Geographical Representation of Local Societal Risk – an update

1. Geographical Representation of Local Societal Risk – an update Dr Diego Lisbona Fire and Process Safety Unit, Health and Safety Laboratory (HSL) diego.lisbona@hsl.gov.uk

2. Outline • Societal Risk • Concept • Context • Land Use Planning Advice • QuickRisk • Representations of Societal Risk • Numerical • Graphical • Geographical • Conclusions

3. 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) Individual risk Harm to an individual always present It has frequency units (eg. chances per million)

4. 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

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

6. 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

7. QuickRisk process • Parameterised • Direct • Models • Scenarios • Frequency • Population • Weather • Zones Prepare Inputs Calculate Produce Outputs 3 zone maps • Individual Risk • Societal Risk FN curves Nmax PLL Geographicalrepresentations

8. Inputs: Release Scenario • Geographical locationof 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

9. F (cumulative frequency) R1% fatality R10% fatality R50% fatality 0.01 0.1 0.5 D (dose) 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)

10. Parameterised harm contours • d, m, c, s = f (release rate, duration) • d, m, c, s = f (inventory) c/2 m d s

11. 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

12. 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

13. 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

14. 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

15. 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

16. FN curves Scenario FN curves ΔPLL curves Comparing FN curves Comparing FN curves against criterion lines Graphical representations

17. Scenario FN curves Scenario ranking for prioritisation of risk reduction measures Graphical representations

18. 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:

19. 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 (%)

20. 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

21. 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

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

23. 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