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CORRELATIONS IN POLLUTANTS AND TOXICITIES. Kovanic P. and Ocelka T. The Institute of Public Health, Ostrava, Czech Republic. DATA. Actions: Regular monitoring of Czech and Moravian rivers Period: 2002 – 2007

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correlations in pollutants and toxicities

CORRELATIONS IN POLLUTANTS AND TOXICITIES

Kovanic P. and Ocelka T.

The Institute of Public Health, Ostrava, Czech Republic

slide2
DATA
  • Actions: Regular monitoring of Czech and Moravian rivers
  • 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.
  • Field activity: Institute of Public Health, Ostrava (IPH)

(The National Reference Laboratory)

  • Chemical analyses: Laboratories of the IPH, Frýdek-Místek
  • Mathematical (Gnostic) analysis: IPH
  • Particular problem:

Are there any interactions

between pollutants?

natural assumptions
“NATURAL” ASSUMPTIONS ?
  • 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.

Positive and significant interactions between

concentrations of pollutants are expected.

IS IT TRUE?

comments
COMMENTS
  • Concentrations of groups of pollutants

differ by orders of magnitude.

  • Distributions differ not only by mean

levels but also by their forms.

  • Distributions are non-Gaussian (“normal”):

domains are finite, densities asymmetric.

  • Datavariability is strong, robust analysis

must be applied.

two approaches to interactions
TWO APPROACHES TO INTERACTIONS
  • Robust correlation coefficients:

interdependence of deviations from the mean value

  • Robust regression models:

interdependence of variables

The former does not imply

the latter automatically !

robust correlations
ROBUST CORRELATIONS
  • Robust estimate: low sensitivity

to “bad” data

  • Non-robust estimates: point statistics

(sample estimates of statistical moments)

  • Many robust estimates exist producing

different results

diversity of estimates
DIVERSITY OF ESTIMATES
  • In the past: lack of robust methods
  • Recently: abundance of robust methods
  • Diversity of results:

IN WHICH METHOD TO BELIEVE?

inference
INFERENCE
  • Significant interactions between groups

of pollutants have been confirmed.

  • Assumption of positive interactions was

falsified: there exist negative interactions.

  • Group HCH initiates negative effects.
  • Interactions of groups implie interactions between individual congeners.

Which congeners interact negatively

and how much?

dependence of pollutants y on the gammahch x
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)’

GRAPHS:

Straight line is the robust linear model.

Points depict the data values (X, Y)

NOTE:Vertical scalings (of Y) differ, the horizontal scale (of X) remains unchanged

parameters of the function lognat y intercept coef lognat gammahch
PARAMETERS OF THE FUNCTIONlognat(Y) = Intercept + Coef × lognat(gammaHCH)

Pollutant (Y) Intercept STD(Intct) Coeff. STD(Coef)

OCDD -1.01 0.23 -0.376 0.040

TCDD 0.080.34 -0.4240.057

PeCDD -0.94 0.28 -0.4540.048

HxCDD -2.580.16-0.0820.027

HpCDD -1.53 0.27 -0.3120.046

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

parameters for the hch and pbde
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 9.150.61-0.666 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

relative impacts of gammahch impact mean pollut concentr how many times is the mean exceeded
RELATIVE IMPACTS OF gammaHCHImpact/mean(pollut.concentr.)(How many times is the mean exceeded)
pollutant s toxicity
POLLUTANT’S TOXICITY

Four methods to measure toxicity:

  • Daphnia Magna
  • Vibrio Fischeri
  • Desmodemus subspicatus
  • Saprobita
natural assumptions29
“NATURAL” ASSUMPTIONS

A) Methods measuring the same give thesame results or

B) Results of measuring the same are at

least similar (correlated)

C) The more pollutant’s concentration, the more toxic effects

significant correlations with toxicities
SIGNIFICANT CORRELATIONSWITH TOXICITIES

Other correlations are not significant.

“Natural” assumptions A) through C)

are not supported by the data.

Let us try the MD-models !

worthwhile
WORTHWHILE
  • MD-models confirm the existence of „contrary toxic effects“.
  • The group PCB affects the toxicity contrary to other groups of pollutants in3 of 4 MD-models in spite of the positiveness of all correlations

(pollutant, toxicity).

summary
SUMMARY

Statistically significant (mostly positive) correlations in organic pollutants exist.

Negative correlations exist as well.

The most negatively “active” is gammaHCH.

Its strongest negative effects are manifested by the congeners of PBDE.

Contrary toxicity impacts of pollutants exist.

HYPOTHESES MUST BE TESTED !

open problems
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 they worth of further investigation?

EXPERIENCE:

DATA TREATMENT MUST BE ROBUST

AND HYPOTHESES MUST BE TESTED !

funding
FUNDING

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