1 WISE TG Meeting, Ispra, September 2011 Direct and Inverse Chemical Fate Modeling based on...

1

WISE TG Meeting, Ispra, September 2011

Direct and Inverse Chemical Fate Modeling based on pan-European Datasets

Dimitar MARINOV, Alberto PISTOCCHI, Robert LOOS, Bernd GAWLIK

European Commission, Joint Research Centre, Institute for Environment and Sustainability

2


Concept for large scale chemical modeling:

Now-a-days the availability of pan-continental datasets allows the development of spatially explicit GIS models for assessment of fate and distribution of pollutants into different environmental media (atmosphere, soil, surface water and sea)

In principle, spatial models predict chemical concentrations when the emissions to the environmental compartments at continental scale are known. This is the "direct" formulation of fate problem aiming to answer the question “where do pollutants go?“

Vice versa, when the emissions of chemicals are unknown, but their concentrations are widely monitored, the "inverse" modeling approach answers the question "where do pollutants come from?" The inverse models tracing back chemical emissions from measured concentrations and identifying possible sources of discharges.

3


Emissions, E

Environmental removal rates, K

Concentration = E/K

4


Datasets used in contaminant modeling:

1.Atmosphere: temperature, OH concentration, Aerosol concentration, Organic carbon content in aerosol, 10 m height wind velocity, Atmospheric mixing height, Precipitation, Duration of the wet period, Atmospheric Source-receptor relations, and Atmospheric Source-receptor time of travel

2.Soil: top soil organic carbon content, soil texture, diffusion runoff, evapotranspiration, infiltration, erosion rate, and Leaf Area Index (LAI)

3.Surface water: river discharge, river slop, river water velocity, water depth, suspended sediment concentration, and surface water residence time

4.Sea: mixing depth, seawater velocity, seawater temperature, total suspended matter, wind speed at 10m height over ocean surface, and chlorophyll

5

Characteristics of MAPPE direct model

Main topic(s) Maps of chemical concentrations and pollutant fluxes

Useful tool for screening hot spots or hazard zones Potential key client: DG ENV, DG SANCO, EEA, JRC

Policy(-ies) Requirements & Application

•Water Framework Directive•Marine Strategy Framework Directive

•Framework Directive on Sustainable Use of Pesticides •Scenarios for horizon 2020

Scale (Time&Space) Temporal: one year Spatial: Europe

Output Maps; Indicators;

Model integration Other impact assessment models

Challenges & Next Steps

•To became a transient model (currently steady state)•To develop global version of the model •Further integration with socio-economic models

Multimedia Assessment of Pollutant Pathways in the Environment (MAPPE)

6


7


8


9


10


11

Inverse modeling approach

)50

2lnexp( i

catchmentii t

DTEQC

C [ML-3] concentration Q [L3T-1] river water discharge Ei [MT-1] chemical mass emitted at the i-th location ti [T] time spent in water from the i-th emission location to the river cross section of measurement DT50 [T] total removal half life in the stream network

At steady state conditions the chemical mass (equivalently called “load”) at a given cross section is assumed to be equal to the sum of all emissions upstream each reduced by the removal occurring along the stream network

12

Two-parameter inverse model

)50

2lnexp( i

catchmentii t

DTPconstantQC

DT50 = 1, 3, 5, 7, 10, 50, 100 and 1000 days; DT50 = ∞ (no decay)

DT50 = 1 day DT50 = 100 days

13

Use of chemical monitoring data

27 countries; 122 sampling locations

16 Polar organic persistent pollutants

NAPROXEN

KETOPROFEN

BEZAFIBRATE

IBUPROFEN

DICLOFENAC

GEMFIBROZIL

BENZOTRIAZOLE

CAFFEINE

CARBAMAZEPINE

SULFAMETHOXAZOLE

METHYLBENZOTRIAZOLE

NPE1C

NONYLPHENOL

BISPHENOL A

ESTRONE

OP

14

Inverse model verification

15

Mapping chemical loads to river network

QC x

16

Disaggregated loads to European seas

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

kg/yAtlantic Baltic Black Mediterranean North

1. The average percentages of disaggregated load to European regional seas:

Atlantic 18.9%; Baltic 15%; Black Sea 8.6%; Mediterranean 26.3% ; North Sea 31.2%.

2. The European discharges more heavily affect North Sea and the system of Mediterranean and Black seas

17

PFOS modeling

PFOS

annual loadto sea

BAU scenario(baseline 2007)

[t/y] % of totalAtlantic ocean 1.240 21.2Baltic sea 1.117 19.1Black sea 1.597 27.4Mediterranean sea 0.853 14.6North sea 1.033 17.7

Total 5.840 N.A.

18


19

Aims

to test capabilities of the inverse modelling to perform back analysis of chemical emissions and half lives for pharmaceuticals, personal care products and substances widely used in households

to extend and improve the modelling approach by adding the further option for accounting the chemical decay in stream network

to map continuous continental spatial distribution of loads and concentrations in river network by generalisation of pan-European screening data from discrete points sampling campaign

to estimate the range of discharges of pharmaceuticals and personal care products to European seas

20

Estimation of model parameters

CAFFEINE DT 50 = 3 days

y = 0.0088xR2 = 0.5401

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 1.E+08

Catchment Population (inhab.)

Computed load, ug/s

CAFFEINE DT 50 = 3 days

y = 0.0002x1.1897

R2 = 0.7023

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 1.E+08

Catchment Population (inhab.)

Computed load, ug/s

)50,( x x DTPctoremisson faQC

Scatter diagrams were interpreted through best-fit linear trend line found by OLS method with zero intercept

Linear regression model

Measure of match: determination coefficient R2

QC x

Non-linear regression model

21

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

R2 DT 50 = 1 DT 50 = 3 DT 50 = 5 DT 50 = 7 DT 50 = 10 DT 50 = 50 DT 50 = 100 DT 50 = 1000 No decay Catchment area

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

R2 DT 50 = 1 DT 50 = 3 DT 50 = 5 DT 50 = 7 DT 50 = 10 DT 50 = 50 DT 50 = 100 DT 50 = 1000 No decay Catchment area

Identification of half lives and emission factors

The average R² correlation coefficients for all chemicals and half lives including ‘no decay’ case is 0.61 for the non-linear model versus 0.2 for the linear one

linear model

log-linear model

22

Estimates of total load to European seas

Total annual load to European seas

1.67 1.63

2.47

4.75

2.61

1.11

30.65

41.00

17.72

4.51

18.1016.00

1.03

2.02

0.46 0.47

0.10

1.00

10.00

100.00

NA

PR

OX

EN

KE

TO

PR

OF

EN

BE

ZA

FIB

RA

TE

IBU

PR

OF

EN

DIC

LO

FE

NA

C

GE

MF

IBR

OZ

IL

BE

NZ

OT

RIA

ZO

LE

CA

FF

EIN

E

CA

RB

AM

AZ

EP

INE

SU

LF

AM

ET

HO

XA

ZO

LE

ME

TH

YL

BE

NZ

OT

RIA

ZO

LE

NP

E1

C

NO

NY

LP

HE

NO

L

BIS

PH

EN

OL

A

ES

TR

ON

E

OP

t y-1

1. On average, the min extremes of total load to European seas count 40.1% less (range 23.1-91.5%) of the median estimates while the max values are 61.7% higher (range 18.6-127%)

2. Based on the median assessment for load coming from EU-27 plus Switzerland, Norway, Moldova, Ukraine, Belarus and part of Turkey, it was found that the total amount of all 16 polar compounds exported to European seas is about 146.2 tonnes per year

23

Conclusions

1. The study showed that for screening assessment at continental level the concentrations of polar chemicals in river waters can be interpreted through a simple two-parameter model that assumes emissions proportional to catchment population and constant chemical half life

2. Besides, it is not possible for a chemical to select univocally a couple of emission factor and half life values. Instead that, a set of non-dominated combinations could be identified. In order to obtain more accurate estimates prior expert knowledge about emission factors or half life values is needed

3. Chemical loads are relatively insensitive to combinations of emission factors and half lives. Therefore, they can be estimated with a reasonable range of uncertainty, typically a factor 2. In this way, it is possible to produce continuous maps of loads and concentrations at continental scale

4. More generally, the inverse modeling from monitoring data supports cost-effective emission inventory of river chemical pollutants at European scale. The method could serve in chemical risk assessment or decision making in river basin management, as required by the European Water Framework Directive or Marine Strategy.

1 WISE TG Meeting, Ispra, September 2011 Direct and Inverse Chemical Fate Modeling based on...

Documents

Transcript of 1 WISE TG Meeting, Ispra, September 2011 Direct and Inverse Chemical Fate Modeling based on...