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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
A.Belusic (AMGI) November 3, 2014 2 / 27
Motivation
• Elevated levels of concentration result of suddenemission and meteorological conditions
• A new approach for the urban area of Zagreb
A.Belusic (AMGI) November 3, 2014 3 / 27
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
A.Belusic (AMGI) November 3, 2014 4 / 27
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.
A.Belusic (AMGI) November 3, 2014 5 / 27
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
A.Belusic (AMGI) November 3, 2014 6 / 27
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)
A.Belusic (AMGI) November 3, 2014 7 / 27
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.
A.Belusic (AMGI) November 3, 2014 8 / 27
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.
A.Belusic (AMGI) November 3, 2014 9 / 27
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
A.Belusic (AMGI) November 3, 2014 10 / 27
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
A.Belusic (AMGI) November 3, 2014 11 / 27
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
A.Belusic (AMGI) November 3, 2014 12 / 27
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
(%)
Figure 6 : CO and SO2
A.Belusic (AMGI) November 3, 2014 13 / 27
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
A.Belusic (AMGI) November 3, 2014 14 / 27
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
(%)
Figure 8 : CO and SO2
A.Belusic (AMGI) November 3, 2014 15 / 27
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
A.Belusic (AMGI) November 3, 2014 16 / 27
Day number
0 500 1000 1500 2000 2500
05
01
00
15
0
Day number
Pa
rtia
l e
ffe
ct
(%)
Figure 10 : CO
A.Belusic (AMGI) November 3, 2014 17 / 27
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
A.Belusic (AMGI) November 3, 2014 18 / 27
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
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Improvements
• Traffic density data
• Vertical cross section
• Interactions
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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)
A.Belusic (AMGI) November 3, 2014 22 / 27
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)
A.Belusic (AMGI) November 3, 2014 23 / 27
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)
A.Belusic (AMGI) November 3, 2014 24 / 27
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)
A.Belusic (AMGI) November 3, 2014 26 / 27
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
A.Belusic (AMGI) November 3, 2014 27 / 27