department of monitoring and model l ing air pollution, krakow, poland
DESCRIPTION
COST-733 WG 4 -Meeting. INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT. Department of Monitoring and Model l ing Air Pollution, Krakow, Poland. TITLE : Comparison of selected weather types classifications , for air pollution data from different areas. Jolanta Godłowska - PowerPoint PPT PresentationTRANSCRIPT
Department of Monitoring and Modelling Air Pollution, Krakow, Poland
INSTITUTE OF METEOROLOGYAND WATER MANAGEMENT
TITLE : Comparison of selected weather types classifications, for air pollution data from different areas
Jolanta GodłowskaAnna Monika Tomaszewska
Ioannina 9-10.05.2008
COST-733
WG4-Meeting
My questions are:
• Are there similar results comparing different classifications: by different methods (EV, WSD, WSD_U) for different air pollutants (PM10, CO, NO2, SO2, ozone) for different sites (Poland, Slovakia, Germany, Belgium)
• What is the nature of EV, WSD, WSD_U parameters ? modification of WSD_U and WSD
• How results depend on domain ? comparing results for 7, 8 and 5 domains
• What kind of classifications is the best for forecasting situations
with high concentrations ?
WSD_U - Ustrnul weighted standard deviation index WSD_U - Ustrnul weighted standard deviation index
Comparison of selected weather types classifications for forecasting the days with high air pollution
Data:
SO2 PM10 NO2 CO
NDJF
daily mean concentrations - SO2, PM10, NO2
maximal daily 8-hour concentrations – CO
from:
• Poland
− Cracow 1994 -1999
− Upper Silesia 1999-2002
• Belgium
− Uccle (1996-2002) – only PM10
NDFJNDFJ
Methods of classification evaluation Methods of classification evaluation :: the best:EV=1-(SSi/SSt) between 0 and 1 the highest k
WSD = (1/k)*∑ sdi depending on standard deviation the lowest k i=1
k
WSD_U = (∑ sdi*ni)/(∑ni) depending on standard deviation the lowest i=1 i=1
Relation between EV (left), WSD (center), WSD_U(right) and number of classes N for different air pollutants
Conclusions:Conclusions:
1.1. WSD and WSD and WSD_UWSD_U methods are methods are nnoot good for comparing results for different air pollutantst good for comparing results for different air pollutants
2. Normalisation of WSD and 2. Normalisation of WSD and WSD_UWSD_U parameters are necessary. parameters are necessary.
05
1015202530354045
0 20 40 60N
EV [%
]
SO2 PM10 NO2 CO
05
1015202530354045
0 20 40 60N
WSD
SO2 PM10 NO2 CO
0
5
10
15
2025
30
35
40
45
0 20 40 60N
WSD
_U
SO2 PM10 NO2 CO
New methods of classification evaluation after normalisation:New methods of classification evaluation after normalisation: WSD and WSD_U normalised: nWSD = WSD/sd
nWSD_U = WSD_U/sd
sd - total standard deviation
Relation between EV (left), nWSD (middle), nWSD_U (right) and number of classes N for different air pollutants (Upper Silesia)
Conclusion:Conclusion: • For all methods and species better quality is observed for classificationFor all methods and species better quality is observed for classificationss
with number of classeswith number of classes larger than 1larger than 155• Probably classifications with number of classes larger then 1Probably classifications with number of classes larger then 155
are better for air pollution forecastingare better for air pollution forecasting• For NO2For NO2 it it is observed the is observed the worstworst evaluation evaluation
0
20
40
60
80
100
0 10 20 30 40 50N
EV
[%]
SO2 PM10 NO2 CO
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40 50N
nWS
D
SO2 PM10 NO2 CO
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40 50N
nWS
D_U
SO2 PM10 NO2 CO
Comparison of different methods of classification evaluationComparison of different methods of classification evaluation
Conclusion:Conclusion: EV and nEV and nWSD_UWSD_U are correlated the most are correlated the most
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
nWSD
nWS
D_U
SO2 PM10 NO2 CO
0.0
0.2
0.4
0.6
0.8
1.0
0 0.2 0.4 0.6 0.8 1
EV
nWS
D
SO2 PM10 NO2 CO
0.0
0.2
0.4
0.6
0.8
1.0
0 0.2 0.4 0.6 0.8 1
EV
nWS
D_U
SO2 PM10 NO2 CO
CompariComparison ofson of different different methods of classification evaluationmethods of classification evaluation(EV, nWSD, nWSD_U) (EV, nWSD, nWSD_U) forfor SO2, PM10, NO2, CO SO2, PM10, NO2, CO
Upper SilesiaUpper Silesia
SO2
0%
20%
40%
60%
80%
100%
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
0%5%10%15%20%25%30%35%
nWSD nWSD_U EV PM10
0%
20%
40%
60%
80%
100%
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
0%5%10%15%20%25%30%35%
nWSD nWSD_U EV
NO2
0%
20%
40%
60%
80%
100%
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
0%5%10%15%20%25%30%35%
nWSD nWSD_U EV CO
0%
20%
40%
60%
80%
100%
CE
CE
SL
PC
10
ES
LP
C3
0E
Z5
00
C1
0E
Z5
00
C3
0G
WT
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
WP
27
PC
AC
AP
CA
XT
RK
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
ST
PC
AV
TP
CA
07
WL
KC
73
3H
BG
WL
OG
WL
PE
CZ
EL
YP
ER
RE
TS
CH
UE
EP
ZA
MG
US
TR
TC
21
TC
11
0%5%10%15%20%25%30%35%
nWSD nWSD_U EV
Comparison of classification Comparison of classification ESLPC30 withESLPC30 with LWT2 LWT2 forfor SO2 SO2
Upper SilesiaUpper Silesia
"E S LP C30"; Oc ze kiwan e ś redn ie b rze gowe
B ie żąc y e fekt: F(2 4 , 391 )=3 .18 94 , p=.00000
Dekom pozyc ja e fektywn yc h h ip o tez
P io nowe s łupki o znac za ją 0 .9 5 p rzed zia ły u fnoś c i
1 3 5 7 9 12 14 16 19 21 25 27 30
E S L P C30
0
50
100
150
200
sZ
AB
RZ
E1
"LW T 2"; Oc zekiw ane ś red n ie b rzegow e
B ieżąc y e fekt: F(25 , 390 )=5 .1469 , p=.0000 0
Dekom po zyc ja e fektywnyc h h ipo tez
P ionow e s łupki oznac za ją 0 .95 p rzedzia ły u fnoś c i
1 3 5 7 9 11 13 15 17 19 21 23 25
LW T 2
0
50
100
150
200
sZ
AB
RZ
E1
CompariComparison ofson of different different methods of classification evaluationmethods of classification evaluation(EV, nWSD, nWSD_U) (EV, nWSD, nWSD_U) forfor SO2, PM10, NO2, CO SO2, PM10, NO2, CO
Upper SilesiaUpper Silesia
EV
0
10
20
30
40
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
SO2 Zabrze PM10 Zabrze NO2 Zabrze
CO KatowiceR NO2 KatowiceR
nWSD
0.4
0.6
0.8
1.0
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
SO2 Zabrze PM10 Zabrze NO2 Zabrze
CO KatowiceR NO2 KatowiceR
nWSD_U
0.7
0.8
0.9
1.0
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
SO2 Zabrze PM10 Zabrze NO2 Zabrze
CO KatowiceR NO2 KatowiceR
Comparison of EV evaluationComparison of EV evaluationfor different pollutants at different placesfor different pollutants at different places
SO2, PM10, NO2, COSO2, PM10, NO2, CO Upper Silesia, Cracow, BrusselsUpper Silesia, Cracow, Brussels
SO2
0
10
20
30
40
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
Zabrze Kraków 2 Kraków 5
Kraków 6 Kraków 4
PM10
0
10
20
30
40
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
Zabrze Kraków 2 Kraków 5
Kraków 6 Uccle Kraków 4
NO2
0
10
20
30
40
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
Zabrze Kraków 2 Kraków 5
Kraków 6 KatowiceR Kraków 4
CO
0
10
20
30
40
CE
CE
SLP
C10
ES
LPC
30E
Z50
0C10
EZ
500C
30G
WT
LIT
AD
VE
LIT
TC
LUN
DLW
T2
NN
WP
27P
CA
CA
PC
AX
TR
KP
CA
XT
RP
ET
ISC
OS
AN
DR
AS
AN
DR
AT
PC
AV
TP
CA
07W
LKC
733
HB
GW
LO
GW
LP
EC
ZE
LYP
ER
RE
TS
CH
UE
EZ
AM
GU
ST
RT
C21
TC
11
Katowice R Kraków 5 Kraków 6
Comparison of different methods of classification evaluationComparison of different methods of classification evaluationEV vs Index of Performance R2EV vs Index of Performance R2
Poland Cracow domena 7 Belgium dom 4
Prokocim Aleje UccleEV R2 EV R2 EV R2
CEC 0.11 0.37 0.13 0.38 0.26 0.23ESLPC10 0.10 0.34 0.09 0.29 0.17ESLPC30 0.15 0.36 0.14 0.34EZ500C10 0.03 0.13 0.03 0.17 0.13 0.09EZ500C30 0.25 0.26 0.18GWT 0.17 0.40 0.21 0.45 0.26 0.18LITADVE 0.13 0.35 0.18 0.43 0.08LITTC 0.49 0.24 0.51 0.28 0.15LUND 0.14 0.33 0.19 0.41 0.11 0.15LWT2 0.19 0.45 0.24 0.50 0.31 0.11NNW 0.07 0.29 0.11 0.37 0.17 0.21P27 0.14 0.36 0.15 0.40 0.28 0.23PCACA 0.05 0.21 0.08 0.28 0.25 0.27PCAXTRKM 0.16 0.37 0.16 0.40 0.15 0.12PCAXTR 0.11 0.34 0.13 0.37 0.14 0.17PETISCO 0.13 0.35 0.16 0.41 0.26 0.11SANDRA 0.19 0.48 0.21 0.47 0.33 0.19SANDRAS 0.21 0.47 0.19 0.43 0.38 0.29TPCAV 0.11 0.33 0.12 0.36 0.10 0.15TPCA07 0.06 0.24 0.07 0.29 0.08 0.14WLKC733 0.36 0.19 0.42HBGWL 0.21 0.45 0.21 0.45 0.41OGWL 0.17 0.45 0.19 0.45 0.43 0.21PECZELY 0.18 0.44 0.19 0.45 0.11 0.20PERRET 0.12 0.36 0.15 0.42 0.37 0.11SCHUEEPP 0.19 0.45 0.22 0.44 0.12ZAMG 0.22 0.44 0.24 0.48 0.33
Cracow Prokocim
0.0
0.1
0.2
0.3
0.4
0.5
0.6
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
HB
GW
L
OG
WL
PE
CZ
EL
Y
PE
RR
ET
SC
HU
EE
PP
ZA
MG
EV R2
Cracow Aleje
0.0
0.1
0.2
0.3
0.4
0.5
0.6
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
HB
GW
L
OG
WL
PE
CZ
EL
Y
PE
RR
ET
SC
HU
EE
PP
ZA
MG
EV R2
Uccle
0.0
0.1
0.2
0.3
0.4
0.5
0.6
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
HB
GW
L
OG
WL
PE
CZ
EL
Y
PE
RR
ET
SC
HU
EE
PP
ZA
MG
EV R2
Comparison of different methods Comparison of different methods of classification evaluationof classification evaluation
EV vs Index of Performance R2EV vs Index of Performance R2
O G W L; P refered by E V
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
O G W L
0
20
40
60
80
100
120
140
160
Ucc
le
P E CZE LY ; P refered by R2
1 2 3 4 5 6 7 8 9 10 11 12 13
P E CZE LY
0
20
40
60
80
100
120
140
160
Ucc
le
ZA M G ; P refered by E V
1 4 7 10 13 16 20 25 30 33 36 40
ZA M G
0
20
40
60
80
100
120
140
160
Ucc
le
Comparison of different methods of classification evaluationComparison of different methods of classification evaluationEV vs Index of Performance R2EV vs Index of Performance R2
Romania domain 10 TSP
Baia Mare Ploiesti Rescita EV R2 EV R2 EV R2
CEC 0.31 0.14 0.34 0.17 0.35ESLPC10 0.19 0.24 0.21ESLPC30 0.20 0.29 0.20EZ500C10 0.18 0.15EZ500C30 0.20 0.24 0.26 0.30GWT 0.26 0.18 0.42 0.36LITADVE 0.15 0.13 0.35 0.12 0.33LITTC 0.41 0.45 0.45LUND 0.20 0.31 0.25LWT2 0.38 0.41 0.38NNW 0.21 0.19 0.34P27 0.41 0.38 0.43PCACA 0.13 0.31 0.15 0.16PCAXTRKM 0.19 0.29 0.17PCAXTR 0.18 0.16 0.29 0.18PETISCO 0.41 0.46 0.44SANDRA 0.31 0.20 0.07SANDRAS 0.21 0.44 0.21 0.48 0.23 0.46TPCAV 0.12 0.32 0.21 0.44 0.11 0.34TPCA07 0.11 0.31 0.12 0.34 0.26WLKC733 0.35 0.50 0.46 0.30 0.43HBGWL 0.25 0.52 0.35 0.60 0.34 0.55OGWL 0.25 0.48 0.38 0.57 0.26 0.48PECZELY 0.33 0.22 0.25PERRET 0.47 0.37 0.56 0.45SCHUEEPP 0.50 0.49 0.47ZAMG 0.38 0.28 0.47 0.40
Baia Mare
0.0
0.1
0.2
0.3
0.4
0.5
0.6
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
HB
GW
L
OG
WL
PE
CZ
EL
Y
PE
RR
ET
SC
HU
EE
PP
ZA
MG
EV R2
Ploiesti
0.0
0.1
0.2
0.3
0.4
0.5
0.6
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
HB
GW
L
OG
WL
PE
CZ
EL
Y
PE
RR
ET
SC
HU
EE
PP
ZA
MG
EV R2
Resita
0.0
0.1
0.2
0.3
0.4
0.5
0.6
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
HB
GW
L
OG
WL
PE
CZ
EL
Y
PE
RR
ET
SC
HU
EE
PP
ZA
MG
EV R2
Comparison of different methods of classification evaluationComparison of different methods of classification evaluationEV vs Index of Performance R2EV vs Index of Performance R2
S CHUE E P P ; P refered by R2
1 4 7 10 16 19 22 25 29 33 37
S CHUE E P P
0
20
40
60
80
100
120
140
160
180
200
Bai
aMar
e
"W LK C733"; P refered by E V
2 4 6 10 14 19 22 24 28 30 32 34 36 38 40
W LK C733
0
20
40
60
80
100
120
140
160
180
200
Bai
aMar
e
Data:
OZONE
AMJJA
8-hour concentration of ozone (for 17 UTC) from:
•Poland 1997-2002
− central and east monitoring stations:
Warszawa IOŚ (urban) and Diabla Góra, Jarczew, Belsk, Zbereże (rural)
− south monitoring stations:
Zabrze and Katowice (urban), Kuźnia Nieborowska (rural),
Kędzierzyn (suburban, industrial),
•German 1997-2002
− central and east monitoring stations:
Hoyeswerda (urban), Goerlitz (urban, traffic), Mittelndorf (rural)
•Slovakia 1997-1998, 2000
− east monitoring station:
Humenne (urban)
•Belgium 1990-2002
− monitoring stations:
Moerkerke and Vezin (rural)
Comparison of selected weather types classifications for forecasting the days with high air pollution
Ozone AMJJAOzone AMJJA
Comparison (EV) of different classifications for ozone Comparison (EV) of different classifications for ozone domain 7domain 7
Upper Silesia, Poland
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ
500C
10
EZ
500C
30
GW
T
LIT
AD
VE
LIT
TC
LUN
D
LWT
2
NN
W
P27
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZ
ELY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC
21
TC
11
ZA
MG
Kędzierzyn
Kuźnia
Zabrze
Katowice Zał
Katowice R
Germany
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ5
00C
10
EZ5
00C
30
GW
T
LITA
DV
E
LITT
C
LUN
D
LWT2
NN
W
P27
PC
AC
A
PC
AX
TRK
M
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TPC
AV
TPC
A07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZE
LY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC21
TC11
ZAM
G
Hoyeswerda
Mittelndorf
Goerlitz
Belgie and Slovakia
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ5
00C
10
EZ5
00C
30
GW
T
LITA
DV
E
LITT
C
LUN
D
LWT2
NN
W
P27
PC
AC
A
PC
AX
TRK
M
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TPC
AV
TPC
A07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZE
LY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC21
TC11
ZAM
G
Moerkerke
Vezin
Humenne
Central and East Poland
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ5
00C
10
EZ5
00C
30
GW
T
LITA
DV
E
LITT
C
LUN
D
LWT2
NN
W
P27
PC
AC
A
PC
AX
TRK
M
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TPC
AV
TPC
A07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZE
LY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC21
TC11
ZAM
G
DG
Jarczew
Warszawa
Belsk
LWT2LWT2 LWT2LWT2
Mean ozonMean ozonee concentrations for concentrations for different types of LWT2different types of LWT2
domain 7domain 7 GermanyGermany
PolandPoland
BelgiumBelgium
1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5
LW T2
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
1 2 0
1 3 0
1 4 0
35r Belgium
1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5
LW T2
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
1 2 0
1 3 0
1 4 0
Gorlitz
1 3 5 7 9 11 13 15 17 19 21 23 25
LW T2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
Kat
owic
e Z
LWT2 ERA40 CompositesLWT2 ERA40 CompositesType 4Type 4High ozone High ozone concentrationsconcentrationsin Germanyin Germany
Type 5Type 5
High ozone High ozone concentrationsconcentrationsin Polandin Poland
Type 3Type 3
The highest ozone The highest ozone concentrationsconcentrationsin Belgiumin Belgium
Mean ozonMean ozonee concentrations concentrationsfor different types of LWT2for different types of LWT2
domain 7domain 7 GermanyGermany
1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5
LW T2
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
1 2 0
1 3 0
1 4 0
35r Belgium
BelgiumBelgium
PolandPoland
1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5
LW T2
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
1 2 0
1 3 0
1 4 0
Gorlitz
1 3 5 7 9 11 13 15 17 19 21 23 25
LW T2
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
Kat
owic
e Z
Type 22Type 22
High ozone High ozone concentrationsconcentrationsin Poland and Germany, in Poland and Germany, Low ozone Low ozone concentrationsconcentrations in Belgium in Belgium
Type 13Type 13
The highest ozone The highest ozone concentrationsconcentrations in in Germany Germany High ozone High ozone concentrationsconcentrations in Poland in Poland Mean ozon Mean ozon concentrationsconcentrations in Belgium in Belgium
LWT2 ERA40 CompositesLWT2 ERA40 Composites
Comparison (EV)of different classifications for ozone Comparison (EV)of different classifications for ozone domain 7domain 7
Upper Silesia, Poland
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ
500C
10
EZ
500C
30
GW
T
LIT
AD
VE
LIT
TC
LUN
D
LWT
2
NN
W
P27
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZ
ELY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC
21
TC
11
ZA
MG
Kędzierzyn
Kuźnia
Zabrze
Katowice Zał
Katowice R
Germany
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ5
00C
10
EZ5
00C
30
GW
T
LITA
DV
E
LITT
C
LUN
D
LWT2
NN
W
P27
PC
AC
A
PC
AX
TRK
M
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TPC
AV
TPC
A07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZE
LY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC21
TC11
ZAM
G
Hoyeswerda
Mittelndorf
Goerlitz
Belgie and Slovakia
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ5
00C
10
EZ5
00C
30
GW
T
LITA
DV
E
LITT
C
LUN
D
LWT2
NN
W
P27
PC
AC
A
PC
AX
TRK
M
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TPC
AV
TPC
A07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZE
LY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC21
TC11
ZAM
G
Moerkerke
Vezin
Humenne
Central and East Poland
0.0
0.1
0.2
0.3
0.4
0.5
CE
C
ES
LPC
10
ES
LPC
30
EZ5
00C
10
EZ5
00C
30
GW
T
LITA
DV
E
LITT
C
LUN
D
LWT2
NN
W
P27
PC
AC
A
PC
AX
TRK
M
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TPC
AV
TPC
A07
WLK
C73
3
HB
GW
L
OG
WL
PE
CZE
LY
PE
RR
ET
SC
HU
EE
PP
US
TR
TC21
TC11
ZAM
G
DG
Jarczew
Warszawa
Belsk
LITtcLITtcLITtcLITtc
1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5 2 7
LITTC
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
1 2 0
1 3 0
1 4 0
KatZ
Ał
1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5 2 7
LITTC
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 0 0
1 1 0
1 2 0
1 3 0
1 4 0
35r Belgium
Mean ozonMean ozonee concentrations concentrations for different types of for different types of LITtcLITtc
domain 7domain 7
1 3 5 7 9 11 13 15 17 19 21 23 25 27
LITTC
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140G
orlit
zGermanyGermany
BelgiumBelgium
PolandPoland
type 12type 12
The highest values of ozone in The highest values of ozone in Germany, Poland and BelgiumGermany, Poland and Belgium
The high values of ozone in Poland, middle in The high values of ozone in Poland, middle in Germany, and low in BelgiumGermany, and low in Belgium
type 13type 13
Comparison of (EV) different classifications for ozone Comparison of (EV) different classifications for ozone domain 5,7,8domain 5,7,8
Diabla Góra, PolandDiabla Góra, Poland
AMJJA
0
0.1
0.2
0.3
0.4
CE
C
ES
LPC
10
ES
LPC
30
EZ
500C
10
EZ
500C
30
GW
T
LIT
AD
VE
LIT
TC
LUN
D
LWT
2
NN
W
P27
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WLK
C73
3
domain 5
domain 8
domain 7
JJA
0
0.1
0.2
0.3
0.4
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
domain 5
domain 8
domain 7
Comparison (EV) of Comparison (EV) of different classifications for ozone different classifications for ozone
domain 5,7,8domain 5,7,8Diabla Góra, PolandDiabla Góra, Poland
EV
0
0.1
0.2
0.3
0 0.1 0.2 0.3
domain 8
do
me
in 7
AMJJA
JJA
EV
0
0.1
0.2
0.3
0 0.1 0.2 0.3
domain 5
dom
ain
7
AMJJA
JJA
domain 7
0
0.1
0.2
0.3
0 0.1 0.2 0.3JJA
AM
JJA
Comparison of(EV) different classifications for ozone Comparison of(EV) different classifications for ozone domain 5,7,8domain 5,7,8
Jarczew, PolandJarczew, Poland
AMJJA
0
0.1
0.2
0.3
0.4
CE
C
ES
LPC
10
ES
LPC
30
EZ
500C
10
EZ
500C
30
GW
T
LIT
AD
VE
LIT
TC
LUN
D
LWT
2
NN
W
P27
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WLK
C73
3
domain 5
domain 8
domain 7
JJA
0
0.1
0.2
0.3
0.4
CE
C
ES
LP
C1
0
ES
LP
C3
0
EZ
50
0C
10
EZ
50
0C
30
GW
T
LIT
AD
VE
LIT
TC
LU
ND
LW
T2
NN
W
P2
7
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WL
KC
73
3
domain 5
domain 8
domain 7
Comparison (EV) ofComparison (EV) ofdifferent classifications for ozone different classifications for ozone
domain 5,7,8domain 5,7,8JarczewJarczew, Poland, Poland
domain 7
0
0.1
0.2
0.3
0.4
0 0.1 0.2 0.3 0.4
JJA
AM
JJA
EV
0
0.1
0.2
0.3
0.4
0 0.1 0.2 0.3
domain 8
do
me
in 7
AMJJA
JJA
EV
0
0.1
0.2
0.3
0.4
0 0.1 0.2 0.3
domain 5
dom
ain
7
AMJJA
JJA
Comparison of (EV) different classifications for ozone Comparison of (EV) different classifications for ozone domain 7,8domain 7,8
Humenne - SlovakiaHumenne - Slovakia
AMJJA
0
0.1
0.2
0.3
0.4
CE
C
ES
LPC
10
ES
LPC
30
EZ
500C
10
EZ
500C
30
GW
T
LIT
AD
VE
LIT
TC
LUN
D
LWT
2
NN
W
P27
PC
AC
A
PC
AX
TR
KM
PC
AX
TR
PE
TIS
CO
SA
ND
RA
SA
ND
RA
S
TP
CA
V
TP
CA
07
WLK
C73
3
domain 8
domain 7
EV
0
0.1
0.2
0.3
0 0.1 0.2 0.3
domain 8
do
me
in 7
AMJJA
Conclusions:
1.1. Evaluation of classifications:Evaluation of classifications:• WSD and WSD_U parameters are not good for comparing results for different air WSD and WSD_U parameters are not good for comparing results for different air
pollutants.pollutants.• Normalisation of WSD and WSD_UNormalisation of WSD and WSD_U parameters is necessary. parameters is necessary. • By comparing EV, nWSD and nWSD_U with variability of PM10 and SO2 for By comparing EV, nWSD and nWSD_U with variability of PM10 and SO2 for
classification ESLPC30 it is found that nWSD is not good parameter for evaluation classification ESLPC30 it is found that nWSD is not good parameter for evaluation classification.classification.
• By comparing EV and R2 for DJF (Poland, Belgium - PM10, Romania - TSP) is By comparing EV and R2 for DJF (Poland, Belgium - PM10, Romania - TSP) is observed the similar behavior for both parameters. It seems that parameter EV is observed the similar behavior for both parameters. It seems that parameter EV is sometimes better.sometimes better.
2.2. The best classifications for winter urban air pollution are:The best classifications for winter urban air pollution are:• Classifications with number of classes greater than 15• Objective classifications: LWT2, LITTC, Sandra, Sandras, • Manual classifications: HBGWL, OGWL and Polish Tc21 classification prepared by
Niedźwiedź
3.3. The best classifications for summer ozone concentrationsThe best classifications for summer ozone concentrations are :are :• objective classifications: CEC, GWT, LITTc, LWT2, Petisco for all areas (Poland,
Slovakia, Germany, Belgium)• objective WLKC733 classifications for Polish stations• objective Sandras classification for Belgian stations• manual Polish Tc21 and Tc11 classifications prepared by Niedźwiedź for Slovak,
German and south or central Polish stations• manual ZAMG classification for German and south or central Polish stations• manual HBGWL, OGWL and Perret for Belgian stations
4.4. There are not considerable differences between classifications evaluations There are not considerable differences between classifications evaluations prepared on the basis of calculations made for different domains prepared on the basis of calculations made for different domains
(when sites of stations are at the border areas in different domains(when sites of stations are at the border areas in different domains).).
Thank you for your attention
KONTAKT:Tel: 12 6398119E-mail: [email protected] [email protected]
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