Geographical Representation of Local Societal Risk – an

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Geographical Representation of Local
Societal Risk – an update
Dr Diego Lisbona
Fire and Process Safety Unit, Health and Safety Laboratory (HSL)
www.hsl.gov.uk
www.hsl.gov.uk
AnAn
Agency
of theof
Health
and Safety
Executive
Agency
the Health
and
Safety
Executive
[email protected]
Outline
•
Societal Risk
– Concept
– Context
•
•
•
Land Use Planning Advice
QuickRisk
Representations of Societal Risk
– Numerical
– Graphical
– Geographical
•
Conclusions
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Concept
Societal 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)
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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
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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
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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
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QuickRisk process
• Parameterised
• Direct
• Models
Prepare
Inputs
•
•
•
•
•
Scenarios
Frequency
Population
Weather
Zones
Calculate
Produce
Outputs
• Individual Risk
• Societal Risk
3 zone maps
FN curves
Nmax
PLL
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Geographical
representations
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)
•
•
Number of operations per year
•
Area, identifier that enables allocation of scenarios to geographical areas
or major hazard site
Time of the release; to allocate scenarios as occurring during a
particular time period only
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
–
Multisite calculations that consider onsite populations when offsite from the site
generating the risk
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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
F (cumulative
frequency)
R1% fatality
R10% fatality
R50% fatality
•
0.01
0.1
0.5
D (dose)
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)
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Parameterised harm contours
1000
d, m, c, s = f (release rate,
duration)
800
distance (m)
600
d, m, c, s = f (inventory)
c/2
400
m
d
200
0
-200
0
200
400
s
-200
distance (m)
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600
800
1000
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
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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
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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
1000
800
frequency, number of fatalities
f  f (event)  Pweather  Pwind direction /(P  nsectors )
distance (m)
600
400
200
f  f (event)  Pweather
0
-200
0
200
400
1000
-200
distance (m)
800
600
distance (m)
400
200
0
-1000
-500
0
500
-200
-400
-600
-800
-1000
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Executive
distance (m)
1000
600
800
1000
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
PLL
f
(
N
)

N

i
e
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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
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Graphical representations
•
•
•
FN curves
Scenario FN curves
ΔPLL curves
–
–
Comparing FN curves
Comparing FN curves against
criterion lines
DPLL vs N curve
20
250
15
D F (cpm)
5
150
0
-5
100
-10
50
-15
-20
0
-25
-30
1
10
100
1000
10000
Number of fatalities
DF (cpm)
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DPLL (x10-6 fatalities per year)
-50
100000
-6
D PLL (x10 fatalities per year)
200
10
Graphical representations
•
Scenario FN curves
–
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Scenario ranking for prioritisation of
risk reduction measures
Geographical representations
•
PLL density map
– 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:
Inner
Middle
Outer
SRB
PLL (x106 fatal./year)
4500
3800
1700
1900
Area (ha)
260
350
520
3400
1.70E-05
1.10E-05
3.30E-06
5.60E-07
PLL density (fatal. /year ha)
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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
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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
IR

f
e
– 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:
PLL
IR

N
P

I

N
i
TROD
i
i
cr
i
T
c
i
i
– 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
D
N
N
N
i
criterion
i
i
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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
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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
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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
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