1. CORRELATIONS IN POLLUTANTS AND TOXICITIES Kovanic P. and Ocelka T. The Institute of Public Health, Ostrava, Czech Republic

2. DATA Actions: Regular monitoring of Czech and Moravian rivers Actions: Regular monitoring of Czech and Moravian rivers Period: 2002 – 2007 Period: 2002 – 2007 Profiles: 21 locations of rivers Bečva, Berounka, Bílina, Dyje, Jihlava, Jizera, Labe, Lužická Nisa, Lužnice, Morava, Odra, Ohře, Opava, Otava, Sázava, Svratka, Vltava. Profiles: 21 locations of rivers Bečva, Berounka, Bílina, Dyje, Jihlava, Jizera, Labe, Lužická Nisa, Lužnice, Morava, Odra, Ohře, Opava, Otava, Sázava, Svratka, Vltava. Field activity: Institute of Public Health, Ostrava (IPH) Field activity: Institute of Public Health, Ostrava (IPH) (The National Reference Laboratory) (The National Reference Laboratory) Chemical analyses: Laboratories of the IPH, Frýdek-Místek Chemical analyses: Laboratories of the IPH, Frýdek-Místek Mathematical (Gnostic) analysis: IPH Mathematical (Gnostic) analysis: IPH Particular problem: Particular problem: Are there any interactions Are there any interactions between pollutants? between pollutants?

3. “NATURAL” ASSUMPTIONS ? “NATURAL” ASSUMPTIONS ? Contaminations are generated, polluted and accumulated mostly simultaneously, hence the more contaminants, the higher contamination and opposite. Contaminations are generated, polluted and accumulated mostly simultaneously, hence the more contaminants, the higher contamination and opposite. The more pollutant A, the more polutant B. The more pollutant A, the more polutant B. Positive and significant interactions between Positive and significant interactions between concentrations of pollutants are expected. concentrations of pollutants are expected. IS IT TRUE? IS IT TRUE?

6. COMMENTS Concentrations of groups of pollutants Concentrations of groups of pollutants differ by orders of magnitude. differ by orders of magnitude. Distributions differ not only by mean Distributions differ not only by mean levels but also by their forms. levels but also by their forms. Distributions are non-Gaussian (“normal”): Distributions are non-Gaussian (“normal”): domains are finite, densities asymmetric. domains are finite, densities asymmetric. Data variability is strong, robust analysis Data variability is strong, robust analysis must be applied. must be applied.

7. TWO APPROACHES TO INTERACTIONS Robust correlation coefficients: Robust correlation coefficients: interdependence of deviations from the mean value interdependence of deviations from the mean value Robust regression models: Robust regression models: interdependence of variables interdependence of variables The former does not imply the latter automatically !

8. ROBUST CORRELATIONS ROBUST CORRELATIONS Robust estimate: low sensitivity to “bad” data to “bad” data Non-robust estimates: point statistics (sample estimates of statistical moments) (sample estimates of statistical moments) Many robust estimates exist producing different results different results

10. DIVERSITY OF ESTIMATES In the past: lack of robust methods Recently: abundance of robust methods Diversity of results: IN WHICH METHOD TO BELIEVE? IN WHICH METHOD TO BELIEVE?

14. INFERENCE Significant interactions between groups Significant interactions between groups of pollutants have been confirmed. of pollutants have been confirmed. Assumption of positive interactions was Assumption of positive interactions was falsified: there exist negative interactions. falsified: there exist negative interactions. Group HCH initiates negative effects. Group HCH initiates negative effects. Interactions of groups implie interactions between individual congeners. Interactions of groups implie interactions between individual congeners. Which congeners interact negatively and how much?

16. DEPENDENCE OF POLLUTANTS (“Y”) ON THE GAMMAHCH (“X”) Title L(Y)[L(X)] is to be read as ‘natural logarithm of the pollutant Y presented as a linear function of the natural logarithm of the pollutant X (gammaHCH)’ Title L(Y)[L(X)] is to be read as ‘natural logarithm of the pollutant Y presented as a linear function of the natural logarithm of the pollutant X (gammaHCH)’ GRAPHS: GRAPHS: Straight line is the robust linear model. Straight line is the robust linear model. Points depict the data values (X, Y) Points depict the data values (X, Y) NOTE: Vertical scalings (of Y) differ, the horizontal scale (of X) remains unchanged

24. PARAMETERS OF THE FUNCTION lognat(Y) = Intercept + Coef ×  lognat( gammaHCH) Pollutant (Y) Intercept STD(Intct) Coeff. STD(Coef) OCDD -1.01 0.23 -0.376 0.040 TCDD 0.08 0.34 -0.424 0.057 PeCDD -0.94 0.28 -0.454 0.048 HxCDD -2.58 0.16 -0.082 0.027 HpCDD -1.53 0.27 -0.312 0.046 OCDF -1.55 0.28 -0.344 0.047 TCDF 2.01 0.35 -0.446 0.058 PeCDF 0.36 0.33 -0.319 0.055 HxCDF -1.58 0.27 -0.249 0.045 HpCDF -2.07 0.25 -0.184 0.041

25. PARAMETERS FOR THE HCH AND PBDE Pollutant (Y) Intercept STD(Intct) Coeff. STD(Coef) alfaHCH 2.14 0.38 0.373 0.064 betaHCH -2.39 0.57 1.054 0.096 deltaHCH -3.47 0.52 1.057 0.089 HCB 9.15 0.61 -0.666 0.102 PBDE28 9.66 0.73 -1.603 0.123 PBDE47 15.30 0.69 -2.019 0.116 PBDE100 11.40 0.56 -1.698 0.095 PBDE99 13.39 0.71 -1.826 0.119 PBDE154 11.42 0.77 -2.004 0.130 PBDE153 9.92 0.71 -1.700 0.119 PBDE183 2.77 0.64 -0.534 0.105

26. RELATIVE IMPACTS OF gammaHCH Impact/mean(pollut.concentr.) (How many times is the mean exceeded) PollutantRel.ImpactPollutantRel.Impact OCDD -0.88 alfaHCH0.49 TCDD -0.89 betaHCH2.27 PeCDD -0.88 deltaHCH2.28 HxCDD -0.21 HCB -0.63 HpCDD -0.68 PBDE28 -2.22 OCDF -0.53 PBDE47 -3.20 TCDF -0.75 -0.75PBDE100 -2.82 PeCDFHxCDF -0.57 -0.33PBDE99PBDE154 -3.06 -3.33 HpCDF -0.31 PBDE153 -2.37 PBDE183 -0.20

27. POLLUTANT’S TOXICITY Four methods to measure toxicity: 1. 1. Daphnia Magna 2. 2. Vibrio Fischeri 3. 3. Desmodemus subspicatus 4. 4. Saprobita

29. “NATURAL” ASSUMPTIONS A) Methods measuring the same give the same results or B) Results of measuring the same are at least similar (correlated) C) The more pollutant’s concentration, the more toxic effects

30. SIGNIFICANT CORRELATIONS WITH TOXICITIES CorrelationCor. Coef.Prob{0} (Vibrio F., Desm. Subsp.)0.5240.022 (sumPAH, Desm. Subsp.)0.6430.010 (sumDDT, Daphnia Magna)0.5010.035 Other correlations are not significant. “Natural” assumptions A) through C) are not supported by the data. Let us try the MD-models !

35. WORTHWHILE MD-models confirm the existence of „contrary toxic effects“. The group PCB affects the toxicity contrary to other groups of pollutants in 3 of 4 MD-models in spite of the positiveness of all correlations (pollutant, toxicity).

36. SUMMARY Statistically significant (mostly positive) correlations in organic pollutants exist. Statistically significant (mostly positive) correlations in organic pollutants exist. Negative correlations exist as well. Negative correlations exist as well. The most negatively “active” is gammaHCH. The most negatively “active” is gammaHCH. Its strongest negative effects are manifested by the congeners of PBDE. Its strongest negative effects are manifested by the congeners of PBDE. Contrary toxicity impacts of pollutants exist. Contrary toxicity impacts of pollutants exist. HYPOTHESES MUST BE TESTED !

37. OPEN PROBLEMS Are these effects caused by some real chemical or physical reactions of the substances or only by different rates of their production and pollution? Are these effects caused by some real chemical or physical reactions of the substances or only by different rates of their production and pollution? Are they worth of further investigation? Are they worth of further investigation?EXPERIENCE: DATA TREATMENT MUST BE ROBUST AND HYPOTHESES MUST BE TESTED !

38. FUNDING European Commission Sixth Framework Program, Priority 6 (Global change and ecosystems), project 2-FUN (contract#036976)