FATEMOD MODELING FOR RISK EXPOSURE FROM CHEMICALS

Jaakko Paasivirta, Department of Chemistry, University,

Niilo Paasivirta, Suomen Postmaster (enterprise),

Jyväskylä, Finland

Risk management scheme (EPA)

Risk estimation Risk management

Dose/response assessment

Risk characteri- zation

Hazard identifi- cation

Exposure assessment

Feedback

Controldecision

Control options

Acceptablelevel deter- mination

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

Model Cyclops: E high, P low (mass invasions of non-native species)

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

Model Cyclops: E high, P low (mass invasions of non-native species)

Model Pythia: E uncertain, P uncertain (gene modification)

Risk "Pythia"

GMOs

Model Pythia: both E and P uncertain

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

Model Cyclops: E high, P low (mass invasions of non-native species)

Model Pythia: E uncertain, P uncertain (gene modification)

Model Pandora: E uncertain, P high (PET compounds – damage is irreversible)

Risk "Pandora"POPs

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

Model Cyclops: E high, P low (mass invasions of non-native species)

Model Pythia: E uncertain, P uncertain (gene modification)

Model Pandora: E uncertain, P high (PET compounds – damage is irreversible)

Model Cassandra: E high, P high (Climatic change - people do not believe)

Cassandra was a profet knowing the future. But people did not believe her (cource of Ares). Here Aigistos and Klytaimnestra are murdering Agamemnon and Kassandra

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

Model Cyclops: E high, P low (mass invasions of non-native species)

Model Pythia: E uncertain, P uncertain (gene modification)

Model Pandora: E uncertain, P high (PET compounds – damage is irreversible)

Model Cassandra: E high, P high (Climatic change - people do not believe)

Model Medusa: E low, P low (high frequency electro-magnetic fields. Many believe that risk is high).

USA a.d. 2001 Syracuse 580 b. Chr.

Images of Medusa Gorgon

RISK CHARACTERIZATION (Germany)Risk = Extend of Damage * Probability of its Occurrence

R = E x PModel Damokles: E high, P low (chemial accident)

Model Cyclops: E high, P low (mass invasions of non-native species)

Model Pythia: E uncertain, P uncertain (gene modification)

Model Pandora: E uncertain, P high (PET compounds – damage is irreversible)

Model Cassandra: E high, P high (Climatic change - people do not believe)

Model Medusa: E low, P low (high frequency electro-magnetic fields. Many believe that risk is high).

Environmental risk assessment of chemical

Exposure assessment(modelling)

Properties of the chemical and the environment

Effect potency assessment(ecotoxicology)

Tests, QSAR, Analyses

PEC PredictedEnvironmentalConcentration

PNEC Predicted No-Effect Con- entration

Risk ratio: Ro = PEC / PNEC by emission Eo

RISK EMISSION = Eo / Ro

Modeling for prediction of thefate of chemical in environment

Machbetin kohtalon ennustaminen: eksaktia noituutta! To predict fate of Machbet:Exact witchcraft !

2346TeCP

OH

Cl

Cl

Cl

Cl

OHOCH3

Cl

Cl

Cl

456TCG

O

ClP

O

O

ClP

PCDD PCDF

CCl3

Cl ClDDT

ClClCl

ClCl Cl

AHCH

ClCl

ClCl

Cl

Cl

HCBz

To predict fate of a chemical in environment - science ?

MODELLING / SIMULATION

EXACT ?

Risk estimation, source detection,need of restrictions or remediation,groudwater quality, obstacles to agriculture, ecosystem damages

Environmental fate prediction models

A. Mackay Steady State Fugacity Models

Level 1. Equilibrium distribution of a totally persistent chemical substance between compartments Air, Water, Soil, Sediment, Suspended Solids and Biota (Fish)

Level 2. Equilibrium including transformations and advections. -----> estimate of residence times (reaction, advection and total)

Level 3. Steady state but non-equilibrium model. -----> estimates more accurate residence times and concentrations in Air, Water, Soil and Sediment under continous constant emission while many significant environmental fate processes are included.

Level 4. Estimates concentrations in Air, Water, Soil, Sedimentand Fish as function of time after stop of all emissions to system.

CemoS programs: Trapp & Matthies (1996)English Handbook: Springer 1998.

AIR. Concentrations caused from continuous point-source emission.

BUCKETS. Transport of compound in surrounding soil layer.

CHAIN. Chain reaction ----> bioaccumulation in a food chain.

LEVEL 1. Identical with the Mackay Level 1 model.

LEVEL 2. Concentrations by continous emission, residence time in system and ("Level 4") recovery from contamination

PLANT. Uptake by plants from soil and air.

SOIL. Vertical movement and fate of chemical in different soil contamination types.

PLUME. Concentration in Air after point emission event.

WATER. Concentrations in River or river-like water system.

FATEMOD model-> Modified Mackay level 1-4 model for fate of chemical in a one box six compartment catchment area environment (WINDOWS program)-> Values of the physical properties and degradation lifetimes of the chemical are automatically adjusted for ambient temperature and pH values of water and soilwater-> Level 1 and 2 output can be used as compound property values in other specific fate models like CemoS and bioaccumulation models

-> Level 3 output gives realistic estimates of levels and residence times by constant emission in non equilibrium steady state

-> Level 4 output presents concentrations at different times after stop of emission (a steady state prediction to the future)

-> To include transport of chemical within catchments, FATEMOD for the joint areas can be run successively. Then, the modelled flows from one area are considered as emissions to the adjacent neighbour area-> The properties of environments and compounds including correction coefficients for temperature adjustments are recorded in the editable database of FATEMOD. Report to EXEL takes place by push button

EXAMPLE OF APPLICATIONS:

Use of the FATEMOD model in the environmental risk estimation of chemicals in discharges

Jaakko Paasivirta, Seija Sinkkonen, Markus Soimasuo, University of Jyväskylä, Finland

FATEMOD database: parametrization of the values for properties of the environments and chemicals

Properties of the environments.Instead using unit world box 1 x 1 x 1 Km as suggested by D.Mackay Multimedia Environmental Models L-242, Lewis, Chelsea, MI, USA)suitable for general risk estimation of chemicals, we adopted naturalcatchment areas as model environments to achieve more flexibility for different cases of risk evaluations. Properties of the chemical compounds.Molecular properties: Name, Group, Subgroup, CAS register number, Molar mass (WM), Melting point (Tm K), Entropy of Fusion (ΔSf), Liquid state molar volume (Vb), pKa (for acids or bases)

Temperature-dependent properties: Log(pr) = Apr – Bpr. Vapor pressure in liquid state (Pl Pa), Solubility in water (S mol m-3),Henry’s law function (H Pa m3 mol-1), Hydrophobity LogKow (where

Kow is the octanol-water partition coefficient) and…. Degradation half-life times HL(i) (i = 1 air, 2 water, 3 soil/plants and 4 sediment; reference time HLT (usually 20 or 25 C)

AirWaterSoil

SedimentSuspended Solids

Fish

FATEMOD environment: catchment area

/ Plants¤ Sizes etc: Area, Depth, Density, pH, Fraction of organic carbon¤ Processes: Advections (flows), Diffusions, Evaporation, Depositions

¤ Temperature

Kemijoki River catchment area (51120 Km2).Mean annual temperature +1 OC

South-West Finland area(36358 Km2). Catchment ofthe Rivers flowing to theBothnian Sea.Mean annual temperature +8 OC

SWF

KemR

Bay ofBothnia

Bothnian Sea

Gotland

0 100 200

km

N

S

Sweden

29

67

o

o

Arctic

Circle

Finland

Russia

Estonia

Jyväskylä

Helsinki

StockholmSt.Petersburg

Baltic Sea

Gulf of Finland

Ladoga

Norway

FATEMOD window for editing property values of the environment box

Southwest Finland (SWF) = catchment area of the Finnish Rivers flowing to the Bothnian Sea. Major compartments for mass balance: Air, surface Water, Soil (including surface plants), and Sediment. Minor compartments for concentration data: Suspended sediment and Fish (aquatic biota).

FATEMOD editing window for substance parameters

Determination of the compound property as function of temperature

VPLEST for evaluation the coefficients Apl and Bpl for:

Log Pl = Apl - Bpl / TMethod is from Clark F. Grain in Handbook of Chemical Estimation Methods, W.J.Lyman, W.F.Reehl and D.H.Rosenblatt (Eds), ACS, Washington, DC (1990) in Chapter 14. Liquid state vapor pressures are computed in one Celsius intervals at environmental range (e.g. -2 to + 30C) by Grain’s equation 14-25 using one known Vp and temperature as reference. Then, the coefficients are determined by linear regression.

(SUBCOOLED) LIQUID STATE VAPOR PRESSURE

The reference Vp can be for either solid or liquid state (Ps or Pl). They can be converted to each other by equation: Log Ps = Log Pl + ∆Sf x (1-Tm/T) / (R x Ln10) 0bs. R x Ln10 = 19.1444

Conversions between temterature coefficients for Vp’s are:Aps = Apl + ∆Sf / (RxLn10) and Bps = Bpl + ∆Sf x Tm / (R*Ln10)

VPLEST result for liquid state Vp’s of DNOC is: Compound Mp C ∆Sf Pl(25) Apl Bpl Aps Bps DNOC 86.5 57.04 0.243 11.31 3496 14.29 4567

OH

CH3

NO2

O2N

0 5 10 15 20 25 30

0

0.5

1

1.5

2Pl (mPa)

Lei et al. 1999

124578-hexachloronaphthalene: Pl values by two methods

GCVPLEST

Cl

Cl

Cl

ClCl

Cl

t OC

EXAMPLE: Dimethoate, Tm = 326, DSF = 74.9

0 5 10 15 20 25 300

2

4

6

8

10

12

14

16

18

20

Ps

Pl

PSH 3C-O

S-CH 2-CO-NH-CH 3H 3C-O

mPa

t OC

Aps = 11.646; Bps = 4147.6

Apl = 7.733; Bpl = 2871.6

OH

CH3

NO2

O2N

Herbicide DNOC: evaluation of solubility coefficients for FATEMOD

CAS 534-52-1, WM 198.122, Mp 86.5 C →Tm 359.65 KEnthalpy of fusion Δ Hf = 20515 J mol-1 (DSC by C.Plato(1972) Anal. Chem. 44, 1531-1534).Entropy of fusion Δ Sf = ΔHf / Tm = 57.04 J K-1 mol-1.

Liquid state molar volume Vb = 137.4 cm3 mol-1 [from incrementsof P.Ruelle et al. (1991) Pharm. Res. 840-850. pKa = 4.31

Solubility parameter DB = Σ Fdi / Vb according to P.Ruelle (2000) Chemosphere 40, 457-512. Σ Fdi is the dispersion component of molar attraction constant calculated from increments of C.W.van Krevelen (1990) in: Properties of Polymers, Elsevier, Amsterdam, pp. 212-213. Value calcd. for DNOC = 18.20.

Parameters needed for estimation of water solubility and hydrophobity of thechemicals are association terms [P.Ruelle (2000) Chemosphere 40, 457-512].vAcc and vDon are the numbers of active sites. KAccW(i) and KDonW(i) are stability constants for proton acceptor and donor groups of the compound in the water. Similar terms for the compound in n-octanol are KAccO(i) and KDonO(i). The greatest value of these association terms, MAXW or MAXO are also needed in evaluation. Additionally, sum of the hydroxyl groups is NOH, and parameter boh has value of 1, 2 or 2.9 for primary, secondary of tertiary OH group, respectively. Example: association terms for DNOC are (KAccO values are zeros) vAcc vDon KAccW(i) KDonW(i) MAXW KDonO(i) MAXO 2 1 100,100 5000 5000 5000 5000

Solubility in water S mol m-3

WATSOLU.bas for evaluation the coefficients for: Log S = As - Bs / T

WATSOLU is based on mobile order thermodynamics estimation for log S at 25 C (P.Ruelle et al. (1997) Int. J. Pharm. 157, 219-232). We have divided equations to temperature dependent (Bs/T) and non-dependent (As) parts:

As = 5.154 + ∆Sf / (RxLn10) - 0.036xVb-0.217xLnVb + ΣNOHx(2+boh) / Ln10 + ΣvAcc(i)xLog(1+KaccW(i)/18.1) + ΣvDon(i)xLog(1+KDonW(i)/18.1) Bs = ∆Sf x Tm / (RxLn10) + (DB- 20.5)2 x Vb / (RxLn10) x Log (1+MAXW / 18.1)

Example: Output from WATSOLU for DNOC: As = 4.617, Bs = 1071.7

VOLATILITY: Henry’s law fuction Simple conversions for Log H = Ah – Bh / TAt the narrow temperature range of environments values of Ah and Bh are in fair agreement with the relation H = Pl / S. Therefore, FATEMOD model automatically calculates them by conversions Ah = Apl – As, and Bh = Bpl - Bs .

Example: conversion result for DNOC: Ah = 6.693 Bh = 2424.3

OH

CH3

NO2

O2N

DNOC

0 5 10 15 20 25 300

100

200

300

400

500

600

Pl mPa

S (mg L-1) / 10

H mPa m 3

mol -1

t C

5 10 15 20 25 30 35 40 45

0

0.1

0.2

0.3

0.4

0.5

0.6

JPTam

456TCGS mol m -3

t OC

pH adjusted

Cl

Cl

Cl

OH

OCH3

WATSOLU

HPLC

Validation of S estimate by two independent methods:

Tam D, Varhanikova D, Shiu WY and Mackay D (1994) J.Chem.Eng.Data 39, 82-86.

pKa = 4.31

pH of the eluent = 5.60

TDLKOW.bas for octanol/water partition: LogKow = Aow – Bow / T Is based on thermodynamic estimation of LogKow at 25 C of P.Ruelle (2000) Chemosphere 40, 457-512. We have divided Ruelle’s equations in two parts to obtain the temperature coefficients Aow and Bow:

Aow = ∆B + ∆F + ∆Acc + ∆Don ∆B = (0.5 x Vb x (1/124.2-1/18.1) + 0.5 x Ln(18.1/124.2) / Ln10 ∆F = [(vB x (rw/18.1 – ro/124.2) – ΣNOH x (boh + rw – ro)] / Ln10 ΔAcc = ΣvAcc x Log[(1 + KaccO(i) / 124.2)/(1 + KaccW(i) / 18.1)] ΔDon = ΣvDon x Log[(1 + KdonO(i) / 124.2) / (1 + KdonW(i) / 18.1)]

Bow = (Vb/(RxLn10)x[(DB-20.5)2/(1+MAXW/18.1)–(DB-16.38)2/(1+MAXO/124.2)] Where 18.1 is the molar volume of pure water, 124.2 the reduced molar volume of water- saturated n-octanol, rw structuration factor for water (2.0) and ro structuration factor for wate-saturated n-octanol. Observe that association coefficients for water are the same as those in WATSOLU.bas (see above). The temperature coefficient Bow is practically zero for compounds (often POP’s) having only one kind of substituents, but with several polar and different substituents in structure Bow can be significant.

Example1: TDLKOW output for DNOC: Aow = 3.826 Bow = - 0.439

Hydrophobity (lipophility) as Log Kow is also temperature-dependent!

Example 2: Musk xylene parameters from TDLKOW are Aow = 5.022 and Bow =361.6 in fair agreement of HPLC and literature values /J.Paasivirta, S.Sinkkonen, A-L.Rantalainen,D.Broman and Y.Zebühr (2002) Environ Sci & Pollut Res 9(5), 345-355/.

NO2

NO2

O2N

Musk xylene

HLT = 20 OC reference values for DNOC are HL(1) = 170 h, HL(2) = 500 h, HL(3) = 720 h, HL(4) = 1000 h

QSPR estimation of the reference lifetimes. Example for polychloronaphthalenes (PCNs). Based on maximal and minimal HLT 25OC values in NCl classes of PCDF mode of Mackay et al. and QSPR from environmental data (J.Falandysz 1998). The most abundant PCN congeners in Baltic Sea are included here:

Code Cl-subst. NCH-CH NβCls F ¤ HL(1) h HL(2) h HL(3) h HL(4) hCN42 1,3,5,7 0 2 13 522 1740 26100 87000CN33 1,2,4,6 2 1 20 483 1610 24150 80500CN28 1,2,3,5 2 2 26 444 1480 22200 74000CN27 1,2,3,4 3 2 33 405 1350 20250 67500CN35 1,2,4,8 2 3 33 405 1350 20250 67500CN38 1,2,5,8 2 3 33 405 1350 20350 67500CN46 1,4,5,8 2 4 39 366 1220 18300 61000CN52 1,2,3,5,7 0 1 7 561 1870 28050 93500CN58 1,2,4,5,7 0 2 13 522 1740 26100 87000CN61 1,2,4,6,8 0 2 13 522 1740 26100 87000CN50 1,2,3,4,6 1 1 13 522 1740 26100 87000CN51 1,2,3,5,6 1 2 20 483 1610 24150 80500CN57 1,2,4,5,6 1 2 20 483 1610 24150 80500CN62 1,2,4,7,8 1 2 20 483 1610 24150 80500CN53 1,2,3,5,8 1 2 20 483 1610 24150 80500CN59 1,2,4,5,8 1 3 26 444 1480 22200 74000CN66 1,2,3,4,6,7 0 0 0 600 2000 30000 100000CN64 1,2,3,4,5,7 0 1 7 561 1870 28050 93500CN69 1,2,3,5,7,8 0 1 7 561 1870 28050 93500CN71 1,2,4,5,6,8 0 2 13 522 1740 26100 87000CN63 1,2,3,4,5,6 1 1 13 522 1740 26100 87000CN65 1,2,3,4,5,8 1 2 20 483 1610 24150 80500

¤ F = (NCH-CH + Nβ)*6.5 % ; HL(i) = HL(i) max * (100 - F) / 100

0

0.5

1

1.5

2

2.5

3

3.5

4

Fish ng/g

Water ng/L

Soil ng/g

Sedim. ng/g

Prediction of contents of lindane in SWF environment after stop of all local uses 1.1.1990

981990 91 92 93 94 95 96 97 99 2000

FATEMOD level IV concentratios in water after stop of early May application of 10 Kg DNOC per hectare on plants (0.7 % was

leached to water) inSWF and KemR areas of South- West and North Finland.

Months

ug/L

OH

O2N

NO2

CH3

DNOC

PNEC / fish

LC50 / fish

KemR 5 OC

SWF 20 OC

0 2 4 6 8 10 12 14 16 18 200

50

100

150

200

250

300

350

400

Industrial discharge to Coastal Bothnian Bay

Recipient Sea Area (RSA) Ar(1,2,3) = 1E+6 (=1000000) m2

HT(1)=500, HT(2) =10, HT(4)=0.01 m GA(1)=2.5E+7, GA(2)=2.86E+5, GA(4)=0.2 m3 h-1

GRA(1)=20, GRA(2) =35, GRA(4)=50000 h OCFr(4) = 0.04

Waste water stream (WS)AR(1,2,4) = 31250 m2 HT(1)=100, HT(2)=3, HT(4)=0.01 mGA(1)=3125000, GA(2)=4167, GA(4)=0.00625 m3 h-1

GRA(1)=1, GRA(2)=22.5, GRA(3)=50000 hOCFr(4) = 0.06

Guideline determination for industrial emission

IFT

CBz

Cl

DKN

ON

H 3 CSO

2

CF 3

COCH C

C

The process chemicalsemitted to the wastestream ---------

LC50 PNEC LC50 PNEC LC50 PNEC Code mg L-1 ug L-1 mg L-1 ug L-1 mg L-1 ug L-1

CBz 5.8 580 22.0 2200 12.5 1250

IFT 1.7 170 0.33 33

DKN 1.7* 170 0.33* 33

Code PEC Ro RE Ro RE Ro RE

t OC ug L-1 Kg h-1 Kg h-1 Kg h-1

CBz 5 2.319 0.004 250 0.0011 949 0.001855 539

CBz 20 2.229 0.00384 260 0.0010 987 0.001783 561

IFT 5 2.529 0.0149 67 0.076636 13

IFT 20 1.566 0.0092 109 0.047455 21

DKN 5 3.458 0.0203 49 0.104788 10

DKN 20 3.386 0.0199 50 0.102606 10

* Assumed toxicity for DKN was the same as for its precursor IFT

PEC = the modelled stationary state concentration in water ("Predicted Environmental Concentration")

Risk ratio Ro = PEC / PNEC (for emission to the first waste basin Eo = 1 Kg h -1)

Risk emission RE = 1 / R (Kg h-1 to the first waste basin)

AlgaeDiketonitrile

RE determination Daphnia Fish

Toxic level -->Cmpound

ChlorobenzeneIsoxaflutole

Values of ecotoxicity Daphnia Fish Algae

IFT

CBz

Cl

DKN

ON

H 3 CSO

2

CF 3

COCH C

C

Conclusions: Guideline values for highest allowable discharges to the waste stream GE = lowest RE value divided by the safety factor (10) for each waste compound:

GE for CBz 25 GE for IFT(stable metabolite DNK incl.) 1 kg h-1

BIBI BIoaccumulation via Benthic Invertebrates

*CON(i) = (K1(i)XCON(2)+KD(i)xCON(i-1))/(K2(i)+KE(i)+KG(i)+KM)

BENTHIC INVERTEBRATESCON(3) = SWR x COC x LFR(3)

(3)

FIRST CONSUMER (4)

CON(4) = * (i=4)

SECOND CONSUMER (5)

CON(5) = * (i=5)

WATERCON(2) = SWR x COC / Kow

(2)

SEDIMENTCOC = CON(1) / OCFR

(1)

K2,KE,KG,KM

K1(5)

K1(4)

SWR

SWR KD(4)

KD(5)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.1

0

5

10

15

20

25

30

35

40

45

mg/cm3

Fate of TeCP in SWF soils; model SOIL/CemoSpH 5.5OC =0.05

pH 6.0OC =0.05

pH 6.5OC = 0.05 pH 6.0

OC = 0.01 pH 6.5OC =0.01

Concentration profiles 20 years after initial contamination of 10 cm

topsoil layer 100 mg / cm 3

Depth m

0 0.5 1 1.5 2 2.5 3 3.5 4 4.52

3

4

5

6

7

8

9

10

11Pentachlorophenol in worms (Aporrectoea) of South-West Finland soil originally contaminated by 100 ug/g dw

CWURM ug/g ww

Time years with no further contamination

Why you dont like rak,fresh Norrland worms?

VPLEST, WATSOLU, TDLKOW

and LEVEL3+ predicted that they

contain too much Malathione !