Mean sea level pressure

980 1000 1020 1040

50

10

01

50

20

0

Press. (hPa)

Pa

rtia

l e

ffe

ct

(%)

980 1000 1020 10405

01

00

15

02

00

Press. (hPa)

Pa

rtia

l e

ffe

ct

(%)

Figure 4 : CO and SO2


Relative humidity

20 40 60 80 100

50

100

150

200

250

Rel. humidity (%)

Part

ial effect (%

)

20 40 60 80 100

50

100

150

200

250

Rel. humidity (%)

Part

ial effect (%

)

20 40 60 80 100

50

10

01

50

20

02

50

Rel. humidity (%)

Pa

rtia

l e

ffe

ct

(%)

Figure 5 : CO, SO2 and NO2


Wind direction

0 5 10 15 20 25 30

60

80

10

01

20

14

0

Wind direction (°)

Pa

rtia

l e

ffe

ct

(%)

0 5 10 15 20 25 306

08

01

00

12

01

40

Wind direction (°)

Pa

rtia

l e

ffe

ct

(%)



Wind speed

0 2 4 6 8

050

100

150

200

Wind speed (m/s)

Part

ial effect (%

)

a)

0 2 4 6 8

050

100

150

200

Wind speed (m/s)

Part

ial effect (%

)

b)

0 2 4 6 8

050

100

150

200

Wind speed (m/s)

Part

ial effect (%

)

c)

Figure 7 : CO, SO2 and NO2


Precipitation last 4 h

0 1 2 3 4 5

50

10

01

50

20

02

50

30

0

Precip. 4h (mm/h)

Pa

rtia

l e

ffe

ct

(%)

0 1 2 3 4 55

01

00

15

02

00

25

03

00

Precip. 4h (mm/h)

Pa

rtia

l e

ffe

ct

(%)



Hour of day

5 10 15 20

60

80

10

01

40

18

0

Hour of day

Pa

rtia

l e

ffe

ct

(%)

Figure 9 : CO


Day number

0 500 1000 1500 2000 2500

05

01

00

15

0

Day number

Pa

rtia

l e

ffe

ct

(%)

Figure 10 : CO


Day of week

1 2 3 4 5 6 7

80

85

90

95

10

01

10

Day of week

Pa

rtia

l e

fefc

t (%

)

Figure 11 : CO


Summary and conclusions

• Emissions have the largest and clear impact on airquality

• Stable atmospheric conditions increase theconcentration and have the negative impact onhuman health

• Changes in concentration quite well estimated


Improvements

• Traffic density data

• Vertical cross section

• Interactions


Thank You!

e-mail: [email protected]


Dataset

• Precipitation last 4 h:

1

10(4Pt + 3Pt−1 + 2Pt−2 + Pt−3) (2)

• Precipitation last week:

1168∑j=1

wj

168∑j=1

wjPt−3−j (3)


log(yi) = s0 + s(hd , k = 12) + s(dw , k = 7)

+ s(dn, k = 28) + s(temp, k = 9) + s(press, k = 9)

+ s(rel .hum, k = 9) + s(speed , k = 9) + s(dir , k = 9)

+ s(prec .4h, k = 5) + s(prec .week , k = 5) + εi(4)


Relative importance

• The proportion (in %) of the variation explained bythe j-th predictor variable in the model:

100σ−j

2 − σ2

p∑i=1

σ−j2 − pσ2

(5)


010

20

30

40

50

Relative importance, CO

Rel. im

port

ance (

%)

Tem

p.

Rel.

hum

.P

ress.

Dir.

Speed

HD

DN

Pre

c.

4h

Pre

c.

week.

DW

Zagreb 1

Zagreb 2

Zagreb 3

020

40

60

80

Relative importance, SO2

Rel. im

port

ance (

%)

Tem

p.

Rel.

hum

.P

ress.

Dir.

Speed

HD

DN

Pre

c.

4h

Pre

c.

week

DW

Zagreb 1

Zagreb 2

Zagreb 3

010

20

30

40

50

Relative importance, NO2

Rel. im

port

ance (

%)

Tem

p.

Rel.

hum

.P

ress.

Dir.

Speed

HD

DN

Pre

c.

4h

Pre

c.

week.

DW

Zagreb 1

Zagreb 2

Zagreb 3

010

20

30

40

50

Relative importance, PM10

Rel. im

port

ance (

%)

Tem

p.

Rel

hum

.P

ress.

Dir.

Speed

HD

DN

Pre

c.

4h

Pre

c.

week.

DW

Zagreb 1

Zagreb 2

Zagreb 3

Figure 12 : FIGURE 2A.Belusic (AMGI) November 3, 2014 25 / 27

Partial effects

• The actual estimated non-linear smooth curves

100Sj(xj)

Sj(xj ,ref )(6)


Model evaluation

i.e. CO:

• Measurement standard deviation: 0,30 mg/m3

• Model standard deviation: 0,23 mg/m3

• Measurement mean: 0,47 mg/m3

• Model mean: 0,47 mg/m3


Quantifying the influence of local meteorology on air ...

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