Geographical Representation of Local Societal Risk – an update

www.hsl.gov.ukAn Agency of the Health and Safety Executive

www.hsl.gov.ukAn Agency of the Health and Safety Executive

Geographical Representation of Local Societal Risk – an update

Dr Diego LisbonaFire and Process Safety Unit, Health and Safety Laboratory (HSL)

[email protected]

An Agency of the Health and Safety Executive

Outline• Societal Risk

– Concept– Context

• Land Use Planning Advice• QuickRisk• Representations of Societal Risk

– Numerical– Graphical– Geographical

• Conclusions


ConceptSocietal risk is the relationship between frequency and number of people suffering

from a specified level of harm in a given population from the realisation of specified hazards (Jones, 1985)

Societal risk…is about the chances

of more than one individual being

harmed simultaneously in an

incident

…varies according to the surrounding

population (location and density)

Individual risk

Harm to an individual always present

It has frequency units (eg. chances per million)


Context• Seveso II directive implemented in the UK via Control

of Major Accident Hazards (COMAH) regulations

– Likelihood, how far, how much harm to people?

• Responses to the CD212 Consultative Document agreed that government policies should take into account societal risk and that HSE should undertake work to achieve this


Land Use Planning• When based on risk, it is individual risk

levels

• Consent from the Hazardous Substances Authority (HSA, usually Local Planning Authority)

• HSA must consult HSE. HSE in turn establishes consultation distance around the installation

• 3-zone maps based on individual risk

– Person always present

– Maximum quantity of substances

– The Local Planning Authority must consult HSE on planning permission for developments within these zones


Land Use Planning• HSE’s advice on land use planning is delivered through PADHI –

Planning Advice for Developments near Hazardous Installations

http://www.hse.gov.uk/landuseplanning/lupcurrent.pdf

http://www.hse.gov.uk/landuseplanning/padhi.pdf

• PADHI uses two inputs to a decision matrix to generate the response:

– which zone the development is located in of the 3 zones– ‘Sensitivity Level’ of the proposed development (derived from an HSE categorisation

system of “Development Types”) http://www.hse.gov.uk/landuseplanning/sensitivitytable.pdf

• ‘Advise Against’ or ‘Don’t Advise Against’ response

• Existing Societal Risk levels not taken into account

• HSE commissioned from HSL the development of a tool for estimating societal risk (QuickRisk) and a framework for integrating societal risk in HSE’s LUP advice

http://www.hse.gov.uk/landuseplanning/sensitivitytable.pdf


QuickRisk process

Prepare Inputs

Calculate

Produce Outputs

• Individual Risk

• Societal RiskFN curves

Nmax

PLL

Geographical representations

3 zone maps

•Parameterised•Direct•Models

•Scenarios•Frequency•Population•Weather•Zones


Inputs: Release Scenario• Geographical location of the scenario (in any Cartesian coordinate

format e.g. Ordnance Survey coordinates). Single or multiple release points

• Scenario frequency; HSE has published a set of failure frequencies that are used in land use planning assessment (HSE, 2010)

• For continuous release scenarios, the release rate and release duration

• Inventory in tonnes or m3 (instantaneous releases)

• Time of the release; to allocate scenarios as occurring during a particular time period only

• Number of operations per year• Scenario type e.g. releases of Cl2, HF, SO2, refrigerated ammonia,

methyl chloroformate; instantaneous and continuous releases of LNG and LPG, isobutane or propane flashfires and poolfires – Parameterised dose contours

• Area, identifier that enables allocation of scenarios to geographical areas or major hazard site

– Multisite calculations that consider onsite populations when offsite from the site generating the risk


Harm contours• Dispersion modelling• Total Risk Of Death (TROD) (Rushton & Carter, 2009)

– 1, 10 and 50% fatality contours as opposed to 1% fatality contour only

• Two levels of complexity – Parameterised equations – Actual dose footprints (per scenario and/or per wind direction-topography)

• from DRIFT outputs (per scenario)• from CFD or shallow layer model (per scenario and direction)

F (cumulative frequency)

R1% fatality

R10% fatality

R50% fatality

0.01 0.1 0.5 D (dose)


Parameterised harm contours

-200

0

200

400

600

800

1000

-200 0 200 400 600 800 1000

distance (m)

dist

ance

(m)

d

c/2

s

m

d, m, c, s = f (release rate, duration)

d, m, c, s = f (inventory)


Harm contours– e.g. shallow layer dispersion or CFD model to take into account topography– area source– irregularly shaped 1%, 10%, 50% fatality contours, which change with wind

direction from release point– defined as lists of geometric polygons per release point (e.g. along pipeline)– EU CO2pipehaz project


Inputs: Weather and Population• Weather data

– QuickRisk uses weather data covering Met Office weather stations across the UK– Atmospheric stability category and wind speed combinations used are D5 (Pasquill

stability D with 5 m/s wind speed) and F2 D2 B2 for both day time and night time periods

• Population– Available from the National Population Database (NPD) – Data sets used in the NPD include

• Census data• Ordnance Survey (OS) digital mapping and addressing products• The Inter Departmental Business Register• Other government and commercial datasets

– Populations linked to time periods and weathers:• Night time • Day time (non term) residential and workplace layers• Road populations• Onsite populations on if offsite from risk• Sensitive populations added in (x vulnerability factors)

• LUP Zones– Societal risk outputs generated per

• LUP zone• Local area e.g. when affected for multiple major hazard sites


QuickRisk Calculation• Run from the Excel interface

• C++ module reads information in from input files generated from Excel template

– example multisite run (3 sites, 45 scenarios, 3 cases) in 20-30min - calc sets: >100,000,000

• Generates numerical and graphical outputs

frequency, number of fatalities

)/()( sec torsdirectionwindweather nPPPeventff

weatherPeventff )(-200

0

200

400

600

800

1000

-200 0 200 400 600 800 1000

distance (m)

dist

ance

(m)

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

-1000 -500 0 500 1000

distance (m)

dist

ance

(m)


Numerical representations• Potential loss of life (PLL), Nmax, f(Nmax), RICOMAH, RILUP

• PLL – sometimes referred to as expectation value (EV); average number of

persons expected to receive a specified level of harm (per year)– Risk integral with no risk aversion– Use in Cost Benefit Analysis and demonstration of ALARP

e

i NNfPLL )(


Graphical representations

• HSE document ‘Reducing Risks Protecting People’ gives a risk criterion: the risk of an accident causing the death of 50 or more people in a single event should be regarded as intolerable if the frequency is estimated to be more than one in five thousand per annum

• This criterion can be represented as a point in an FN curve and used to derive a comparison line with slope of -1 (no scale aversion) passing through the point

• No universal agreement upon comparison lines and level of detail in FN curves


Graphical representations• FN curves• Scenario FN curves• ΔPLL curves

– Comparing FN curves– Comparing FN curves against

criterion linesDPLL vs N curve

-30

-25

-20

-15

-10

-5

0

5

10

15

20

1 10 100 1000 10000 100000

Number of fatalities

DF

(cpm

)

-50

0

50

100

150

200

250

DPL

L (x

10-6

fata

litie

s pe

r yea

r)

DF (cpm) DPLL (x10-6 fatalities per year)


Graphical representations• Scenario FN curves

– Scenario ranking for prioritisation of risk reduction measures


Geographical representations• PLL density map

Inner Middle Outer SRB

PLL (x106 fatal./year) 4500 3800 1700 1900

Area (ha) 260 350 520 3400

PLL density (fatal. /year ha) 1.70E-05 1.10E-05 3.30E-06 5.60E-07

– Spatial distribution of PLL around the major hazard installation

– ‘Societal Risk Attention Zone’ or ‘Societal Risk Boundary’ (SRB), a function of the consultation distance

– PLL density values within each LUP zone:


Geographical representations• Risk maps derived from PLL density map

– QuickRisk effectively generates an FN curve for each grid square– PLL density can be broken down per scenario for each grid square– Useful to analyse risks in geographical areas that can be affected by releases from

several sites– Advice on potential risk reduction measures more likely to be effective at a given area

1) PLL dominant scenario 2) Contribution to PLL from dominant scenario (%)


Geographical representations• Risk-informed population density maps

– The summation of the frequencies at a grid location i represents the individual risk (IR), or the risk that one single individual will experience at that grid location

– When the individual risk is based on a harm criteria weighted TROD approach, individual risk and PLL density at each single grid location are linked:

– Some PLL density guideline values are available from the open literature e.g. 10-5 fatalities per year per hectare (Atkins, 2009), an order of magnitude lower than the value used by Wiersma et al. (2007). 10-5 fatalities per year per hectare (ha) used in the example

– Maximum population that would meet PLL criteria Ncriterion i is calculated

– Comparison with existing population

e

fIR

iiTRODi NIRPLL icriterioniTRODicriterion NIRPLL

iicriterioni NNN D


Geographical representations• Population density maps

– Show populations derived from risk criteria and spatial distribution of PLL– Developments and existing populations assessed against a risk criterion or guideline

value

1) Maximum population density that meets PLL density guideline of 10-5 fatalities/year/ha

2) Population density change to meet PLL density guideline value


Geographical representations• Nmax maps

– Maximum number of fatalities at each grid square or development.– Show scenario, time period (population) and weather condition that would

cause the worst-case consequence at each grid square

1) Map of maximum number of fatalities 2) Nmax scenario (inc. time period/population and weather)


Conclusions• QuickRisk produces numerical, graphical and geographical

representations

• Geographical representations of societal risk derived from PLL density and Nmax show:

– Distribution of PLL density contributions from each release scenario and the populations most affected by them

– Risk-informed population density maps

– Maximum number of fatalities associated to the worst case event in any given geographical area

• These representations can be used for more effective communication of societal risk levels affecting areas in the vicinity of major hazard sites

Documents

Geographical Representation of Local Societal Risk – an update