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Department of Monitoring and Model l ing Air Pollution, Cracow, Poland

COST-733 WG 4 -Meeting. INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT. Department of Monitoring and Model l ing Air Pollution, Cracow, Poland. TITLE : Comparison of selected weather types classifications , for forecasting the days with high air pollution in Cracow. Jolanta Godłowska.

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Department of Monitoring and Model l ing Air Pollution, Cracow, Poland

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  1. COST-733 WG4-Meeting INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT Department of Monitoring and Modelling Air Pollution, Cracow, Poland TITLE :Comparison of selected weather types classifications, for forecasting the days with high air pollution in Cracow. Jolanta Godłowska Brussels 6-7.III.2008

  2. Comparison of selected weather types classifications, for forecasting the days with high air pollution in Cracow. • Data: • AIR POLLUTION • 1994 -1999 • daily mean concentrations (SO2, PM10, NO2) ormaximal daily 8-hourconcentrations (CO) • 4 air pollution station in Cracow (2- Krowodrza, 4 - Prokocim, 5 -Aleje, 6-Nowa Huta) • SODAR (boundary layer parameters) • 1994-1999, X-III • mean daily number and maximal, minimal and mean daily height of: • - convection • ground inversion • elevated inversion

  3. My questions are: • Does urban air pollution in Cracow depend on circulation type? • How? • What kind of classifications is the best for forecasting situations with high concentrations? • My problems are: • incoherently situated sources of emission (industrial, traffic, unorganized) • Find a solution: • I analysed data from 4 pollution stations, together • change of emission within year • Find a solution: • For each class I analysed number of days with air pollution greater than 90-percentille calculated for each month separately • Type of data: Number of days (kifor each class) meeting our criteria • Methods evaluation of classification: Lorenc curve, Gini coefficient ,ev. c2 • For each class and for each seasonseparately I analysed daily mean of the SO2, PM10, NO, NO2, NOx and CO concentrations (ANOVA, MANOVA). • Type of data: all daily mean pollution concentration for each class • Methods evaluation of classification :criteria WG3 - explained variation (EV)

  4. Analysis for the days when 90-percentille (calculated for each month and each air pollution separately)is exceeded (1994-1999). • Criteria to determine days with growing air pollution in Cracow: • Choosing days (for each pollution and each station separately) when pollution concentration exceed 90 –percentille. • Choosing days when we have no less than 2 different pollution at 2 different stations meeting criteria 1. • N – total number of days • N=2191 • K – days meeting criteria 2 • K=274 Variability of 90 percentille daily mean SO2, CO, PM10, NO2 concentrations calculated for every month of year separately (1994-1999).

  5. Does probability occurrence high pollution concentration depend on Lityński circulation type? • Occurrence of : • Lityński circulation types (up), • N= ∑ ni =2191 • N – total number of days (1994-1999) • days meeting criteria 2 (in the middle) • K= ∑ ki =274 • K – days meeting criteria 2 (1994-1999) • Probability occurrence days meeting criteria 2 calculated for each Lityński circulation type separately (down) • pi=ki/ni • 27 • ∑ pi ≠ 1 • i=1 • 27 • ∑ pi ≠ K/N • i=1

  6. Comparison of the results obtained for different weather type classification • How compare results for different classifications when we have only 2 numbers for each class (number of days meeting our criteria (ki) and total number of days (ni) for class i)? • There are two problems with comparison of the results for different classifications: • 1. Unequal number of classes for different classifications • (eg I=9 for LITadwe, I=10 for LUND, I=18 for WTC, I=27 for Litynski, I=40 for CEC). • Unequal number of days in every class (unequal ni). • Consequence: • We don’t properly compare classifications using entropy. • BUT WE CAN COMPARE OUR RESULT WITH LORENC CURVE AND GINI COEFFICIENTAND MAYBE using c2statistic

  7. DOMAIN 7

  8. Lorenc curve • The Lorenz curve (in statistics) is a curve described extent of inequality for distribution of some characteristic. It is graphical representation of the cumulative distribution function of a probability distribution; It is often used to represent income and wealth distribution. • How we can obtain the Lorenc curve for comparison different weather types classifications? • For each class (separately) of some classification we must calculate pi=ki/ni (probability occurrence days with some characteristic (eg high pollution concentration, large precipitation, fog)) • We sort our data according rising pi. • 0<p1<p2<…<pI • We must calculate abscissa and ordinate for each point Lorenc curve: • x0=y0=0 • k • xk=(∑ ni)/N • i=1 • k • yk=(∑ ki)/K • i=1 • xI=yI=1 • The Gini coefficient is a measure of statistical dispersion. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenz curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. Thus, a low Gini coefficient indicates more equal income or wealth distribution, while a high Gini coefficient indicates more unequal distribution. 0 corresponds to perfect equality (everyone having exactly the same income) and 1 corresponds to perfect inequality (where one person has all the income, while everyone else has zero income). The Gini coefficient requires that no one have a negative net income or wealth. • The Gini coefficient was developed by the ItalianstatisticianCorrado Gini and published in his 1912 paper "Variabilità e mutabilità" ("Variability and Mutability").

  9. Gini coefficient The Gini coefficient is a measure of statistical dispersion. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenc curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. Thus, a low Gini coefficient indicates more equal distribution, while a high Gini coefficient indicates more unequal distribution. 0 corresponds to perfect equality and 1 corresponds to perfect inequality . The Gini coefficient was developed by the Italian statistician Corrado Gini and published in his 1912 paper "Variabilità e mutabilità". How we can obtain the Gini coefficient for compare different weather types classifications? I G=1-∑ (xi-xi-1)(yi+yi-1) i=1 for k xk=(∑ ni)/N i=1 k yk=(∑ ki)/K i=1 I-number of classes for some classification The Gini coefficient is a measure of statistical dispersion. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenz curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. Thus, a low Gini coefficient indicates more equal income or wealth distribution, while a high Gini coefficient indicates more unequal distribution. 0 corresponds to perfect equality (everyone having exactly the same income) and 1 corresponds to perfect inequality (where one person has all the income, while everyone else has zero income). The Gini coefficient requires that no one have a negative net income or wealth. The Gini coefficient was developed by the ItalianstatisticianCorrado Gini and published in his 1912 paper "Variabilità e mutabilità" ("Variability and Mutability").

  10. Gini coefficient - resuls

  11. c2 statistic The c2statistic: I c2=∑ ((ki-Npiteor)2 /Npiteor ) i=1 for :piteor= (ni/N)*(K/N) ki-number of days meeting our criteria for i class ni- total number of days for i class K – amount of days meeting criteria 2 (1994-1999) N – total amount of days(1994-1999) I-number of classes for some classification Comparison ability to forecast bed situation in air pollution in Cracow by using different weather typeclassifications (c2statistic and GINI coeff.)

  12. My questions are: • Does urban air pollution in Cracow depend on circulation type? • How? • What kind of classifications is the best for forecasting situations with high concentrations? • My problems are: • incoherently situated sources of emission (industrial, traffic, unorganized) • Find a solution: • I analysed data from 4 pollution stations, together • change of emission within year • Find a solution: • For each class I analysed number of days with air pollution greater than 90-percentille calculated for each month separately • Type of data: Number of days (kifor each class) meeting our criteria • Methods evaluation of classification: Lorenc curve, Gini coefficient ,ev. c2 • For each classand for each seasonseparately I analysed daily mean of the SO2, PM10, NO, NO2, NOx and CO concentrations (ANOVA, MANOVA). • Type of data: all daily mean pollution concentration for each class • Methods evaluation of classification:criteria WG3 - explained variation (EV)

  13. Comparison within-type air pollution in Cracow variability for 9 objective (7 from COST733 classification catalog – domain 7) and 2 subjective classification

  14. Comparison within-type air pollution in Cracow variability for 17 objective (16 from COST733 classification catalog – domain 7) and 2 subjective classification

  15. PM10 IN WINTER (XI-II) IN CRACOW EV RESULTS FOR DIFFERENT STATIONS

  16. SO2 IN WINTER (XI-II) IN CRACOW EV RESULTS FOR DIFFERENT STATIONS

  17. NO2 IN WINTER (XI-II) IN CRACOW EV RESULTS FOR DIFFERENT STATIONS

  18. CO IN WINTER (XI-II) IN CRACOW EV RESULTS FOR DIFFERENT STATIONS

  19. Conclusions: • For both way of our analysis we obtain similar results • The best (for air pollution in Cracow) classification is subjective Polish classification TCN21 prepared by Niedzwiedz • Good but a little worst are LITtc, Ustrnul and LWT2 classifications • There are some differences between different air pollutions – • for PM10 the largest values of SV exceed 30% for LUND, WTC, LWT2, Ustrnul • For NO2 the largest values of SV exceed 30 % for LWT2, LITtc, Ustrnul • For SO2 the largest values of SV exceed 20 % (a little) for LWT2, LITtc, Ustrnul • For CO the largest values of SV exceed 20 % (a little)for LWT2, LITtc, Ustrnul, WTC, Petisco • There are some differences between different stations – • The values of SV for PM10 at the station 6 (large industrial emission from Mittal Steel) are larger than at other stations • The values of SV for NO2 at the station 2 are larger than at other stations

  20. Thank you for your attention KONTAKT: Tel: 12 6398119 E-mail: zigodlow@cyf-kr.edu.pl IMGW 01-673 Warszawa, ul.: Podleśna 61 tel.: (022) 56 94 000 fax: (022) 00 00 000 kom.: 0 503 000 000 nazwa@imgw.pl www.imgw.pl

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