IChemE SYMPOSIUM SERIES NO. 153 # 2007 Crown Copyright

QUICKFN: A SIMPLIFIED METHODOLOGY FOR SOCIETAL RISK ESTIMATES†

M Wardman1,3, A H K Fowler2, D A Carter2 and J L Saw1

1Health and Safety Laboratory, Process Safety Section2Health and Safety Executive, Hazardous Installations Directorate, Chemical Industries Division3Contact details: HSL, Harpur Hill, Buxton, Derbyshire, SK17 9JN; e-mail: mike.wardman@hsl.gov.uk

The paper will describe simplified risk assessment techniques developed to address the risk from

onshore major hazard sites in the UK. “QuickFN” estimates an FN curve and various risk integrals

using a greatly reduced scenario set. It has been developed to capture the part of the FN curve

covering the highest N scenarios. The methodology, its implementation and its use by HSE will

be discussed.

KEYWORDS: societal risk, methodology, quantitative risk assessment (QRA), risk integral, FN curves

INTRODUCTIONSocietal Risk is the relationship between frequency andthe number of people suffering from a specified level ofharm in a given population from the realisation of specifiedhazards. It is used to account for the harm to society causedby major, multiple fatality events.

The best available technology for studying this“societal risk” is full scope application of quantitative riskassessment (QRA). However, the technique is time-consum-ing and requires a high level of technical capability. Theusual way of expressing the results of a QRA is in theform of a plot of the pairs of frequency (f) and potential con-sequences, typically number of fatalities (N). A cumulativefrequency (F) of events having N or more fatalities isplotted against N as a graph with log-log axes. These arecommonly referred to as F-N curves. Comparison of F-Ncurves against criteria can be done using criterion plots onthe same graphs and the F-N curve visually compared anda judgement made.

Measures of integral risk are another way of repre-senting societal risk. This gives a single numerical valueto represent the societal risk from a site. This numericalvalue could be directly compared against some criterion orused as a form of relative comparison of a set of majoraccident sites or different risk reduction measures at asingle site. Expectation value (EV) is the expected numberof fatalities per year and is calculated as follows:

EV ¼XN max

N¼1f (N)� N (1)

The expectation value does not distinguish betweenthe relatively frequent accidents that could lead to a smallnumber of potential fatalities and those lower frequencyaccidents that could lead to large numbers of potential fatal-ities. Hirst (2002) explains the use of an aversion multiplier(a) as a means of reflecting society’s aversion to accidentsthat lead to large numbers of potential fatalities. The

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aversion multiplier a is in the range of 1 to 3 and has beentaken as 1.4 for calculations by the HSE. This leads to aCOMAH Risk Integral denoted as RICOMAH.

RICOMAH ¼XN max

N¼1f (N)� Na (2)

The F-N curves and the measures of integral riskrequire a full QRA. Hirst (2002) describes a short cutmethod to calculate this risk integral without the need fora full QRA. This leads to an Approximate COMAH RiskIntegral (ARICOMAH). It is calculated from the worst caseevent with the highest N and associated frequency, f(N).The algorithm used differs for non-wind dependent eventsand wind dependent events. Part of the basis behind themethod is the assumption that the slope of the left handportion (lower N values) of an F-N curve is approximately–1. This approximate Risk Integral can be used for screen-ing and ranking. It has been noted that results are increas-ingly overestimated as NMAX increases (Fowler, 2004).

The QuickFN methodology has been developed toreproduce the F-N curve from a reduced set of scenariosusing information more easily available and less resourceintensive than that required for a full QRA. The method-ology also allows the generation of the EV and RICOMAH.It allows a greater resolution than ARICOMAH as a numericalrepresentation of the risk and is less resource intensive thana full QRA. The greater resolution leads to greater confi-dence in using this method over ARICOMAH to considerthe results as an absolute value and to use results to considerrisk reduction options.

METHODOLOGYHSE commissioned work to develop an intermediatesocietal risk tool that could provide improved resolutionthan ARICOMAH while still being less resource intensivethan a full QRA (HSE, 2004). It considered reducing thescenario set from a full QRA. The idea was to generate

†# 2007 Crown Copyright. This article is published with the permission of the controller of HMSO and the Queen’s Printer for Scotland.

IChemE SYMPOSIUM SERIES NO. 153 # 2007 Crown Copyright

the right hand portion (higher N values) of the FN curvewith a representative set of scenarios. The left handportion of the F-N curve is then extrapolated using a slopeof 21 on the log-log graph. The work involved taking rea-listic and representative installations for hazards includingliquefied chlorine and anhydrous hydrogen flouride alongwith a population distribution that reflected a real situation.The full f-N relationships were determined before sub setsof scenarios were selected and the F-N curves compared.The conclusion was that a reduced scenario set would beable to closely replicate the right hand portion.

The QuickFN methodology developed by HSE pro-duces a set of f and N pairs for a specific scenario with aspecific material. There are several of these pairs foreach scenario reflecting conditional probabilities inludingdirectional effects and weather conditions. Each material hasa set of parameterised equations to calculate the exposurecontour for three levels of harm, LD01, LD10 and LD50 i.e.1%, 10% and 50% prbability of fatality. These contoursalong with appropriate population data are used to calculateN. For a specified set of directions the population inside eachcontour is calculated. The right hand part of the F-N curve isgenerated from these calculated points and the left hand partfrom a slope of 21. The point of change is either taken tobe 10% of NMAX or the point where a slope of 21 is atangent to the calculated F-N curve, whichever is the greater.

IMPLEMENTATIONThe methodology is implemented as a workbook. It is pre-sented as a series of sheets. Sheets are provided for definingthe scenarios, selecting the weather station and defining thepopulation grid. The information needed is input on a fewsheets and the results and templates are stored in further sheets.

Figure 1. S

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The setup sheet is where the set of scenarios is listed,the weather station chosen and some parameters set thataffect accuracy and speed. The scenario definition ismade 7up of a reference, defining it as an instantaneous orcontinuous release; frequency of scenario; quantity released,or rate and duration of release depending on whetherinstantaneous or continuous; and the hazardous substance.

QuickFN is currently available for specific installa-tion types; including bulk storage installations of Cl2, HF,SO2, refrigerated NH3, refrigerated LPG and LNG. The con-sequences of the representative events for these installationtypes are calculated using parameterised equations to sim-plify the modelling. These are stored in the spreadsheet asa series of templates, two for each installation type tocover instantaneous releases and continuous releases.

Selection of scenarios for each installation type isbased on the work done by Quinn (2003) for Cl2, HF andLPG. The methodology has been extended to refrigeratedflammables by comparison with QRA. The scenarios arethen listed on the setup sheet.

Weather data from a range of weather stations aroundthe country is stored on another sheet and accessed using apull down menu on the setup sheet. The weather data usedis currently for F2 (Pasquill stability F at 2 m/s) and D5(Pasquill stability D at 5 m/s).

Precision is a parameter that determines the numberof directions the contours will be positioned in each winddirection. The slices parameter is used to increase the accu-racy of the population counted by subdividing each grid andusing that for counting population inside each contour.

Population is stored in grids of a specified size as inFigure 2. Typically this is a 30 � 30 km grid with a gridsize of 1 km. However depending on the hazard ranges ofthe scenarios a smaller or larger grid may be used. A

etup sheet

Figure 2. Population grid

IChemE SYMPOSIUM SERIES NO. 153 # 2007 Crown Copyright

larger grid has a significant impact on the time taken to run.Additionally two grids overlapping may be used. An outergrid with a larger spacing and an inner grid with a smallerspacing. For each event the numbers of persons affected inboth grids are summed. This gives greater accuracy forthe calculations while reducing speed impact. Where theinner grid overlaps the outer grid, the outer grid cells areset to zero. The inner and outer grid sizes are defined onthe setup sheet and it is indicated whether just the outer orboth inner and outer grids are used. Geographical infor-mation systems (GIS) can be used to generate populationdata. Within the HSE this is typically from HSE’s NationalPopulation Database tool (Balmforth, 2005 and Reston,2005). This supplies the necessary grid of population sur-rounding a site from a given location. This grid is theninserted onto a sheet and the grid size and the position ofthe site specified.

Figure 3. Gener

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When the code is run, the workbook generates a sheetfor each scenario, filling in all the information from thesetup sheet and population grid. These individual sheetsare then calculated giving a set of f(N) and N pairs forthat scenario. All the set of results are then copied to aresults sheet and sorted. The cummulative F values arethen calculated for each N. The point at which the slopeof –1 replaces the left hand part of the F-N curve dependson the choice of calculation. If 10% of NMAX is taken asthe cut off point then this is calculated and the closestpoint found. If the tangent of the slope of –1 to the F-Ncurve is taken then this point on the curve is determinedin the code and becomes the cut off point. The F pointsare then calculated from this cut off point down to Nequals 1. The F-N curve is then generated as shown inFigure 3. The EV and RICOMAH values are calculatedfrom this data.

ated F-N curve

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RISK REDUCTIONAs the QuickFN tool is relatively quick to run it can be usedto analyse an F-N curve including sensitivity studies.This could be in the form of changing a specific scenariosuch as decreasing a storage quantity or changing the situ-ation such as increasing population in particular areas.

Rankings of individual scenarios or groups ofscenarios contributing to the overall F-N curve can also beproduced. This allows the consideration of where riskreduction measures might be most effective. This wouldusually be from a fuller set of scenarios than that used forthe application of the QuickFN method as a ranking/screen-ing tool. There are two approaches available to considerranking of scenarios using QuickFN.

The first breaks the curve down into component scen-arios and component groups and displays these as a series ofcurves to allow the visual indication of which areas contrib-ute more to the QuickFN curve. The EV and RICOMAH arealso calculated for each component.

Figure 4 shows the F-N curves for the groups of scen-arios given in Table 1. Table 2 below lists the results for thenumerical analysis of the groups presented in a sheet in thespreadsheet. This lists the groups with the EV, the percen-tage contribution to the EV for the full F-N curve, total fre-quency for that group, NMAX, NAVG (scenario expectationvalue divided by its event frequency) and RICOMAH.

Similar results are generated for each componentscenario, leading to a graph with many more plots and alonger table of results. Visual inspection of the curves canbe used to determine those scenarios contributing to the fre-quency most if higher up and those scenarios contributing tohigh N if further right on the plot.

The second method produces simplified representa-tive f-N plots using a more complex approach, assumingthe consequences of events follow a log-normal distributionabout the mean. A three stage process is used to reduce the

Figure 4. Plot of F-N cu

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data for contributing scenarios of the FN curve to a simpli-fied form to improve clarity. The results are presented asgraphs showing the contribution of each group of scenariosto the FN curve. Each graph has discreet points that showapproximately the upper and lower significant f-N pairsfor the group of scenarios which gives some indication of

. the range of N for the scenarios,

. the contribution to the NMAX,

. an indication also of the contribution to likelihood whichalso indicates which part of the FN curve the group con-tributes most to,

. and this contribution relative to the other groups.

These discrete points are generated for each scenario.The points of these shapes are then combined together intothe scenario grouping to give the characteristic shapes asshown in Figure 5.

The first stage is to recalculate all the scenarios with aprecision of 1 and not limiting N to integers (i.e. allowingfractional N values). This is used to generate the upperand lower values from the calculated f-N points. The basisof the points used was originally from 4 weather conditionsbut it was found that all the intermediate points could be cal-culated from the upper and lower points providing the totalEV remains the same. The f-N pairs are sorted ascending onN and the fourth point is taken as the lower value (thesecond lowest F2 point), and the upper two points (thehighest F2 and D5 points) are averaged to give the uppervalue. If the lower value is zero it is set to 0.001 forconsistency.

The second stage is to calculate the intermediatepoints and distribute the frequency for the scenariobetween these points. There are 7 points for each scenario.The natural logarithm of the upper and lower values aretaken to give the first and last points. The remaining fivepoints are calculated from this using a relationship with

rves for scenario groups

Table 1. Scenarios with groups for risk ranking

Scenarios group Ref Cont/inst

Freq

(1026)

Size

(te)

Rate

(kg/s)

Duration

(mins) Material

Storage vessels Catastrophic full inst 1 25 cl

Storage vessels Catastrophic half inst 1 12.5 cl

Liquid space 50 mm cont 2 44 9.5 cl

Liquid space 25 mm cont 2 11 30 cl

Liquid space 13 mm cont 4 2.8 30 cl

Liquid space 6 mm cont 16 0.69 30 cl

Vapour space 50 mm cont 3 2.63 30 cl

Vapour space 25 mm cont 3 1.3 30 cl

Vapour space 13 mm cont 6 0.42 30 cl

Vapour space 6 mm cont 24 0.1 30 cl

RTCoupling guillotine ROV cont 58.2 10 5 cl

RTCoupling guillotine Manual cont 1.8 10 20 cl

RTCoupling 15 mm ROV cont 5.82 2.6 5 cl

RTCoupling 15 mm Manual cont 0.18 2.6 20 cl

RTCoupling 5 mm ROV cont 87.3 0.34 5 cl

RTCoupling 5 mm Manual cont 2.7 0.34 20 cl

Delivery pipework guillotine ROV cont 4.85 9.25 5 cl

Delivery pipework guillotine manual cont 0.15 9.25 20 cl

Delivery pipework 25 mm ROV cont 9.7 5.3 5 cl

Delivery pipework 25 mm manual cont 0.3 5.3 20 cl

Delivery pipework 3 mm ROV cont 19.4 0.12 5 cl

Delivery pipework 3 mm manual cont 0.6 0.12 20 cl

Delivery pipework flange ROV cont 19.4 1 5 cl

Delivery pipework 8.75 mm manual cont 0.6 1 20 cl

Process pipework guillotine ROV cont 9.7 14.2 5 cl

Process pipework guillotine manual cont 0.3 14.2 20 cl

Process pipework 25 mm ROV cont 19.4 3.6 5 cl

Process pipework 25 mm manual cont 0.6 3.6 20 cl

Process pipework 3 mm ROV cont 38.8 0.12 5 cl

Process pipework 3 mm manual cont 1.2 0.12 20 cl

Process pipework flange ROV cont 38.8 1 5 cl

Process pipework 8.75 mm manual cont 1.2 1 20 cl

IChemE SYMPOSIUM SERIES NO. 153 # 2007 Crown Copyright

the first and last point. The exponent is taken of all theseseven points to give the corresponding N values. The fvalues for each of these n values are calculated by splittingthe scenario frequency between the seven points with theupper and lower values being half the middle five values.The 7 points generated all give the same shape for each ofthe scenarios (values are different though). The combined

Table 2. Results f

Group EV EV % Freq

RTCoupling 457 35%

Liquid space 379 29%

Process pipework 180 14%

Storage vessels 176 14%

Delivery pipework 72 5%

Vapour space 34 3%

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f and n values from all of the scenarios can be used directlyto generate the F-N curve albeit with less contributingpoints, but with the same shape as the full F-N curve.

The third stage is to combine these seven fn points foreach scenario together into groups. This is done by aligningthe n values of each scenario in the group as closely as poss-ible. This can be done by inspection. The code aligns where

or scenario groups

total (1026) NMAX NAVG RICOMAH

156 294 3 1889

24 1359 16 3864

110 546 2 772

2 1200 88 1664

55 256 1 228

36 68 1 105

Figure 5. Plot of the representative f-N method for generic bulk chlorine installation

IChemE SYMPOSIUM SERIES NO. 153 # 2007 Crown Copyright

the most n values are as close to each other as possible. The fand n values are then used to generate an FN curve for eachgroup and it is these points that are plotted on the graph.

CONCLUSIONSThis paper has described the QuickFN methodology and itsimplementation as a software tool. HSE has developed thistool to assist in the assessment of societal risk from hazar-dous installations. A consistent set of assumptions can beapplied to each installation to allow meaningful compari-sons. The methodology is less resource intensive than afull QRA necessary to generate an F-N curve and measuresof risk integrals that are needed to compare societal risks tocriteria or for ranking. The resolution of the results is betterthan ARICOMAH although the range of situations is reducedas each material has to be parameterised and implemented.Some use of generics can increase the scenarios that can beconsidered.

REFERENCES1. Balmforth, H., McManus, H. and Fowler, A. (2005).

Assessment and management of ‘at risk’ populations

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using a novel GIS based UK population database tool.

Safety and Security Engineering.

2. Fowler, A. H. K., Reston, S., Carter, D. A., Quinn, D. and

Davies, P. (2004). QuickFN – a less resource intensive

methodology for determining the magnitude of societal

risks at major accident hazard installations. IChemE

Symposium Series No. 150, pp 627–634.

3. Hirst, I. L. and Carter, D. A. (2002), A ‘worst case’ meth-

odology for obtaining a rough but rapid indication of the

societal risk from a major accident hazard installation,

J. Haz. Mats. A92: 223–237.

4. Health and Safety Executive (2004) RR176 – Develop-

ment of an intermediate societal risk methodology, An

investigation of FN curve representation. HSE Books.

http://www.hse. gov.uk/research/rrhtm/rr176.htm).

5. Quinn, D. J. and Davies, P. A. (2003), Societal Risks:

An Investigation of FN Curve Representation, ERM

report, October 2003.

6. Reston, S., Maddison, T., Fowler, A. H. K. and Lawton,

M. (2005). Methods for determining risks from major

accident hazard installations. Centre For Chemcial

Process Safety 20th Annual International Conference,

Atlanta, Georgia, USA.