Environmental risk assessment: from perception to … · Environmental risk assessment: from...

Environmental risk assessment: from perception to decision

Paul Kwan-sing LAM

Department of Biology and Chemistry City University of Hong Kong

Hong Kong State Key Laboratory in Marine Pollution Hong Kong

Challenges in the information age

Challenges in the information age

Ability to collect relevant information

Sources of information Internet Television programmes, newspaper,

magazines Politicians, NGOs, Green groups, Books Seminars, forums and talks Quasi-scientific literature Scientific publications in academic journals

Ability to handle misinformation

Perception Vs Science

Fact or Fiction Myths or Truth

….virtually nothing left to fish from the seas by the middle of the century….?

Global Loss of Seafood Species %

of

spe

cie

s co

llap

sed

Years Source: Science/FAO

Global fisheries data (1950-2003)

Extrapolated long-term trend

..... Himalayan glaciers could melt to a fifth of current levels by 2035 (or 2350?)?....

http://pictures.howbits.com/the-unseen-effect-of-global-warming/

CHINESE WHITE DOLPHINS Chinese White Dolphins: 400 in 1990 80 in 1995 Extinction in 5 years' time

About US$ 1 million in 3 years

Population of Chinese White Dolphins > 1,200

CHINESE WHITE DOLPHINS

Ability to interpret information

Adopt a Risk-based approach

Management options

• Suspicion-based • Hazard-based • Risk-based*

• Risk-based, with cost-benefit considerations

*Risk = f (hazard x exposure)

Science Pre-caution

- HOW?

Management options

• Suspicion-based • Hazard-based • Risk-based*

• Risk-based, with cost-benefit considerations

*Risk = f (hazard x exposure)

Science Pre-caution

- HOW?

THE PRECAUTIONARY PRINCIPLE (North Sea Convention)

“accepting the principle of safeguarding the marine ecosystem of the North Sea by reducing polluting emissions of substances that are persistent, toxic and liable to bioaccumulate at source by the use of the best available technology and other appropriate measures…………….

PRECAUTIONARY PRINCIPLE (CONT.)

This applies especially when there is reason to assume that certain damage of harmful effects on the living resources of the sea are likely to be caused by such substances even when there is no scientific evidence to prove a causal link between emissions and effects (the principle of precautionary action)”

PRECAUTIONARY PRINCIPLE

Under-protection will lead to inadequate protection of ecological systems

Over-protection will lead to wastage of valuable resources which should be better targeted to protecting genuinely vulnerable and important systems. Not sustainable

Provide an estimate of the risk levels in a particular setting

Risk quotient (RQ) analysis Determine PEC or MEC PNEC

PEC/PNEC ratio 1: low risk PEC/PNEC ratio > 1: high risk

Risk characterization

Provide an estimate of the risk levels in a particular setting

Risk quotient (RQ) analysis Determine PEC or MEC PNEC

PEC/PNEC ratio 1: low risk PEC/PNEC ratio > 1: high risk

Risk characterization Predicted Environmental Concentration

Provide an estimate of the risk levels in a particular setting

Risk quotient (RQ) analysis Determine PEC or MEC PNEC

PEC/PNEC ratio 1: low risk PEC/PNEC ratio > 1: high risk

Risk characterization Measured Environmental Concentration

Provide an estimate of the risk levels in a particular setting

Risk quotient (RQ) analysis Determine PEC or MEC PNEC

PEC/PNEC ratio 1: low risk PEC/PNEC ratio > 1: high risk

Risk characterization Predicted No Effect

Concentration

How to derive PNECs

(Predicted No Effect Concentration)

How to derive PNECs

Some examples

Waterbird Study (Herons and Egrets)

Map of South China showing the sampling sites

Quanzhou

Xiamen

Hong Kong

Collection of eggs from nests located

on top of tall bamboos using a cherry

picker

Collection of eggs from nests located

on tall trees by a professional climber

Analyses of POPs

POPs

HCB, Endrin, Dieldrin,

Aldrin, Heptachlor,

Mirex, PCBs,

Chlordane, DDTs

Toxaphene Co-PCB &

PCDD/Fs

GCMS-NCI (negative chemical ionization)

HRGC-HRMS Two micro-ECD

with dual-column

Gas Chromatograph

Derivation of threshold effects level

BIOLOGICAL EFFECTS OF DDE

2.5 3.0 3.5 4.0 4.5 5.0-10

0

10

20

30

40

50Henny et al. (1984)Findholt (1984)Findholt & Trost (1985)

Log10 DDE (ng/g, wet weight)

% R

ed

uc

tio

n i

n S

urv

iva

l o

f Y

ou

ng

2.5 3.0 3.5 4.0 4.5 5.0-10

0

10

20

30

40

50Henny et al. (1984)Findholt (1984)Findholt & Trost (1985)

Log10 DDE (ng/g, wet weight)

% R

ed

uc

tio

n i

n S

urv

iva

l o

f Y

ou

ng

BIOLOGICAL EFFECTS OF DDE

2.5 3.0 3.5 4.0 4.5 5.0-10

0

10

20

30

40

50Henny et al. (1984)Findholt (1984)Findholt & Trost (1985)

Log10 DDE (ng/g, wet weight)

% R

ed

uc

tio

n i

n S

urv

iva

l o

f Y

ou

ng

BIOLOGICAL EFFECTS OF DDE

2.5 3.0 3.5 4.0 4.5 5.0-10

0

10

20

30

40

50Henny et al. (1984)Findholt (1984)Findholt & Trost (1985)

Log10 DDE (ng/g, wet weight)

% R

ed

uc

tio

n i

n S

urv

iva

l o

f Y

ou

ng

BIOLOGICAL EFFECTS OF DDE

2.5 3.0 3.5 4.0 4.5 5.0-10

0

10

20

30

40

50Henny et al. (1984)Findholt (1984)Findholt & Trost (1985)

Log10 DDE (ng/g, wet weight)

% R

ed

uc

tio

n i

n S

urv

iva

l o

f Y

ou

ng

BIOLOGICAL EFFECTS OF DDE

2.5 3.0 3.5 4.0 4.5 5.0-10

0

10

20

30

40

50Henny et al. (1984)Findholt (1984)Findholt & Trost (1985)

Estimated threshold

Log10 DDE (ng/g, wet weight)

% R

ed

uc

tio

n i

n S

urv

iva

l o

f Y

ou

ng

Deviation from zeroOne sample t-test:

t=1.92, P=0.0421000 ng/g wet wt.

Estimated threshold:

1,000 ng/g wet wt.

BIOLOGICAL EFFECTS OF DDE

Risks of DDE to Birds

Relationship between [DDE] in eggs of piscivorous birds and % fledging success for a

sustainable population showing the regression line and 95% confidence intervals.

DDE

2.5 3.0 3.5 4.0 4.5 5.00

50

100

150

200

Log concentration (ng/g wet wt.)

Su

rviv

al

of

yo

un

gfl

ed

ged

(%

)

Risks of DDE to Birds

Relationship between [DDE] in eggs of piscivorous birds and % fledging success for a

sustainable population showing the regression line and 95% confidence intervals.

DDE

2.5 3.0 3.5 4.0 4.5 5.00

50

100

150

200

Log concentration (ng/g wet wt.)

Su

rviv

al

of

yo

un

gfl

ed

ged

(%

)

(3.45)

Estimated threshold:

2,818 ng/g wet wt.

Risks of DDE to South China Waterbirds

DDE

Log concentration (ng/g wet wt.)

2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8

Cu

mu

lati

ve P

rob

ab

ilit

y%

0.1

1

10

30

50

70

90

99

99.9

Hong Kong

Xiamen

Quanzhou

Threshold (3.45)

Quanzhou

Xiamen

Hong Kong

Quanzhou

Xiamen

Hong Kong

Fish-eating

birds

Herons

Risks of DDE to South China Waterbirds

DDE

Log concentration (ng/g wet wt.)

2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8

Cu

mu

lati

ve

Pro

bab

ilit

y%

0.1

1

10

30

50

70

90

99

99.9

Hong Kong

Xiamen

Quanzhou

Threshold (3.45)

(2818 ng/g) Fish-eating birds

鹭鸟 吃鱼的鸟类

(1000 ng/g) Herons

Ability to extrapolate

Marine cetacean

Indo-Pacific Humpback Dolphin Sightings () in Pearl River Estuary (AFCD).

Hong Kong

Source: AFCD

Exposure Pathway

Sewage Outfall

Contaminated Mud Pit

Sea Water

Sediment

Contaminated Mud Pit

Fish Samples

Lionhead

(Collichthys lucida)

Croceine croaker

(Pseudosciaena crocea)

Mullet (Mugil sp.)

Croaker (Johnius sp.)

Anchovies

(Thryssa sp.)

Hairtail

(Trichiurus sp.)

Diet of dolphin comprise >90% fish

Threshold effect levels (No Observable Adverse Effect Levels; NOAELs)

Safety factors Scaling factor

RfD (human) TRV (other mammals)

Laboratory Toxicological Data

Threshold effect levels (No Observable Adverse Effect Levels; NOAELs)

Safety factors Scaling factor

RfD (human) TRV (other mammals)

Laboratory Toxicological Data

参考剂量(人类)

Variable Values

Contaminant concentration in

fish (CF)

Maximum; 95th and 50th

percentiles

Ingestion rate (IR) 0.076 kg/day

Fraction ingested (FI) 0.007-0.4

Exposure frequency (EF) 350 days/year

Exposure duration (ED) 70 years

Body weight (BW) 60 kg

Average time (AT) 25,550 days

Variable Values

Contaminant concentration in

fish (CF)

Maximum; 95th and 50th

percentiles

Ingestion rate (IR) 9 kg/day for dolphin

Fraction ingested (FI) 0.9 for dolphins

Exposure frequency (EF) 365 days/year

Exposure duration (ED) 35 years for dolphin

Body weight (BW) 185 kg for dolphin

Average time (AT) 12,775 days for dolphin

Variable Values

Contaminant concentration in

fish (CF)

Maximum; 95th and 50th

percentiles

Ingestion rate (IR) 9 kg/day for dolphin

Fraction ingested (FI) 0.9 for dolphins

Exposure frequency (EF) 365 days/year

Exposure duration (ED) 35 years for dolphin

Body weight (BW) 185 kg for dolphin

Average time (AT) 12,775 days for dolphin

Variable Values

Contaminant concentration in

fish (CF)

Maximum; 95th and 50th

percentiles

Ingestion rate (IR) 9 kg/day for dolphin

Fraction ingested (FI) 0.9 for dolphins

Exposure frequency (EF) 365 days/year

Exposure duration (ED) 35 years for dolphin

Body weight (BW) 185 kg for dolphin

Average time (AT) 12,775 days for dolphin

Threshold effect levels (No Observable Adverse Effect Levels; NOAELs)

Safety factors Scaling factor

RfD (human) TRV (other mammals)

Laboratory Toxicological Data

Threshold effect levels (No Observable Adverse Effect Levels; NOAELs)

Safety factors Scaling factor

RfD (human) TRV (other mammals)

Laboratory Toxicological Data

Derivation of MAC based on TRV

Toxicity Reference Values (TRV) were derived for the dolphin based on NOAELs from

mammalian surrogates following Sample et al. (1996) as follows:

Scaling factor: TRVr = NOAELt (BWt/BWr)1/4

Where: TRVr = Toxicity reference value for receptor species (mg kg-1

ww day-1

);

NOEAL = No observable adverse effect level for test species (mg kg-1

ww

day-1

);

BWr = Body weight of the receptor species (kg ww);

BWt = Body weight of the test species (kg ww);

Do not procrastinate - Extrapolate

Do not procrastinate - Extrapolate

(if cannot be based on science, should at least be transparent)

Ability to handle uncertainties

PEC or MEC RQ = PNEC

Sources of uncertainties

Measurement Understanding Time/space

Measurement Understanding Time/space

Measurement Understanding Biological variation

Risk characterization Determination of PEC/PNEC or MEC/PNEC ratio

Frequency

PEC and MEC

Frequency

PNEC

PEC, MEC and PNEC are not simple single numbers, but ranges, and even frequency distributions

RISK ASSESSMENT - CRITICISMS

• Too complex; too slow

• Too simplistic; too naive

• Too opaque

• Too unrealistic

Back to the precautionary principle;

Don’t wait for science

Ability to simplify complex issues

Ability to simplify complex issues

One example

Does RQ indicate unacceptable risk? Step 1: Examine worst-case RQ (i.e. highest MEC/lowest PNEC) If RQ > 1 => requires further estimate; If RQ < 1 => little concern

Does RQ indicate unacceptable risk? Step 2: Examine best-case RQ (i.e. lowest MEC/highest PNEC) If RQ > 1 => manage; If RQ < 1; refine estimates (?)

Does RQ indicate unacceptable risk? Step 3: Apply re-sampling techniques to estimate probability that RQ exceeds critical values and check sensitivity of distribution assumptions

Risk characterization Determination of PEC/PNEC or MEC/PNEC ratio

Frequency

PEC and MEC

Frequency

PNEC

PEC/PNEC or MEC/PNEC ratio

Ratio 1: low risk

Ratio > 1: high risk)

Frequency

RQ

RISK ASSESSMENT Feasible Affordable Scientifically-based Transparent

RISK ASSESSMENT Feasible Affordable Scientifically-based Transparent

FAST

Ability to adopt a multidisciplinary

approach

Framework for Risk Assessment and Management

Dose-response assessment (Toxicity assessment)

Exposure assessment

Risk characterization

Risk communication

Risk management

Hazard identification

Framework for Risk Assessment and Management

Dose-response assessment (Toxicity assessment)

Exposure assessment

Risk characterization

Risk communication

Risk management

Hazard identification

Science

Framework for Risk Assessment and Management

Dose-response assessment (Toxicity assessment)

Exposure assessment

Risk characterization

Risk communication

Risk management

Hazard identification

Science

Environmental economics

Ability to separate science from politics in decision making

Nature Conservation Policy Statement (Hong Kong)

Our nature conservation policy is to regulate, protect and manage natural resources that are important for the conservation of biological diversity of Hong Kong in a sustainable manner, taking into account social and economic considerations, for the benefit and enjoyment of the present and future generations of the community

Why should we conserve?

Intrinsic rights to live

Heritage for future generations

Biodiversity

Ecosystem functions

Ecosystem functions

Nutrient cycling Waste treatment Pollination Biological control Refugia Raw materials Genetic resources Recreation Cultural

Gas regulation Climate regulation Disturbance regulation Water regulation Water supply Erosion control Sediment retention Soil formation Food production

Ecosystem functions

Goods and services

Clean water Clean air Clean food Safe environment

NUMBER OF SPECIES

‘REDUNDANT SPECIES’ ‘RIVET’ HYPOTHESIS ‘IDIOSYNCRATIC’ HYPOTHESIS HYPOTHESIS

Three hypothetical relationships between the rate of an ecosystem process (e.g. primary production, rate of decomposition) and ecosystem species richness

What should we conserve?

“Hot spots” of high diversity

Rare species

Representative species assemblages

What should we conserve?

“Hot spots” of high diversity

Rare species

Representative species assemblages

Can we achieve all?

A Conservation Policy Why should we conserve? What should we

conserve? What is acceptable and

what is not?

A Conservation Policy Why should we conserve? What should we

conserve? What is acceptable and

what is not?

Conclusion - Ability to: collect relevant information handle misinformation interpret information extrapolate handle multiple stressors (mixtures) handle uncertainties simplify complex issues separate science from politics in

decision making

Acknowledgements Dr. Nobuyoshi Yamashita (AIST, Japan), Prof. John Giesy (University of Saskatewan , Canada), Prof. Shinsuke Tanabe (Ehime University, Japan), Prof. Kannan Kurunthachalam (SUNY, USA), Dr. Sachi Taniyasu (AIST, Japan), Dr. Yuichi Miyake (AIST, Japan), Prof. Des Connell (Griffith University, Australia), Dr. Keerthi S. Guruge (NIAH, Japan), Mr. Leo W.Y. Yeung (CityU, HK), Mr. Ridge K.F. Lau (CityU, HK), Dr. James C.W. Lam (CityU, HK), Dr. Margaret Murphy (CityU, HK) Dr. Iris M.K. So (CityU, HK), Dr. Bruce Richardson (CityU, HK) K.S. Cheung, Ivan Chan, Joseph Sham (AFCD) Stephanie Ma (EPD)

Research Grants Council, HK; City University of Hong Kong;

Agriculture, Fisheries and Conservation Dept., HKSAR

Thank You