Supporting Information

S1. Emission Model Formulations and Input Data

Schematization

For the spatially distributed modelling of fate and transport, we use the schematization of Europe provided by the E-Hype hydrology model. This schematization consists of some 35,000 sub-catchments (SC), irregularly shaped parts of river catchments, which form the smallest calculation unit for the hydrology model. The average surface area of the SCs is 247 km2. The average spatial resolution is therefore approximately √ (247 ) ≈16 km. For emission calculations, every SC is allocated to the country with the largest overlap with the SC area.

Domains

The emissions are defined for the pan-European domain, consisting of 42 countries (for REACH registered chemicals) or for country domains (for pharmaceuticals and pesticides), and downscaled (“disaggregated”) to the SC level, distributed over time and relocated.

Locators

For the downscaling, we use “locator” variables (Y), spatially variable quantities that explain emissions or that are correlated to emissions. We determine the value of Y for all sub-catchments YSC. These values are simply added for all SC’s in a domain to obtain the total YTot. The emissions per

SC ESC (M.T-1) are then calculated from the total emissions per domain ETot (M.T-1): ESC=ETot ×Y SC

Y Tot.

We use the following locators:

For REACH registered chemicals and pharmaceuticals: weighed population count per SC (population data obtained from LandScan (2006)™ High Resolution global Population Data Set) 1, weight factor is discussed below);

land used for agriculture (following land use definition from hydrology model).

For REACH-registered chemicals and pharmaceuticals, we assumed that a higher standard of living implies a higher use of chemicals. We used the World Bank per capita gross domestic product based on purchasing-power-parity (GDP-PPP or GDP) to quantify standard of living (https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD). In addition, we accounted for a relative reduction of the losses with increasing standard of living due to increasing environmental awareness. To quantify this effect, we used effort towards management of domestic wastewater as a model factor. We quantified the share of wastewater collected, the share of wastewater treated and the share of wastewater treated with secondary and tertiary processes as a function of the GDP-PPP on a country-by-country basis (Table 1.2). The result is shown in Figure 1.1(a).

1 While projecting the population data on the E-Hype grid, we identified missing values due to interpolation problems in about 100 cases. We replaced these missing values by using the country population density multiplied with the surface area. Values of 0 have been replace by 1 to avoid division by zero problems.

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(a) (b)Figure 1.1: Country-by-country indicators for wastewater management and treatment (a) and GDP dependent population weight factor used in spatial distribution of emissions (b).

Extrapolation of country data:

For pharmaceuticals and pesticides, the locator was also used to extrapolate use data for substances and countries without data. For pharmaceuticals, we neglected very low sales data in the UK or Sweden (< 0.15 mg/cap/year), which are unlikely to be representative for substances found in detectable quantities in surface water outside the UK and Sweden.

Receptors

Emission estimates per domain and per substance discriminated 5 receptors:

1. lower atmosphere layer 2

2. surface water (sw)3. waste water (ww)4. soil (ss)5. production losses3

The resulting emission for receptor i are denoted as ESC,i (M.T-1).

Impermeable surfaces pathway

A part of the emissions to soil is taking place on impermeable surfaces. We assume this proportional to the fraction of paved surfaces fpaved in the hydrology model schematisation. Impermeable surfaces aggregate emissions, until they are degraded or washed off by a runoff event. k (T-1) equals a removal rate due to biodegradation, photolysis, etc., currently equal to 0 (no removal). The fraction to runoff frunoff is defined as follows. If the daily precipitation is below a set lower threshold (2 mm/day), frunoff = 0. If the daily precipitation is above a set upper threshold (5 mm/day), frunoff = 1. In between the two thresholds, frunoff is determined by linear interpolation. This results in a time dependent washed off fraction controlled by previous build-up of pollutants and rain events. This

2 Not yet implemented.3 Not yet implemented.

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release is allocated to surface water in case of separated sewer systems, and to collected waste water in case of combined sewer systems.

Unice et al. (2019) discuss how the linear wash-off formula used here relates to commonly used empirical exponential wash-off approaches used for storm water modelling. They conclude that the approach is comparable to the use of w wash-off coefficient of 0.55 mm-1 , which is at the high end of the range encountered in such models.

Wastewater pathway

Emissions to waste water will either be collected and treated for population connected to sewer systems or be released to the environment. Per SC, the share of untreated, primary treated, secondary treated and “other” treatment (more than secondary) is specified. Per substance we use a characterisation of the treatment. In particular:

fSTPAir fraction of substance in influent emitted to air fSTPEff fraction of substance in influent emitted via effluent fSTPSld fraction of substance in influent emitted via sludge fSTPRem fraction of substance in influent removed

The present implementation is based on a simulated representation of the activated sludge treatment, as available in the SimpleTreat model (Struijs, 2014). It is assumed that the treatment process proceeds at a constant and homogeneous operating temperature, which is by good approximation independent of the ambient temperature. For this reason, the above fractions are assumed independent of space and time. The fraction emitted via effluents is discharged to the surface water. The fraction emitted to air is allocated to the lower atmosphere layers. The fraction emitted to sludge is reduced with the fraction of sludge incinerated and stored in landfills and allocated to soil. Waste water not collected via sewer systems is distributed over surface water (fraction HSC) and soil (fraction 1-HSC), without treatment.

Disaggregation in time

For pesticides used in agriculture the emissions are not constant in time. A feature has been introduced to distribute these emissions over time in a consistent, mass-conserving way. This is based on clusters of landuse and crops combinations defined in the underlying hydrology model:

agriculture areas with seasonal autumn-sown crops; agriculture areas with seasonal spring-sown crops; agriculture areas with perennial crops.

For the seasonal crops we define two control periods of three months (spring -summer for spring-sown crops; autumn-winter for winter-sown crops), for perennial crops 4 control periods. Within these control periods, a period of one week of actual application is randomly selected for each SC. The total emission is controlled to be consistent. The result of this is expressed as a time and space dependent application factor FA.

Mass Balances equations

Parameters used:

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fsew Fraction of population with sewer connectionfreuse Fraction of sewage sludge re-used in agriculturefuntr Fraction of collected wastewater not treatedfprim Fraction of collected wastewater primary treatedfsec Fraction of collected wastewater secondary treatedfother Fraction of collected wastewater treated more than secondaryH Fraction of wastewater from unsewered areas discharged to surface waterfcomsew Fraction of combined sewer systems

Equations:

direct emissions to sw (pesticides) dSW2WAT = E sw FAwastewater sewered dWW2STP= Eww f sewwastewater unsewered to sw dWW2WAT= Eww (1−f sew ) Hwastewater unsewered to soils dWW2SOL = Eww (1−f sew ) (1−H )emissions to paved areas (REACH) dSS2PAV = E ss f paveddirect emissions to soil (REACH) dSS2SOL = E ss (1− f paved )direct emissions to soil (pesticides) dSS2SOL = E ss FAremoved from paved areas dPAV2REM = k paved M pavedRunoff from paved areas

runoff = (M paved

∆ tf runoff – dPAV2REM)

runoff separated sewers dPAV2WAT = runoff (1−f comsew )runoff mixed sewers dPAV2STP = runoff f comsewinfluent to WWTPs influent = dWW2STP + dPAV2STemissions to air from WWTPs dSTP2LAT = influent ( f sec+f other) fSTPairremoved at WWTPs dSTP2REM = influent ( f sec+f other) fSTPrem+ influent

( f prim+ f sec+ f other) fSTPsld (1−f reuse )sludge deposited on land dSTP2SOL = influent ( f prim+ f sec+ f other ) fSTPsld f reuseeffluents to sw dSTP2WAT= influent ( f untr ) + influent ( f prim ) (1−fSTP sld) + influent

( f sec+f other ) fSTPeff

Mass balances for paved areas:dM paved

dt = dSS2PAV - dPAV2REM - dPAV2WAT - dPAV2STP

Mass balances for waste water:dMwastewater

dt = dWW2STP + dPAV2STP - dSTP2LAT - dSTP2REM - dSTP2SOL - dSTP2WAT

Losses to soil = dWW2SOL + dSS2SOL + dSTP2SOL

Losses to surface water = dSW2WAT + dWW2WAT + dSTP2WAT + dPAV2WAT

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Spatial input data

Table 1.1 lists the spatial input data, their resolution and the data sources used, while Table 1.2 lists the spatial input data at the country level.

Table 1.1: Overview of spatial data used

Item Resolution SourcePopulation 30" x 30" LandScan (2006)™ High Resolution global Population Data

SetGross domestic product based on purchasing-power-parity (GDP-PPP)

Country https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD

Weight factor (WF) to represent environmental management practices

Country Derived from other spatial data (Figure 1.1)

Agriculture land use ≈ 16 km (av.) E-Hype input defined on E-Hype sub-catchments (Hundecha et al., 2016)

Fraction of paved area ≈ 16 km (av.) E-Hype input (Hundecha et al., 2016)Cultivation type (spring, autumn, perennial crops)

≈ 16 km (av.) E-Hype input (Hundecha et al., 2016)

Population connected to sewer systems

Country http://ec.europa.eu/eurostat/data/database; Waterbase – UWWTD, 2015; ICPDR,2015

Treated share of collected wastewater

Country http://ec.europa.eu/eurostat/data/database; Waterbase – UWWTD, 2015; ICPDR,2015

Treatment level of collected wastewater

Country http://ec.europa.eu/eurostat/data/database; Waterbase – UWWTD, 2015; ICPDR,2015

Share of uncollected wastewater reaching surface waters

Country van den Roovaart et al., 2013, and references therein

Share of sewage sludge re-used Country Waterbase – UWWTD, 2015Share of separated sewer systems Europe homogeneous estimate of 25% (no European-wide data)

Table 1.2: Input data at country level for emission model.

Country GDP-PPP

(k$/cap/y)

WF fsew freuse funtr fprim fsec fother H

1 Albania 11.9 10.2 78% 80% 60% 30% 10% 0% 1%2 Andorra 40.0 29.8 100% 80% 0% 0% 41% 59% 1%3 Austria 50.1 34.8 100% 44% 0% 0% 0% 100% 1%4 Belarus 18.1 15.9 30% 80% 60% 30% 10% 0% 2%5 Belgium 46.4 32.8 100% 44% 0% 0% 37% 63% 1%6 Bosnia Herc. 12.1 11.0 43% 80% 66% 0% 34% 0% 2%7 Bulgaria 19.2 16.6 80% 70% 0% 3% 97% 0% 1%8 Croatia 23.6 19.6 66% 80% 25% 6% 63% 5% 2%9 Cyprus 32.6 25.4 70% 100% 3% 0% 20% 77% 0%10 Czech Rep. 34.7 26.6 92% 80% 0% 0% 87% 13% 1%11 Denmark 49.7 34.3 100% 66% 0% 0% 0% 100% 2%12 Estonia 29.4 23.6 96% 92% 0% 0% 0% 100% 5%13 Finland 43.1 31.3 100% 98% 0% 0% 0% 100% 16%14 France 41.5 30.3 100% 72% 0% 0% 41% 59% 1%15 Germany 48.7 33.8 98% 80% 0% 0% 1% 99% 1%16 Greece 26.8 21.6 84% 26% 3% 0% 0% 97% 1%17 Hungary 26.7 21.6 100% 82% 0% 1% 26% 73% 3%18 Iceland 51.4 35.3 91% 80% 2% 7% 11% 80% 9%19 Ireland 68.9 43.8 100% 100% 5% 0% 73% 22% 17%

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Country GDP-PPP

(k$/cap/y)

WF fsew freuse funtr fprim fsec fother H

20 Italy 38.2 28.7 94% 48% 1% 0% 27% 72% 1%21 Kosovo 10.1 9.3 10% 80% 60% 30% 10% 0% 2%22 Latvia 26.0 21.6 88% 82% 0% 0% 85% 15% 4%23 Lithuania 30.0 23.6 90% 56% 0% 0% 4% 96% 3%24 Luxemb. 105.9 59.8 99% 87% 0% 0% 19% 81% 0%25 Macedonia 15.1 13.5 7% 80% 60% 30% 10% 0% 5%26 Malta 37.9 28.2 100% 25% 0% 0% 100% 0% 2%27 Moldova 5.3 4.8 26% 80% 12% 45% 43% 0% 2%28 Montenegro 16.9 14.3 61% 80% 90% 2% 8% 0% 2%29 Netherlands 50.9 34.8 100% 1% 0% 0% 0% 100% 10%30 Norway 59.3 39.3 95% 80% 2% 7% 11% 80% 11%31 Poland 27.8 22.3 62% 80% 0% 3% 13% 84% 2%32 Portugal 30.6 24.2 100% 97% 0% 12% 39% 49% 1%33 Romania 23.6 19.6 60% 26% 15% 3% 43% 39% 3%34 Russia 23.2 19.6 40% 80% 60% 30% 10% 0% 2%35 Serbia 14.5 12.7 71% 80% 81% 3% 14% 2% 2%36 Slovakia 30.6 24.2 95% 90% 1% 0% 38% 61% 1%37 Slovenia 32.9 25.4 86% 18% 15% 0% 60% 25% 1%38 Spain 36.3 27.7 99% 75% 1% 1% 30% 68% 1%39 Sweden 49.2 34.3 100% 91% 0% 0% 0% 100% 15%40 Switzerland 62.9 40.8 98% 10% 0% 0% 4% 96% 2%41 Ukraine 8.3 7.6 38% 80% 4% 7% 90% 0% 2%42 UK 42.6 30.8 99% 80% 0% 0% 94% 6% 3%

For the share of separated sewer systems, we used a homogeneous estimate of 25%. We have not been able to access European-wide data to verify this estimate or to apply a country-by-country variability.

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S2. Fate and transport model formulations and input data

General

For chemical fate and transport we use a dynamic mass balance model that calculates contaminant concentrations in a spatially and temporally resolved way. The model has been coupled with the Delft3D-WAQ open source modelling framework (http://oss.deltares.nl/) and is called STREAM-EU (Spatially and Temporally Resolved Exposure Assessment Model for European basins; Lindim et al., 2016; 2017).

STREAM-EU distinguishes environmental compartments for surface water, sediment, soil/groundwater, air and snow/ice, with their own mass balance. An environmental compartment is composed of four different phases (water, inert solid, particulate organic carbon (POC) and dissolved organic carbon (DOC)). A compartment is considered well-mixed and homogeneous with respect to the phases and contaminant distribution. The contaminant can be present in any of the phases, except the inert solid phase. The distribution between compartments and phases is expressed by the fugacity concept (Mackay, 2001). Processes for precipitation, dissolution and ionization of the simulated substances have been implemented. Partitioning between sediments and water for ionizing species is estimated using the distribution ratio instead of the octanol-water partition coefficient. Within all compartments the contaminants undergo degradation. Within compartments with an atmosphere interface, surface-to-air vapour-phase transport is included by applying the two-film theory (Mackay et al., 2001). It is assumed that the ionized forms do not volatilize from solution. Within all compartments representing surface waters, a loss term for the particulate phase representing storage in aquatic sediments is defined. Advective carrier fluxes of water and particles are defined between the compartments and carry the chemical through the model domain.

Mass balance equation

This section is identical to previously published formulations copied from Lindim et al. (2016; 2017), supplemented or modified as appropriate.

The mass balance equation for compartment a reads:

d (V a Zbulka(T ) f a(T ))

dt=Ea + ∑

b ∈ Ja

(Dba(T ) f b(T )) − ∑b ∈ Ja

Dab(T ) f a(T )−ka(T ) V a Zbulka(T ) f a(T )

where V is the compartment volume (m3), Z is the fugacity capacity (mol m-3Pa-1), f is the fugacity (Pa), T is the absolute temperature (K), t is time (s), E are the emissions (mol s-1), D is a transport variable (mol Pa-1 s-1) and k is the reaction rate of the compound (s-1). All quantities are time dependent. The second and third terms on the right-hand side represent advective terms to and from compartments b sharing a contact surface with compartment a (b∈J a). The contaminant concentration C (mol m-3) for compartment a is given by:Ca(t) =Za(T).fa(t)

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The fugacity capacities need to be estimated in order to calculate the concentrations. They depend not only on the temperature but also on the nature of the phases in the compartment. The different fugacity capacities are given by Mackay (2001):

Zair (T )= 1R T air

; Zwater (T )= 1R T water Kaw (T )

= 1H

; ZPOC i(T )=Zwater (T )K POC

i(T )

;

ZDOCi(T )=Zwater(T ) KDOC i

(T )

for air, water, particulate organic carbon (POC) and dissolved organic carbon (DOC) respectively, where R is the gas constant (8.314 Pa m3 mol-1 K-1), H is the Henry´s law constant (Pa m3 mol-1), Kaw is the dimensionless partition coefficient between air and water, KPOCi is the dimensionless partition coefficient between POC and water in a compartment of type i and KDOCi is the dimensionless partition coefficient between DOC and water in a compartment of type i. POC and DOC partition coefficients are compartment specific and consequently so are the fugacity capacities. KPOC and KDOC are estimated as:

K POC=x1φoc Kow∧K DOC=x2φoc Kow

where Kow is the octanol-water partition coefficient, φoc is the dimensionless average organic carbon content, x1 and x2 are empirical sorptive capacities of organic carbon. x1 and x2

depend on the compound and on the organic carbon type and lie in the range [0.14-0.9] (Mackay, 2001). x1 and x2 in the model are compartment specific.

Bulk fugacity capacities for each compartment are constructed to be used in the mass balance equation with the contributions of all the phases j present in that compartment. For a generic compartment a:

Zbulka=∑

jZ j Vf j

where Vf is the volume fraction of the phase.

Temperature corrections for the partition coefficients, K(T), use the Van´t Hoff relationship:

K (T )=K (T0) . exp (− ΔHR

( 1T 1

− 1T 0

))

where T0 is the absolute reference temperature and ΔH (J mol-1) is the enthalpy of phase change. Temperature corrections for the reaction rate, k(T), follow the exponential dependency:

k (T )=k (T 0 ) ϑ(T−T o)

with θ = 1.08.

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Two adjacent compartments sharing a contact area between them, can exchange mass. This includes advective contaminant mass transfer with water fluxes and sediment fluxes. The transport variable D in the mass balance for the advective mass transfer is given by:

Du❑

→d=Q

u❑→

d∑

j(Z j (T ) .

Vf j

V fcarrier)u

where u and d are adjacent upstream and downstream compartments, Q the volumetric carrier flux (m3 s-1), Vfcarrier the volume fraction of the carrier and j is a phase.

Surface-to-air vapour-phase transport (volatilization) is modelled using the two-resistance mass transfer coefficient approach (Mackay, 2001), for substances with a vapour pressure exceeding 10-8 (Pa). The diffusive contributions for the water-air interface (de), the water film (dw) and air film (da) are calculated using empirical relationships from Mackay and Yeun (1983):

da=36w s (6.1+0.63ws )0.5; de=0.065da; dw=1.7510−4da

where ws is wind speed (m s-1). The associated transport variable D is calculated as follows:

D=A ( 1de Za

+ 1dw Zw )

−1

where A is the cell surface and Za and Zw are the air and water fugacity capacity.

Ionizing substances

Ionization in freshwater is implemented using the Henderson-Hasselbach relationship to compute the dissociated and non-dissociated fractions at each time step and in each cell. For monoprotic substances:

f ndiss=(1+10 ( pH− pKa1) )−1 for monoprotic acids

f ndiss=(1+10 ( pKa1− pH ) )−1 for monoprotic bases

f diss=1−f ndiss

where fndiss is the non-dissociated fraction and fdiss is the dissociated fraction. For diprotic substances:

f ndiss=(1+10 ( pH− pKa1)+10(2pH− pKa1−pKa2 ) )−1 for diprotic acids

f diss1=(1+10( pKa1− pH )+10( pH− pKa2 ) )−1 for diprotic acids

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f ndiss=(1+10 ( pKa1− pH )+10( pKa1+ pKa2−2 pH ) )−1 for diprotic bases

f diss1=(1+10( pH−pKa1)+10( pKa2−pH ) )−1 for diprotic bases

f diss2=1−f ndiss−f diss1

where fdiss1 and fdiss2 are mono-ionized and bi-ionized fractions respectively. For amphoteric species:

f ndiss=(1+10 (pH−pKa1' )+10 (pKa1− pH ) )−1

fdiss+¿=f ndiss(1+10 ( pKa 1− pH ))−1¿

fdiss−¿=f ndiss (1+10( pH− pKa1

' ) )−1¿

1=f diss+¿+f ndiss +f diss−¿ ¿¿

where pKa1’ is for the base form and pKa1 is for the acid form, and fdiss+ and fdiss- are the cationic and anionic fractions respectively. It is assumed that ionised species do not volatilize from solution. OC-water partitioning for ionizing substances is calculated using the distribution ratio (DR) instead of Kow:

DR= f ndiss K ow

Precipitation and dissolution

Precipitation and dissolution are evaluated at each time step and in each cell using the substances aqueous solubility and the local concentrations in the aqueous and solid states. The Van’t Hoff equation was used to represent temperature dependency on the water solubility.

Numerical solution

Delft3D-WAQ discretizes the advection-diffusion equation by a time-implicit first order finite volumes method (Deltares, 2016). The resulting set of equations is solved by the Generalised Minimal Residual method (Saad and Schultz, 1986).

Model compartments and relevant carrier fluxes

Table 2.3: Fate and transport model compartments

Compartment name Abbr. Compartment name Abbr.Glacier GLA Local streams STRSnow cover SNO Main river, from upstream RIVTop layer of soil and groundwater S1 Lake within local streams ILKMiddle layer of soil and groundwater S2 Lake within main river OLKBottom layer of soil and groundwater S3 Irrigation canals IRR

Table 2.4: Carrier fluxes defined in the fate and transport model

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Flux description From ToWater fluxes between different SCsOutflow to downstream sub-catchment OLK other SCDiversion outflow OLK other SCIrrigation related transfer from elsewhere other SC IRRWater fluxes within one horizontal unitPrecipitation outside domain STR, RIV, ILK,OLK,GLASnowfall on land outside domain SNOSurface runoff outside domain STRInfiltration outside domain S1Macroporeflow outside domain S1, S2, S3Snowmelt (infiltration) SNO S1Snowmelt (surface runoff) SNO STRSnowmelt (macropore flow) SNO S1, S2, S3Glacier melt (infiltration) GLA S1Glacier melt (surface runoff) GLA STRGlacier melt (macropore flow) GLA S1, S2, S3Glacier growth SNO GLAPercolation S1 S2Percolation S2 S3Upward groundwater flow S3 S2Upward groundwater flow S2 S1Surface runoff S1 STRGroundwater runoff S1, S2, S3 STRTile drainage S1, S2, S3 STRFlow STR ILKFlow STR RIVFlow ILK RIVFlow RIV OLKIrrigation IRR S1, S2Local withdrawal for irrigation OLK, ILK, RIV IRRPoint sources outside domain RIVRural diffuse sources outside domain STR, S1, S2, S3Sediment fluxes within one SCErosion S1 STRNet settling STR, RIV, ILK, OLK outside domainOther fluxesVolatilisation S1, STR, RIV, ILK, OLK outside domain

Particle and OC forcing

Model forcing data consisting of erosion fluxes and the net settling fluxes of solids, as well as the concentrations of solids, POC and DOC in all compartments have been derived from a separate dynamic mass balance simulation with Delft3D-WAQ. Substance properties input is discussed in S3.

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S3. Model parameters, substance properties models used and data gap handling

Software used includes the CATALOGIC suite (Dimitrov et al., 2011), QSAR Toolbox (Dimitrov et al., 2016), ChemProp (Schüürmann et al. 2011), ACD/Percepta (ACD/Labs 2015) and EPI Suite (US EPA, 2012).

Table 3.5 provides a complete account of the fate and transport model substance properties used in the integrated modelling exercises.

Table 3.5:Complete account of fate and transport model substance properties used in integrated modelling.

Property unit value adopted Explanation

Molar mass (g/mol) calculated value

default reaction enthalpy (J/mol) 30000 replaces all undefined enthalpies

boiling point (C) model predicted

melting point (C) model predicted

vapour pressure (Pa) model predicted

water solubility (mol/m3) model predicted

ref.temp.(water solubility k) (C) 25 follows from model assumptions

enthalpy (water solubility) (J/mol) Undefined

- log10 acid dissociation constant 1 (-) model predicted

ref.temp.(acid diss. constant 1) (C) 20 follows from model assumptions

enthalpy (acid diss. 1) (J/mol) Undefined

- log10acid dissociation constant 2 (-) model predicted

ref.temp.(acid diss. constant 2) (C) 20 follows from model assumptions

enthalpy (acid diss. 2) (J/mol) Undefined

- log10base dissociation constant 1 (-) model predicted

ref.temp.(base diss. constant 1) (C) 20 follows from model assumptions

enthalpy (base diss. 1) (J/mol) Undefined

- log10base dissociation constant 2 (-) model predicted

ref.temp.(base diss. constant 2) (C) 20 follows from model assumptions

enthalpy (base diss. 2) (J/mol) Undefined

acid catalysed hydrolysis rate constant (1/s) model predicted

ref.temp. (acid catalysed hydrolysis k) (C) 25 follows from model assumptions

enthalpy (acid catalysed hydrolysis) (J/mol) Undefined

base catalysed hydrolysis rate constant (1/s) model predicted

ref.temp. (base catalysed hydrolysis k) (C) 25 follows from model assumptions

enthalpy (base catalysed hydrolysis) (J/mol) Undefined

neutral hydrolysis rate constant (1/s) model predicted

ref.temp. (neutral hydrolysis k) (C) 25 follows from model assumptions

enthalpy (neutral hydrolysis) (J/mol) Undefined

direct photolysis rate at surface (1/s) 0 neglected due to lack of data

ref.temp. (photolysis k) (C) 25 neglected due to lack of data

enthalpy (photolysis) (J/mol) 0 neglected due to lack of data

microbial degradation in sediment (1/s) model predicted

ref.temp. (mic. deg. sediment k) (C) 25 follows from model assumptions

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Property unit value adopted Explanation

microbial degradation in soil (1/s) model predicted

ref.temp. (mic. deg. soil k) (C) 25 follows from model assumptions

microbial degradation in water (1/s) model predicted

ref.temp. (mic. deg. water k) (C) 25 follows from model assumptions

Log10 Part coeff octanol water at ref T (-) model predicted

Log10 Part coeff air water at ref T (-) model predicted

Log10 Part coeff octanol air at ref T (-) internally calculated, see note below table

enthalpy of phase change o-w (J/mol) internally calculated, see note below table

enthalpy of phase change a-w (J/mol) model predicted

enthalpy of phase change o-a (J/mol) internally calculated, see note below table

Karickhoff par. POC water comp. (-) 0.41 Karickhoff (1981); Mackay (2001)

Karickhoff par. POC soil and groundw comp

(-) 0.0082 Karickhoff (1981); Mackay (2001)

Karickhoff par. POC aquatic sediments (-) 0.016 Karickhoff (1981); Mackay (2001)

Karickhoff par. DOC water comp. (-) 0.41 Karickhoff (1981); Mackay (2001)

Karickhoff par. DOC soil and groundw comp

(-) 0.41 Karickhoff (1981); Mackay (2001)

Karickhoff par. DOC aquatic sediments (-) 0.41 Karickhoff (1981); Mackay (2001)

Notes

The logarithm of the air octanol partition coefficient (koa0 (-)) is calculated as kow0 - kaw0, the difference between the logarithms of the octanol to water and air to water partition coefficients.

Only the enthalpy of phase change air-water is available from modelling. The other two phase change enthalpies are estimated (Mackay, 2001):

o enthalpy of phase change octanol-water = 0;o enthalpy of phase change octanol-air = - enthalpy of phase change air-water.

Various authors report that model predicted half-lives can only be used in fate modelling with significant uncertainty (Greskowiak et al., 2017). As there is no alternative to the use of model-predicted substance degradability in this study, we considered two alternative models providing results for a large range of substances: (a) a semi-quantitative read-across model to estimate the overall degradation half-lives in air, water, soil, and sediment including temperature dependence by Kühne et al. (2007), and (b) the CATALOGIC 301C biodegradation model that predicts percentage biodegradation under OECD 301C test conditions (Dimitrov et al., 2011). The Joint Danube Survey 3 field data allowed us to conclude that the Kühne et al. estimated degradation rates were overestimating field values, while this was not the case for the CATALOGIC 301C estimated biodegradation rates. Therefore, current results are based on the CATALOGIC 301C biodegradation model.

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273

S4. Impact of values below LoD/LoQ on average concentrations

For the assessment of overall model performance, we tested how many values unaffected by LoD/LoQ (“un-flagged” values) are necessary to still approach the average value with a reasonable accuracy (within a factor of two). We did that by studying the results for selected chemicals from different substance groups with no or as few as possible flagged values. By applying hypothetical limit values that replace all values below the limit, by counting the number of replacements and by recalculating the average using the limit value instead of the replaced value, we established a relation between the percentage of un-flagged values and the error in the calculated mean value. Examples are shown for the SCARCE dataset in Figure 4.2.

If all analyses are unaffected by LoD/LoQ (share of unflagged values 100%), the real average is calculated. In the SCARCE example shown in Figure 4.2, the average is not affected a lot, until the fraction of unflagged values reaches values below 0.2. The first two substances in Figure 4.2, tris(butoxyethyl) phosphate and gemfibrozil, have 100% and 96% of unaffected values respectively. For those two substances, the approach outlined above is consistent. For the other four substances shown, all pesticides, however, there are always values affected by LoD/LoQ. The share of unaffected values is 73% for chlorpyriphos, 68% for diazinon, 45% for terbutylazine and 42% for carbendazim respectively. For these pesticides, the validity of the approach is less obvious. The results however, are robust, so we presume the conclusion valid over the whole range of substances.

In the cases shown in Figure 4.2 from the Spanish Basins Case Study, the fraction of un-flagged analyses sufficient to estimate the mean value with an error less than a factor of 2 equals 7%, 7%, 9%, 9%, 13% and 17% respectively (mean value 10%). For the RIWA dataset from the Rhine Case Study, we obtained values of 1%, 2%, 3%, 5% and 8% (mean value of 4%) for the chemicals lindane, carbendazim, carbamazepine, diclofenac and acesulfame respectively. For the JDS3 dataset from the Danube Case Study, we obtained values of 2%, 8% and 9% (mean value of 6%) for the chemicals tris(2-chloroethyl)phosphate (TCEP), terbutylazine and tramadol respectively.

Based on these results we chose to use data for all chemicals with a share of un-flagged values larger than 10%.

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275276277278279280281282

283284285286287288289290291292

293294295296297298299

300301

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0% 50% 100%

aver

age

conc

. (μg

/L)

share of un-flagged values

GEMFIBROZIL

Blue dashed lines represent the real average value of all analyses results (obtained with 100% un-flagged values). Green dashed lines represent two times that value.

Figure 4.2: Relation between the fraction of unflagged values and the apparent mean concentration.

15

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305306

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309

S5. Overview of error per chemical and per Case Study Area

Model error is defined as the logarithm of the quotient of the simulated average concentration and the observed average concentration. Substances groups: (1) pesticides, (2) pharmaceuticals, (3) REACH chemicals (non-volatile), (4) REACH chemicals (volatile, with boiling point > 431K). Substances in group (4) have been omitted from the results included in the main text, and the validity of the model has been limited to substances with a boiling point exceeding 431K.

CAS Name Group JDS3 SCARCE

RIWA WaR Vege

67747-09-5 Prochloraz 1 -2.035554-44-0 Imazalil 1 -1.4120923-37-7 Amidosulfuron 1 -1.323103-98-2 Pirimicarb 1 -1.2330-55-2 Linuron 1 -1.058-89-9 Lindane 1 -1.060207-90-1 Propiconazole 1 -0.8 -1.1131860-33-8 Azoxystrobin 1 -0.71698-60-8 Chloridazon 1 -0.5 -0.810605-21-7 Carbendazim 1 -0.8 -0.6 -0.1148-79-8 Thiabendazole 1 -0.5333-41-5 Diazinon 1 -0.394361-06-5 Cyproconazole 1 -0.3470-90-6 Chlorfenvinphos 1 -0.251218-45-2 Metolachlor 1 -0.4 -0.1 0.2138261-41-3 Imidacloprid 1 0.7 -0.769377-81-7 Fluroxypyr 1 0.03060-89-7 Metobromuron 1 0.01746-81-2 Monolinuron 1 0.1330-54-1 Diuron 1 0.0 0.5 -0.1 -0.1 0.060-51-5 Dimethoate 1 0.3 0.1107534-96-3 Tebuconazole 1 0.2886-50-0 Terbutryn 1 -0.1 0.6139-40-2 Propazine 1 0.219937-59-8 Metoxuron 1 0.315545-48-9 Chlorotoluron 1 0.7 -0.3 0.418691-97-9 Methabenzthiazuron 1 0.31563-66-2 Carbofuran 1 0.425057-89-0 Bentazone 1 0.8 0.126225-79-6 Ethofumesate 1 0.541394-05-2 Metamitron 1 1.0 0.05915-41-3 Terbuthylazine 1 1.1 -0.4 0.6 1.0563-12-2 Ethion 1 0.623950-58-5 Propyzamide 1 0.6122-34-9 Simazine 1 1.1 0.283164-33-4 Diflufenican 1 0.71702-17-6 Clopyralid 1 0.887674-68-8 Dimethenamid 1 0.893-65-2 Mecoprop 1 0.8101205-02-1 Cycloxydim 1 0.8111988-49-9 Thiacloprid 1 0.967129-08-2 Metazachlor 1 1.3 1.0 0.82921-88-2 Chlorpyrifos 1 1.11912-24-9 Atrazine 1 1.1 1.7 0.4

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311312313314315

CAS Name Group JDS3 SCARCE

RIWA WaR Vege

7287-19-6 Prometryn 1 1.61071-83-6 Glyphosate 1 1.9 1.894-74-6 MCPA 1 2.7 1.9 1.434123-59-6 Isoproturon 1 2.7 2.6 1.9 2.2 1.294-75-7 2,4-D 1 2.876-73-3 Secobarbital 2 -3.71622-61-3 Klonazepam 2 -3.020830-75-5 Digoxin 2 -3.03380-34-5 Triclosan 2 -2.5146-22-5 Nitrazepam 2 -2.4846-49-1 Lorazepam 2 -2.7 -2.158-73-1 Diphenhydramine 2 -2.425451-15-4 Felbamat 2 -2.158-25-3 Chlordiazepoxid 2 -2.1106266-06-2 Risperidon 2 -1.825812-30-0 Gemfibrozil 2 -1.750-02-2 Dexametason 2 -1.778649-41-9 Jomeprol 2 -1.773334-07-3 Iopromid 2 -0.7 -1.8 -2.350-28-2 Estradiol 2 -2.2 -1.066722-44-9 Bisoprolol 2 -1.5604-75-1 Oxazepam 2 -1.2 -1.2 -1.6137-58-6 Lidokain 2 -1.33930-20-9 Sotalol 2 -1.1 -1.458-93-5 Hydroklortiazid 2 -0.9 -1.4 -1.2137862-53-4 Valsartan 2 -0.6 -1.4139481-59-7 Kandesartan 2 -0.953-86-1 Indometacin 2 -0.966108-95-0 Iohexol 2 -0.8 -0.952-53-9 Verapamil 2 -0.783905-01-5 Azitromycin 2 -0.761869-08-7 Paroxetin 2 -1.3 0.084057-84-1 Lamotrigin 2 -0.4 -0.6 -0.822071-15-4 Ketoprofen 2 -0.650-48-6 Amitriptyline 2 -0.658-08-2 Caffeine 2 -0.7 -0.4 -0.479617-96-2 Sertralin 2 -0.5439-14-5 Diazepam 2 -1.2 0.282419-36-1 Ofloxacin 2 -0.460142-96-3 Gabapentin 2 -0.5 -0.154910-89-3 Fluoxetin 2 -0.2138402-11-6 Irbesartan 2 0.3 -0.6657-24-9 Metformin 2 -0.1 -0.129122-68-7 Atenolol 2 0.2 -0.1 -0.457-27-2 Morfin 2 -0.181103-11-9 Klaritromycin 2 0.7 -0.4 -0.5723-46-6 Sulfametoxazol 2 -0.4 1.4 -0.3 -0.593413-69-5 Venlafaxin 2 0.1 0.5 -0.527203-92-5 Tramadol 2 0.137350-58-6 Metoprolol 2 -0.2 1.2 -0.4 -0.115307-86-5 diclofenac 2 0.6 -0.4298-46-4 Karbamazepin 2 0.0 1.4 0.0 -0.2738-70-5 Trimetoprim 2 0.459729-33-8 Citalopram 2 0.0 0.826787-78-0 Amoxicillin 2 0.454-31-9 Furosemid 2 0.4

17

CAS Name Group JDS3 SCARCE

RIWA WaR Vege

41859-67-0 Bezafibrat 2 1.2 -0.188150-42-9 Amlodipin 2 0.6443-48-1 Metronidazol 2 0.751481-61-9 Cimetidin 2 0.842399-41-7 Diltiazem 2 1.060-80-0 Phenazone 2 1.015687-27-1 Ibuprofen 2 1.0 0.9114-07-8 Erythromycin 2 1.222204-53-1 Naproxen 2 1.2113665-84-2 Klopidogrel 2 1.3525-66-6 Propranolol 2 1.385721-33-1 Ciprofloxacin 2 1.4134523-00-5 Atorvastatin 2 1.8103-90-2 Paracetamol 2 2.5 1.6115-96-8 Tris(2-chloroethyl) phosphate 3 -2.0 -2.6143-24-8 Bis(2-(2-methoxyethoxy)ethyl) ether 3 -1.3791-28-6 Triphenylphosphine oxide 3 -1.2288-13-1 Pyrazole 3 -1.2131-56-6 2,4-dihydroxybenzophenone 3 -1.081-07-2 1,2-benzisothiazol-3(2H)-one 1,1-

dioxide3 -0.9 -0.9

100-97-0 Methenamine 3 -0.453-16-7 Estrone 3 -0.3129-00-0 Pyrene 3 -0.295-14-7 Benzotriazole 3 -0.4 0.1 -0.3 -0.313674-84-5 Tris(2-chloro-1-methylethyl)

phosphate3 -0.2

76-03-9 Trichloroacetic acid 3 -0.194-13-3 Propyl 4-hydroxybenzoate 3 0.0131-57-7 Oxybenzone 3 0.078-51-3 Tris(2-butoxyethyl) phosphate 3 0.3 -0.1120-47-8 Ethyl 4-hydroxybenzoate 3 0.1117-81-7 Bis(2-ethylhexyl) phthalate 3 0.1126-71-6 Triisobutyl phosphate 3 0.213674-87-8 Tris[2-chloro-1-(chloromethyl)ethyl]

phosphate3 0.3

99-76-3 Methyl 4-hydroxybenzoate 3 0.915307-79-6 Sodium [2-[(2,6-

dichlorophenyl)amino]phenyl]acetate

3 1.1 0.9

126-73-8 Tributyl phosphate 3 1.084852-15-3 Phenol, 4-nonyl-, branched 3 1.0115-86-6 Triphenyl phosphate 3 1.1106-48-9 4-chlorophenol 3 1.31241-94-7 2-ethylhexyl diphenyl phosphate 3 1.4120-12-7 Anthracene 3 1.5108-78-1 melamine 3 1.791-20-3 Naphthalene 3 1.9120-18-3 Naphthalene-2-sulphonic acid 3 2.0106-47-8 4-chloroaniline 3 2.580-05-7 4,4'-isopropylidenediphenol 3 2.6 3.5123-91-1 1,4-dioxane 4 -1.2624-92-0 Dimethyl disulphide 4 1.2107-06-2 1,2-dichloroethane 4 1.6127-18-4 Tetrachloroethylene 4 1.667-66-3 Chloroform 4 1.8

18

CAS Name Group JDS3 SCARCE

RIWA WaR Vege

108-88-3 Toluene 4 2.61634-04-4 Tert-butyl methyl ether 4 2.6637-92-3 2-ethoxy-2-methylpropane 4 2.771-43-2 Benzene 4 3.7

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S6. Classification of validated REACH chemicals based on ERCs

CAS Name Potential emission (% of use volume)

115-96-8 Tris(2-chloroethyl) phosphate 50

143-24-8 Bis(2-(2-methoxyethoxy)ethyl) ether 100

791-28-6 Triphenylphosphine oxide 2

288-13-1 Pyrazole Public REACH dossier not found

131-56-6 2,4-dihydroxybenzophenone 100

81-07-2 1,2-benzisothiazol-3(2H)-one 1,1-dioxide 100

100-97-0 Methenamine 100

53-16-7 Estrone 6

129-00-0 Pyrene 6

95-14-7 Benzotriazole 100

13674-84-5 Tris(2-chloro-1-methylethyl) phosphate 100

76-03-9 Trichloroacetic acid 100

94-13-3 Propyl 4-hydroxybenzoate 100

131-57-7 Oxybenzone 100

78-51-3 Tris(2-butoxyethyl) phosphate 100

120-47-8 Ethyl 4-hydroxybenzoate 100

117-81-7 Bis(2-ethylhexyl) phthalate 100

126-71-6 Triisobutyl phosphate 100

13674-87-8 Tris[2-chloro-1-(chloromethyl)ethyl] phosphate 6

99-76-3 Methyl 4-hydroxybenzoate 100

15307-79-6 Sodium [2-[(2,6-dichlorophenyl)amino]phenyl]acetate 6

126-73-8 Tributyl phosphate 100

84852-15-3 Phenol, 4-nonyl-, branched 100

115-86-6 Triphenyl phosphate 100

106-48-9 4-chlorophenol 2

1241-94-7 2-ethylhexyl diphenyl phosphate 100

120-12-7 Anthracene 6

108-78-1 Melamine 100

91-20-3 Naphthalene 100

120-18-3 Naphthalene-2-sulphonic acid 100

106-47-8 4-chloroaniline 6

80-05-7 4,4'-isopropylidenediphenol 100

123-91-1 1,4-dioxane 100

624-92-0 Dimethyl disulphide 6

107-06-2 1,2-dichloroethane 100

127-18-4 Tetrachloroethylene 100

67-66-3 Chloroform 100

108-88-3 Toluene 100

1634-04-4 Tert-butyl methyl ether 100

637-92-3 2-ethoxy-2-methylpropane 6

71-43-2 Benzene 100

20

318

S7. Application of the NORMAN prioritization framework

The ranking of substances within (sets of associated) water bodies is based on their contribution to

the mixture effect. To this end, we use the toxic units metric, defined as TU = Cw/L, where Cw is the

freely dissolved concentration and L is the effect level. Substance ranking requires a definition of the

collection of sites to be considered, and the elimination of the space and time dimensions. For the

latter, we used elements of the NORMAN (substances) prioritization framework (Dulio and von der

Ohe, 2013). Time variability is eliminated by choosing the P99 of the time dependent TU, say TU99.

The spatial aggregation method of the TU99 at all sites first quantifies the “Extent of Exceedance”

(EoE) by determining the spatial P95 of the TU99 per site. The EoE is initially a number between 0 and

infinity. It is converted to a number in the range [0,1] according to the NORMAN method (Table 7.6).

Next, it defines the “Frequency of Exceedance” (FoE) as the fraction of sites considered where TU99

exceeds 1. The FoE is a number in the range [0,1]. A final spatially aggregated score is obtained by

adding EoE and FoE to obtain a number in the range [0,2]. The calculation steps and an example are

collected in Table 7.6. For the effect level L, we used the so-called NORMAN lowest PNECs (NORMAN

PNEC list 2018 v7.xlsx, received from Jaroslav Slobodnik on 24 April 2018)5th percentile of the chronic

NOEC SSD, which is a metric also used for deriving environmental quality standards. For a lognormal

SSD with a median value µ (50% of affected species) and a standard deviation σ = 0.7, L = 0.07057 µ.

The results from the substance prioritization based on predicted environmental concentrations (PEC)

are collected in Table 7.7. To support the discussion in the main text, 5 scores are collected: the

score for the original PEC and 4 scores for 1 and 2 orders of under- and overestimation respectively.

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322

323

324

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326

327

328

329

330

331

332

333

334

335

336

337

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Table 7.6: Summary and example of substances ranking method used

Step Explanation Example (10 sites)Starting point c(x,t) for one chemicalConversion to effect metric TU(x,t) = c(x,t)/LElimination of time dimension TU99(x) = c99(x)/L = temporal P99 per

sitevalues for 10 sites: 0.22, 0.85, 0.35, 0.19, 0.35, 2.61, 0.67, 0.10, 1.53, 2.05

Calculation of EoE Initial EoE = (TU99(x))95 (c99(x))95/L spatial P95 of P99 values per site

2.36

Conversion of EoE to range [0:1] EoE <1 0.010≥ EoE ≥1 0.1100≥ EoE >10 0.21000≥ EoE >100 0.5EoE >1000 1.0

0.1

Calculation of FoE Fraction of sites with TU99(x) > 1 0.3Final score EoE + FoE 0.4

Table 7.7: Tabulated results from substance prioritization.

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

80-05-7 4,4'-isopropylidenediphenol Other 1.072 1.482 1.997 1.999 2.000

13071-79-9 Terbufos Pest 0.51 1.20 1.92 1.96 1.97

34256-82-1 Acetochlor Pest 0.57 1.27 1.83 1.87 1.89

115-32-2 Dicofol Pest 0.60 1.17 1.77 1.82 1.82

60168-88-9 Fenarimol Pest 0.67 1.12 1.63 1.65 1.65

62-53-3 aniline Other 0.340 1.084 1.483 1.996 1.998

131983-72-7 Triticonazole Pest 0.77 1.12 1.46 1.97 1.97

5598-13-0 Chlorpyrifos-Methyl Pest 0.22 0.75 1.43 1.97 1.97

29232-93-7 Pirimiphos-Methyl Pest 0.35 0.88 1.43 1.96 1.97

786-19-6 carbophenothion Pest 0.33 0.86 1.42 1.96 1.97

7786-34-7 Mevinphos Pest 0.39 0.88 1.42 1.96 1.97

52-68-6 Trichlorfon Pest 0.16 0.61 1.38 1.94 1.97

62-73-7 Dichlorvos Pest 0.23 0.70 1.38 1.95 1.97

67129-08-2 Metazachlor Pest 0.28 0.97 1.36 1.89 1.90

563-12-2 Ethion Pest 0.20 0.67 1.35 1.95 1.97

1918-16-7 Propachlor Pest 0.16 0.68 1.33 1.87 1.89

40487-42-1 Pendimethalin Pest 0.18 0.60 1.30 1.96 1.97

94-75-7 2,4-D Pest 0.28 0.74 1.29 1.91 1.95

2642-71-9 Azinphos-ethyl Pest 0.18 0.59 1.29 1.93 1.96

72490-01-8 Fenoxycarb Pest 0.24 0.68 1.27 1.87 1.88

108-95-2 phenol Other 0.032 0.478 1.119 1.487 1.997

101-20-2 triclocarban Other 0.004 0.225 1.044 1.481 1.996

23560-59-0 Heptenophos Pest 0.04 0.41 1.03 1.43 1.96

140-66-9 4-(1,1,3,3-tetramethylbutyl)phenol Other 0.010 0.313 1.022 1.482 1.997

106-47-8 4-chloroaniline Other 0.002 0.229 1.014 1.477 1.996

122-39-4 diphenylamine Other 0.003 0.244 1.010 1.472 1.995

5915-41-3 Terbuthylazine Pest 0.01 0.35 0.99 1.42 1.97

1582-09-8 Trifluralin Pest 0.04 0.50 0.98 1.45 1.97

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341

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

15687-27-1 ibuprofen Phar 0.00 0.28 0.96 1.45 1.99

137-26-8 thiram Pest 0.03 0.35 0.94 1.42 1.97

83164-33-4 Diflufenican Pest 0.00 0.23 0.93 1.35 1.88

34123-59-6 Isoproturon Pest 0.02 0.35 0.92 1.33 1.96

91465-08-6 Lambda-Cyhalothrin Pest 0.02 0.40 0.91 1.45 1.97

111988-49-9 Thiacloprid Pest 0.02 0.44 0.89 1.29 1.80

33693-04-8 Terbumeton Pest 0.00 0.19 0.88 1.35 1.89

298-02-2 Phorate Pest 0.01 0.31 0.86 1.43 1.96

67564-91-4 Fenpropimorph Pest 0.01 0.27 0.84 1.42 1.95

52315-07-8 Cypermethrin | Zeta-cypermethrin Pest 0.02 0.30 0.83 1.40 1.95

86-50-0 Azinphos-methyl Pest 0.00 0.23 0.79 1.41 1.95

1897-45-6 Chlorothalonil Pest 0.01 0.23 0.79 1.36 1.89

2921-88-2 Chlorpyrifos Pest 0.01 0.27 0.78 1.40 1.96

55-38-9 Fenthion Pest 0.01 0.28 0.78 1.42 1.96

2032-65-7 Methiocarb Pest 0.01 0.30 0.78 1.28 1.85

24017-47-8 Triazophos Pest 0.01 0.26 0.78 1.42 1.96

56-38-2 Parathion Pest 0.01 0.25 0.77 1.42 1.95

120-12-7 anthracene Other 0.000 0.167 0.758 1.466 1.993

944-22-9 Fonofos Pest 0.01 0.26 0.76 1.41 1.95

50563-36-5 Dimethachlor Pest 0.00 0.18 0.74 1.34 1.87

333-41-5 Diazinon Pest 0.01 0.28 0.73 1.37 1.94

84852-15-3 Phenol, 4-nonyl-, branched Other 0.002 0.158 0.720 1.465 1.993

52918-63-5 Deltamethrin Pest 0.00 0.18 0.69 1.35 1.95

88-85-7 dinoseb Other 0.001 0.042 0.678 1.099 1.467

1113-02-6 Omethoate Pest 0.00 0.21 0.68 1.31 1.94

133-07-3 Folpet Pest 0.00 0.22 0.65 1.27 1.89

66230-04-4 Esfenvalerate Pest 0.00 0.17 0.65 1.31 1.95

94-74-6 MCPA Pest 0.00 0.16 0.64 1.25 1.88

14816-18-3 Phoxim Pest 0.00 0.20 0.63 1.33 1.94

138261-41-3 Imidacloprid Pest 0.00 0.25 0.63 1.14 1.78

122-14-5 Fenitrothion Pest 0.00 0.20 0.62 1.31 1.94

1912-24-9 atrazine Pest 0.00 0.18 0.62 1.29 1.85

21087-64-9 Metribuzin Pest 0.00 0.04 0.61 1.06 1.39

22224-92-6 Fenamiphos Pest 0.00 0.17 0.59 1.36 1.94

83121-18-0 Teflubenzuron Pest 0.00 0.17 0.59 1.23 1.84

142459-58-3 Flufenacet Pest 0.00 0.04 0.56 1.03 1.37

118-96-7 2,4,6-trinitrotoluene Other 0.001 0.024 0.547 1.156 1.495

106-24-1 geraniol Other 0.000 0.041 0.533 1.138 1.487

15972-60-8 Alachlor Pest 0.00 0.15 0.53 1.23 1.82

74070-46-5 Aclonifen Pest 0.00 0.16 0.52 1.12 1.70

91-20-3 naphthalene Other 0.000 0.020 0.510 1.145 1.489

98-82-8 cumene Other 0.000 0.020 0.510 1.138 1.487

87674-68-8 Dimethenamid Pest 0.00 0.18 0.50 1.23 1.81

101-77-9 4,4'-methylenedianiline Other 0.002 0.028 0.482 1.124 1.491

1241-94-7 2-ethylhexyl diphenyl phosphate Other 0.001 0.024 0.472 1.127 1.490

136426-54-5 Fluquinconazole Pest 0.00 0.02 0.45 1.06 1.45

23

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

2310-17-0 Phosalone Pest 0.00 0.05 0.42 0.95 1.43

120-83-2 2,4-dichlorophenol Other 0.000 0.017 0.420 1.139 1.491

668-34-8 Fentin Pest 0.00 0.02 0.41 0.94 1.40

104-76-7 2-ethylhexan-1-ol Other 0.000 0.017 0.406 1.099 1.486

25057-89-0 Bentazone Pest 0.00 0.04 0.41 0.86 1.37

732-11-6 Phosmet Pest 0.00 0.04 0.40 0.89 1.38

115-29-7 Endosulfan Pest 0.00 0.04 0.39 0.91 1.31

298-00-0 Parathion-Methyl Pest 0.00 0.03 0.38 0.88 1.42

3347-22-6 Dithianon Pest 0.00 0.03 0.36 0.88 1.27

7287-19-6 Prometryn Pest 0.00 0.04 0.36 0.87 1.31

114-26-1 Propoxur Pest 0.00 0.04 0.36 0.79 1.28

112-30-1 decan-1-ol Other 0.000 0.013 0.357 0.945 1.477

143390-89-0 Kresoxim-Methyl Pest 0.00 0.03 0.34 0.82 1.37

1563-66-2 Carbofuran Pest 0.00 0.04 0.34 0.80 1.32

60-51-5 Dimethoate Pest 0.00 0.05 0.33 0.84 1.40

298-46-4 Karbamazepin Phar 0.00 0.01 0.32 1.06 1.49

330-55-2 Linuron Pest 0.00 0.01 0.32 0.98 1.35

10605-21-7 Carbendazim Pest 0.00 0.00 0.31 0.85 1.38

314-40-9 Bromacil Pest 0.00 0.01 0.30 0.64 1.05

163515-14-8 Dimethenamid-P Pest 0.00 0.01 0.30 0.85 1.21

51218-45-2 Metolachlor Pest 0.00 0.01 0.29 0.92 1.32

16752-77-5 Methomyl Pest 0.00 0.01 0.28 0.68 1.20

950-37-8 Methidathion Pest 0.00 0.01 0.27 0.77 1.41

2764-72-9 Diquat Pest 0.00 0.00 0.26 0.72 1.33

121552-61-2 Cyprodinil Pest 0.00 0.01 0.25 0.68 1.12

131860-33-8 Azoxystrobin Pest 0.00 0.01 0.25 0.74 1.35

3209-22-1 1,2-dichloro-3-nitrobenzene Other 0.000 0.004 0.252 1.048 1.484

129-00-0 pyrene Other 0.000 0.005 0.250 0.958 1.480

107534-96-3 Tebuconazole Pest 0.00 0.00 0.25 0.87 1.42

57-63-6 Etinylestradiol Phar 0.00 0.00 0.24 1.02 1.48

82-68-8 Quintozene Pest 0.00 0.01 0.24 0.68 1.23

139481-59-7 Kandesartan Phar 0.00 0.00 0.23 0.92 1.44

21725-46-2 Cyanazine Pest 0.00 0.00 0.23 0.92 1.36

13684-56-5 Desmedipham Pest 0.00 0.00 0.23 0.69 1.25

139-40-2 propazine Pest 0.00 0.00 0.23 0.91 1.36

112-53-8 dodecan-1-ol Other 0.000 0.002 0.222 0.783 1.460

82657-04-3 Bifenthrin Pest 0.00 0.00 0.22 0.74 1.39

93413-69-5 Venlafaxin Phar 0.00 0.00 0.22 0.98 1.47

115-86-6 triphenyl phosphate Other 0.000 0.003 0.215 0.807 1.469

53-16-7 estrone Other 0.000 0.005 0.215 0.898 1.478

6197-30-4 octocrilene Other 0.000 0.004 0.200 0.864 1.479

298-04-4 Disulfoton Pest 0.00 0.00 0.20 0.65 1.38

96489-71-3 Pyridaben Pest 0.00 0.00 0.20 0.63 1.19

82558-50-7 Isoxaben Pest 0.00 0.00 0.20 0.58 1.19

28249-77-6 Thiobencarb Pest 0.00 0.01 0.19 0.55 1.15

133-06-2 Captan Pest 0.00 0.00 0.19 0.59 1.12

24

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

26787-78-0 amoxicillin Phar 0.00 0.00 0.18 0.86 1.43

470-90-6 Chlorfenvinphos Pest 0.00 0.00 0.18 0.61 1.35

15545-48-9 Chlorotoluron Pest 0.00 0.01 0.18 0.66 1.32

15307-86-5 diclofenac Phar 0.00 0.00 0.18 0.83 1.41

611-06-3 1,3-dichloro-4-nitrobenzene Other 0.000 0.001 0.175 0.904 1.474

100-00-5 1-chloro-4-nitrobenzene Other 0.000 0.000 0.170 0.916 1.475

1746-81-2 Monolinuron Pest 0.00 0.00 0.17 0.89 1.34

99-54-7 1,2-dichloro-4-nitrobenzene Other 0.000 0.001 0.164 0.871 1.472

50-28-2 estradiol Phar 0.00 0.00 0.16 0.80 1.46

23103-98-2 Pirimicarb Pest 0.00 0.00 0.16 0.54 1.23

108-78-1 melamine Other 0.000 0.003 0.158 0.768 1.455

15165-67-0 Dichlorprop-P Pest 0.00 0.00 0.15 0.51 1.09

2540-82-1 Formothion Pest 0.00 0.00 0.15 0.53 1.34

36734-19-7 Iprodione Pest 0.00 0.00 0.15 0.46 1.08

101-84-8 diphenyl ether Other 0.000 0.000 0.151 0.772 1.465

58-89-9 Lindane Pest 0.00 0.00 0.05 0.48 0.94

626-43-7 3,5-dichloroaniline Other 0.000 0.001 0.044 0.616 1.137

709-98-8 Propanil Pest 0.00 0.00 0.04 0.43 0.87

886-50-0 Terbutryn Pest 0.00 0.00 0.04 0.40 0.87

64902-72-3 Chlorsulfuron Pest 0.00 0.00 0.04 0.37 0.69

1861-40-1 Benfluralin Pest 0.00 0.00 0.03 0.48 0.99

100-51-6 benzyl alcohol Other 0.000 0.001 0.032 0.483 1.121

67747-09-5 Prochloraz Pest 0.00 0.00 0.03 0.35 0.85

1014-69-3 Desmetryn Pest 0.00 0.00 0.03 0.48 0.84

13674-87-8 tris[2-chloro-1-(chloromethyl)ethyl] phosphate

Other 0.000 0.001 0.025 0.466 1.124

1689-83-4 Ioxynil Pest 0.00 0.00 0.02 0.39 0.78

100-02-7 4-nitrophenol Other 0.000 0.001 0.022 0.537 1.149

135-19-3 2-naphthol Other 0.000 0.001 0.022 0.441 1.125

26225-79-6 Ethofumesate Pest 0.00 0.00 0.02 0.41 1.02

111-87-5 octan-1-ol Other 0.000 0.000 0.020 0.405 1.006

122-34-9 Simazine Pest 0.00 0.00 0.02 0.49 0.99

59447-55-1 (pentabromophenyl)methyl acrylate Other 0.000 0.000 0.016 0.394 1.063

1194-65-6 dichlobenil Pest 0.00 0.00 0.02 0.46 0.94

114-07-8 erythromycin Phar 0.00 0.00 0.01 0.41 1.11

60207-90-1 Propiconazole Pest 0.00 0.00 0.01 0.37 1.02

88-73-3 1-chloro-2-nitrobenzene Other 0.000 0.000 0.014 0.421 1.135

58-08-2 caffeine Phar 0.00 0.00 0.01 0.36 1.08

97-00-7 1-chloro-2,4-dinitrobenzene Other 0.000 0.000 0.011 0.330 1.101

81103-11-9 Klaritromycin Phar 0.00 0.00 0.01 0.38 1.10

35554-44-0 Imazalil Pest 0.00 0.00 0.01 0.32 0.89

57018-04-9 Tolclofos-methyl Pest 0.00 0.00 0.01 0.26 0.69

106-48-9 4-chlorophenol Other 0.000 0.000 0.010 0.319 1.103

1918-00-9 Dicamba Pest 0.00 0.00 0.01 0.33 0.71

99-30-9 Dicloran Pest 0.00 0.00 0.01 0.25 0.76

63-25-2 Carbaryl Pest 0.00 0.00 0.01 0.25 0.68

25

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

1420-07-1 Dinoterb Pest 0.00 0.00 0.01 0.32 0.69

94-82-6 2,4-DB Pest 0.00 0.00 0.01 0.30 0.67

120-36-5 Dichlorprop Pest 0.00 0.00 0.01 0.27 0.68

108-68-9 3,5-xylenol Other 0.000 0.000 0.006 0.250 0.915

732-26-3 2,4,6-tri-tert-butylphenol Other 0.000 0.000 0.006 0.231 0.911

89-63-4 4-chloro-2-nitroaniline Other 0.000 0.000 0.005 0.268 1.038

19666-30-9 Oxadiazon Pest 0.00 0.00 0.01 0.22 0.64

52645-53-1 Permethrin Phar 0.00 0.00 0.01 0.22 0.89

71751-41-2 Abamectin Pest 0.00 0.00 0.01 0.02 0.26

106-46-7 1,4-dichlorobenzene Other 0.000 0.000 0.005 0.262 1.062

2212-67-1 Molinate Pest 0.00 0.00 0.00 0.19 0.57

119-61-9 benzophenone Other 0.000 0.000 0.004 0.223 1.025

85721-33-1 Ciprofloxacin Phar 0.00 0.00 0.00 0.29 0.96

110488-70-5 Dimethomorph Pest 0.00 0.00 0.00 0.22 0.72

74051-80-2 Sethoxydim Pest 0.00 0.00 0.00 0.04 0.37

66246-88-6 Penconazole Pest 0.00 0.00 0.00 0.22 0.74

73334-07-3 iopromide Phar 0.00 0.00 0.00 0.19 0.88

330-54-1 diuron Pest 0.00 0.00 0.00 0.30 0.78

1689-84-5 Bromoxynil Pest 0.00 0.00 0.00 0.30 0.71

120-82-1 1,2,4-trichlorobenzene Other 0.000 0.000 0.003 0.210 1.008

65-85-0 benzoic acid Other 0.000 0.000 0.003 0.237 0.938

41394-05-2 Metamitron Pest 0.00 0.00 0.00 0.23 0.90

103-90-2 paracetamol Phar 0.00 0.00 0.00 0.17 0.81

108-42-9 3-chloroaniline Other 0.000 0.000 0.003 0.233 1.040

120-18-3 naphthalene-2-sulphonic acid Other 0.000 0.000 0.003 0.254 0.913

99-76-3 methyl 4-hydroxybenzoate Other 0.000 0.000 0.003 0.184 0.773

148-79-8 Thiabendazole Pest 0.00 0.00 0.00 0.21 0.71

10453-86-8 Resmethrin Pest 0.00 0.00 0.00 0.22 0.74

82419-36-1 Ofloxacin Phar 0.00 0.00 0.00 0.16 0.80

15299-99-7 Napropamide Pest 0.00 0.00 0.00 0.21 0.61

2164-08-1 Lenacil Pest 0.00 0.00 0.00 0.18 0.57

112-05-0 nonanoic acid Other 0.000 0.000 0.002 0.170 0.694

22204-53-1 Naproxen Phar 0.00 0.00 0.00 0.21 0.87

98967-40-9 Flumetsulam Other 0.000 0.000 0.001 0.164 0.811

95-16-9 benzothiazole Other 0.000 0.000 0.001 0.159 0.810

92-52-4 biphenyl Other 0.000 0.000 0.001 0.166 0.783

19937-59-8 Metoxuron Pest 0.00 0.00 0.00 0.17 0.72

38083-17-9 climbazole Other 0.000 0.000 0.001 0.036 0.598

525-66-6 Propranolol Phar 0.00 0.00 0.00 0.04 0.56

101-42-8 Fenuron Pest 0.00 0.00 0.00 0.02 0.43

66357-35-5 ranitidine Phar 0.00 0.00 0.00 0.00 0.29

2164-17-2 Fluometuron Pest 0.00 0.00 0.00 0.23 0.74

95-49-8 2-chlorotoluene Other 0.000 0.000 0.001 0.032 0.621

63-05-8 androst-4-ene-3,17-dione Other 0.000 0.000 0.001 0.022 0.465

126-71-6 triisobutyl phosphate Other 0.000 0.000 0.001 0.021 0.429

919-86-8 Demeton-S-methyl Pest 0.00 0.00 0.00 0.02 0.37

26

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

88671-89-0 Myclobutanil Pest 0.00 0.00 0.00 0.16 0.63

83905-01-5 Azitromycin Phar 0.00 0.00 0.00 0.02 0.61

541-73-1 1,3-dichlorobenzene Other 0.000 0.000 0.001 0.023 0.552

106-44-5 p-cresol Other 0.000 0.000 0.001 0.024 0.434

18691-97-9 Methabenzthiazuron Pest 0.00 0.00 0.00 0.00 0.28

123312-89-0 Pymetrozine Pest 0.00 0.00 0.00 0.00 0.22

86-73-7 fluorene Other 0.000 0.000 0.000 0.049 0.650

55219-65-3 Triadimenol Pest 0.00 0.00 0.00 0.03 0.48

5466-77-3 2-ethylhexyl 4-methoxycinnamate Other 0.000 0.000 0.000 0.025 0.451

112410-23-8 Tebufenozide Pest 0.00 0.00 0.00 0.05 0.43

10265-92-6 Methamidophos Pest 0.00 0.00 0.00 0.02 0.34

88150-42-9 Amlodipin Phar 0.00 0.00 0.00 0.00 0.32

37350-58-6 Metoprolol Phar 0.00 0.00 0.00 0.01 0.31

3060-89-7 Metobromuron Pest 0.00 0.00 0.00 0.00 0.22

63284-71-9 Nuarimol Pest 0.00 0.00 0.00 0.00 0.22

84057-84-1 Lamotrigin Phar 0.00 0.00 0.00 0.00 0.05

51481-61-9 Cimetidin Phar 0.00 0.00 0.00 0.00 0.04

657-24-9 Metformin Phar 0.00 0.00 0.00 0.00 0.03

443-48-1 Metronidazol Phar 0.00 0.00 0.00 0.00 0.00

3930-20-9 Sotalol Phar 0.00 0.00 0.00 0.00 0.00

68-35-9 Sulfadiazin Phar 0.00 0.00 0.00 0.00 0.00

1698-60-8 Chloridazon Pest 0.00 0.00 0.00 0.16 0.64

114369-43-6 Fenbuconazole Pest 0.00 0.00 0.00 0.17 0.63

1085-98-9 Dichlofluanid Pest 0.00 0.00 0.00 0.16 0.57

84087-01-4 Quinclorac Pest 0.00 0.00 0.00 0.17 0.56

54-31-9 Furosemid Phar 0.00 0.00 0.00 0.02 0.53

117-81-7 bis(2-ethylhexyl) phthalate Other 0.000 0.000 0.000 0.006 0.518

723-46-6 Sulfametoxazol Phar 0.00 0.00 0.00 0.01 0.48

50471-44-8 Vinclozolin Pest 0.00 0.00 0.00 0.05 0.43

94-81-5 MCPB Pest 0.00 0.00 0.00 0.05 0.43

111991-09-4 Nicosulfuron Pest 0.00 0.00 0.00 0.05 0.40

83055-99-6 Bensulfuron-methyl Pest 0.00 0.00 0.00 0.04 0.38

74223-64-6 Metsulfuron-methyl Pest 0.00 0.00 0.00 0.04 0.38

78587-05-0 Hexythiazox Pest 0.00 0.00 0.00 0.04 0.37

834-12-8 ametryn Pest 0.00 0.00 0.00 0.01 0.37

534-52-1 2-methyl-4,6-dinitro-phenol | DNOC Other 0.000 0.000 0.000 0.006 0.351

134523-00-5 Atorvastatin Phar 0.00 0.00 0.00 0.00 0.34

85-68-7 benzyl butyl phthalate Other 0.000 0.000 0.000 0.014 0.340

53112-28-0 Pyrimethanil Pest 0.00 0.00 0.00 0.03 0.33

23950-58-5 Propyzamide Pest 0.00 0.00 0.00 0.00 0.33

121-75-5 Malathion Phar 0.00 0.00 0.00 0.01 0.31

79617-96-2 Sertralin Phar 0.00 0.00 0.00 0.01 0.29

120928-09-8 Fenazaquin Pest 0.00 0.00 0.00 0.01 0.29

51-28-5 2,4-dinitrophenol Other 0.000 0.000 0.000 0.003 0.286

84-74-2 dibutyl phthalate Other 0.000 0.000 0.000 0.007 0.279

106-43-4 4-chlorotoluene Other 0.000 0.000 0.000 0.005 0.262

27

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

95-14-7 benzotriazole Other 0.000 0.000 0.000 0.003 0.261

78-51-3 tris(2-butoxyethyl) phosphate Other 0.000 0.000 0.000 0.006 0.252

18181-80-1 Bromopropylate Pest 0.00 0.00 0.00 0.00 0.25

7003-89-6 Chlormequat Pest 0.00 0.00 0.00 0.01 0.24

439-14-5 Diazepam Phar 0.00 0.00 0.00 0.00 0.24

131-57-7 oxybenzone Other 0.000 0.000 0.000 0.005 0.236

13674-84-5 tris(2-chloro-1-methylethyl) phosphate

Other 0.000 0.000 0.000 0.003 0.216

69-53-4 ampicillin Phar 0.00 0.00 0.00 0.00 0.21

54910-89-3 Fluoxetin Phar 0.00 0.00 0.00 0.00 0.20

131-11-3 dimethyl phthalate Other 0.000 0.000 0.000 0.003 0.201

1333-07-9 toluenesulphonamide Other 0.000 0.000 0.000 0.002 0.184

41483-43-6 Bupirimate Pest 0.00 0.00 0.00 0.00 0.18

101-21-3 Chlorpropham Pest 0.00 0.00 0.00 0.00 0.18

126-73-8 tributyl phosphate Other 0.000 0.000 0.000 0.002 0.174

96-18-4 1,2,3-trichloropropane Other 0.000 0.000 0.000 0.001 0.158

41859-67-0 Bezafibrat Phar 0.00 0.00 0.00 0.00 0.16

131341-86-1 Fludioxonil Pest 0.00 0.00 0.00 0.00 0.16

120-47-8 ethyl 4-hydroxybenzoate Other 0.000 0.000 0.000 0.001 0.048

87820-88-0 Tralkoxydim Pest 0.00 0.00 0.00 0.00 0.04

87-61-6 1,2,3-trichlorobenzene Other 0.000 0.000 0.000 0.001 0.041

99-87-6 p-cymene Other 0.000 0.000 0.000 0.001 0.040

91-57-6 2-methylnaphthalene Other 0.000 0.000 0.000 0.000 0.039

55512-33-9 Pyridate Pest 0.00 0.00 0.00 0.00 0.04

188425-85-6 Boscalid Pest 0.00 0.00 0.00 0.00 0.04

4065-45-6 sulisobenzone Other 0.000 0.000 0.000 0.000 0.034

791-28-6 triphenylphosphine oxide Other 0.000 0.000 0.000 0.001 0.019

94-13-3 propyl 4-hydroxybenzoate Other 0.000 0.000 0.000 0.000 0.016

59-50-7 chlorocresol Other 0.000 0.000 0.000 0.000 0.010

78-40-0 triethyl phosphate Other 0.000 0.000 0.000 0.000 0.007

57837-19-1 Metalaxyl Pest 0.00 0.00 0.00 0.00 0.00

121-86-8 2-chloro-4-nitrotoluene Other 0.000 0.000 0.000 0.000 0.004

3115-49-9 (4-nonylphenoxy)acetic acid Other 0.000 0.000 0.000 0.000 0.003

77732-09-3 Oxadixyl Pest 0.00 0.00 0.00 0.00 0.00

80-73-9 1,3-dimethylimidazolidin-2-one Other 0.000 0.000 0.000 0.000 0.002

70458-96-7 Norfloxacin Phar 0.00 0.00 0.00 0.00 0.00

25812-30-0 Gemfibrozil Phar 0.00 0.00 0.00 0.00 0.00

738-70-5 Trimetoprim Phar 0.00 0.00 0.00 0.00 0.00

83-32-9 acenaphthene Other 0.000 0.000 0.000 0.000 0.001

3380-34-5 triclosan Phar 0.00 0.00 0.00 0.00 0.00

102-82-9 tributylamine Other 0.000 0.000 0.000 0.000 0.001

115-96-8 tris(2-chloroethyl) phosphate Other 0.000 0.000 0.000 0.000 0.001

10238-21-8 Glibenklamid Phar 0.00 0.00 0.00 0.00 0.00

70-55-3 toluene-4-sulphonamide Other 0.000 0.000 0.000 0.000 0.001

5234-68-4 Carboxin Pest 0.00 0.00 0.00 0.00 0.00

54-11-5 nicotine Phar 0.00 0.00 0.00 0.00 0.00

28

CAS Number Name Type Score (PEC/100)

Score (PEC/10)

Score (PEC)

Score (PEC*10)

Score (PEC*100)

126833-17-8 Fenhexamid Pest 0.00 0.00 0.00 0.00 0.00

76824-35-6 Famotidin Phar 0.00 0.00 0.00 0.00 0.00

29122-68-7 Atenolol Phar 0.00 0.00 0.00 0.00 0.00

53-86-1 Indometacin Phar 0.00 0.00 0.00 0.00 0.00

81-07-2 1,2-benzisothiazol-3(2H)-one 1,1-dioxide

Other 0.000 0.000 0.000 0.000 0.000

768-94-5 amantadine Other 0.000 0.000 0.000 0.000 0.000

42200-33-9 Nadolol Phar 0.00 0.00 0.00 0.00 0.00

122931-48-0 Rimsulfuron Pest 0.00 0.00 0.00 0.00 0.00

138402-11-6 Irbesartan Phar 0.00 0.00 0.00 0.00 0.00

16672-87-0 Ethephon Pest 0.00 0.00 0.00 0.00 0.00

101205-02-1 Cycloxydim Pest 0.00 0.00 0.00 0.00 0.00

60-54-8 Tetracyklin Phar 0.00 0.00 0.00 0.00 0.00

128-13-2 ursodeoxycholic acid|Ursodeoxicholsyra | Ursodiol

Phar 0.00 0.00 0.00 0.00 0.00

93413-62-8 4-[2-(Dimethylamino)-1-(1-hydroxycyclohexyl)ethyl]phenol

Other 0.000 0.000 0.000 0.000 0.000

61869-08-7 Paroxetin Phar 0.00 0.00 0.00 0.00 0.00

24579-73-5 Propamocarb Pest 0.00 0.00 0.00 0.00 0.00

121-69-7 N,N-dimethylaniline Other 0.000 0.000 0.000 0.000 0.000

114798-26-4 Losartan Phar 0.00 0.00 0.00 0.00 0.00

1702-17-6 Clopyralid Pest 0.00 0.00 0.00 0.00 0.00

131-56-6 2,4-dihydroxybenzophenone Other 0.000 0.000 0.000 0.000 0.000

81-81-2 warfarin Phar 0.00 0.00 0.00 0.00 0.00

94-09-7 Bensokain Phar 0.00 0.00 0.00 0.00 0.00

69377-81-7 Fluroxypyr Pest 0.00 0.00 0.00 0.00 0.00

50-02-2 Dexametason Phar 0.00 0.00 0.00 0.00 0.00

69-72-7 salicylic acid Phar 0.00 0.00 0.00 0.00 0.00

98-92-0 nicotinamide Phar 0.00 0.00 0.00 0.00 0.00

89-57-6 5-aminosalicylic acid | Mesalazin Phar 0.00 0.00 0.00 0.00 0.00

143-24-8 bis(2-(2-methoxyethoxy)ethyl) ether Other 0.000 0.000 0.000 0.000 0.000

137862-53-4 Valsartan Phar 0.00 0.00 0.00 0.00 0.00

112-49-2 1,2-bis(2-methoxyethoxy)ethane Other 0.000 0.000 0.000 0.000 0.000

S8. Accuracy of pharmaceuticals emissions

We investigated the inaccuracy from our assumption of a constant excretion factor of 12% for all

substances. Out of 105 validation cases for pharmaceuticals, Lindim et al. (2016a) report substance-

specific excretion rates for 50 cases. For these 50 cases, the error is -0.14 (mean) ±1.06 (SD). With

variable excretion rates the error would have been -0.08±1.22. Oldenkamp et al. (2018) collected

substance-specific excretion rates for 45 of our validation cases. The error for these 45 cases is 0.11

(mean) ±0.89 (SD). With variable excretion rates the error would have been 0.48±1.05. As using

29

342

343

344

345

346

347

348

349

substance-specific excretion rates does not reduce the scatter of the error across substances, the

use of constant excretion rates is not likely a key factor explaining the model error. We noticed that

there is poor correlation between excretion rates collected by Lindim et al. and Oldenkamp et al.

respectively for the same chemical. This suggests that the accuracy of substance-specific excretion

rates collected from literature is limited.

We also investigated the accuracy of the fate of pharmaceuticals in WWTPs simulated with

SimpleTreat, as errors amount to one order or more (Lautz et al., 2017). Out of the 105 validation

cases, we could compare the simulated fraction to effluent to observed values (UNESCO-IHP; 2017)

for 65 cases. The error for these 65 cases is 0.04 (mean) ±0.99 (SD). Using the observed fraction to

effluents, the error would have been 0.07±0.92. As using observed fate in WWTPs does not reduce

the scatter of the error across substances, the use of simulated fate in WWTPs is not likely a key

factor explaining the model error.

S9. References to Supporting Information

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