Scoring Severity of Injury — Hidden Probabilities

This entry is part 8 of 8 in the series Risk Assess­ment

I’ve been think­ing a lot about risk scor­ing tools and the algo­rithms that we use. One of the key ele­ments in risk is the Sever­i­ty of Injury. There are hid­den prob­a­bil­i­ties attached to the Sever­i­ty of Injury scores that are assigned that are not dis­cussed clear­ly in any of the risk assess­ment stan­dards that are com­mon­ly in use. This all start­ed when I was chal­lenged to write an analy­sis of the prob­lems with the CSA Risk Scor­ing Tool that you can find in the 2014 ver­sion of CSA Z434. That tool is deeply flawed in my opin­ion, but that is not the top­ic of this post. If you want to read my analy­sis, you can down­load the white paper and the pre­sen­ta­tion notes for my analy­sis from the Com­pli­ance inSight Pub­li­ca­tions page [1].

Scor­ing risk can be a tricky thing, espe­cial­ly in the machin­ery sec­tor. We rarely have much in the way of real-world data to use in the analy­sis, and so we are left with the opin­ions of those build­ing the machine as the basis for our eval­u­a­tion. Sever­i­ty is usu­al­ly the first risk para­me­ter to be esti­mat­ed because it’s seen as the “easy” one — if the char­ac­ter­is­tics of the haz­ard are well known. One aspect of sever­i­ty that is often missed is the prob­a­bil­i­ty of a cer­tain sever­i­ty of injury. We’re NOT talk­ing about how like­ly it is for some­one to be injured here; we’re talk­ing about the most like­ly degree of injury that will occur when the per­son inter­acts with the haz­ard. Let me illus­trate this idea anoth­er way: Let’s call Sever­i­ty “Se”, any spe­cif­ic injury “I”, and the prob­a­bil­i­ty of any spe­cif­ic injury “Ps”. We can then write a short equa­tion to describe this rela­tion­ship.

Se f (I,Ps)

Since we want there to be a pos­si­bil­i­ty of no injury, we should prob­a­bly relate these para­me­ters as a prod­uct:

Se = I x Ps

Ok, so what? What this equa­tion says is: the Sever­i­ty (Se) of any giv­en injury (I), is the prod­uct of the spe­cif­ic type of injury and the prob­a­bil­i­ty of that injury. More sim­ply yet, you could say that you should be con­sid­er­ing the most like­ly type of injury that you think will occur when a per­son inter­acts with the haz­ard. Con­sid­er this exam­ple: A work­er enters a robot­ic work cell to change the weld tips on the weld­ing gun the robot uses. This task has to be done about once every two days. The entry gate is inter­locked, and the robot was locked out before entry. The floor of the work cell has wire­ways, con­duits and pip­ing run­ning across it from the edges of the cell to the var­i­ous pieces of equip­ment inside the cell, cre­at­ing uneven foot­ing and lots of slip and trip haz­ards. The work­er miss­es his foot­ing and falls. What can you expect for Se in this case?

We know that falls on the same lev­el can lead to fatal­i­ties, about 600/year in the USA [2], but that these are most­ly in the con­struc­tion and min­ing sec­tors rather than gen­er­al man­u­fac­tur­ing. We also know that bro­ken bones are more like­ly than fatal­i­ties in falls to the same lev­el. About a mil­lion slips and falls per year result in an emer­gency room vis­it, and of these, about 5%, or 50,000, result in frac­tures. Ok, so what do we do with this infor­ma­tion? Let’s look at typ­i­cal sever­i­ty scale, this one tak­en from IEC 62061 [3].

Table 1 – Sever­i­ty (Se) clas­si­fi­ca­tion [2, Table A.1]

Con­se­quences Sever­i­ty (Se)
Irre­versible: death, los­ing an eye or arm 4
Irre­versible: bro­ken limb(s), los­ing a finger(s) 3
Reversible: requir­ing atten­tion from a med­ical prac­ti­tion­er 2
Reversible: requir­ing first aid 1

Using Table 1, we might come up with the fol­low­ing list of pos­si­ble sever­i­ties of injury. This list is not exhaus­tive, so feel free to add more.

Table 2 — Poten­tial Injury Sever­i­ties

Pos­si­ble Injury Sever­i­ty (Se)
Fall on same lev­el — Fatal­i­ty 4
Fall on same lev­el — Bro­ken wrist 3
Fall on same lev­el — Bro­ken col­lar­bone 3
Fall on same lev­el — Torn rota­tor cuff 2
Fall on same lev­el — Bruis­es 1
Fall on same lev­el — Head Injury 3
Fall on same lev­el — Head Injury 4

How do we score this using a typ­i­cal scor­ing tool? We could add each of these as line items in the risk reg­is­ter, and then assess the prob­a­bil­i­ty of each, but that will tend to cre­ate huge risk reg­is­ters with many line items at very low risks. In prac­tice, we decide on what we think is the most like­ly degree of injury BEFORE we score the risk. This results in a sin­gle line item for the haz­ard, rather than sev­en as would be the case if we scored each of these poten­tial injuries indi­vid­u­al­ly.

We need a prob­a­bil­i­ty scale to use in assess­ing the like­li­hood of injuries. At the moment, no pub­lished scor­ing tool that I know of has a scale for this, so let’s do the sim­ple thing: Prob­a­bil­i­ty (Ps) will be scored from 0–100%, with 100% being a cer­tain­ty.

Going back to the sec­ond equa­tion, what we are real­ly doing is assign­ing a prob­a­bil­i­ty to each of the sever­i­ties that we think exist, some­thing like this:

Table 3 — Poten­tial Injuries and their Prob­a­bil­i­ties

Pos­si­ble Injury (I) Sever­i­ty (Se) Prob­a­bil­i­ty (Ps)
Fall on same lev­el — Fatal­i­ty 4  0.0075%
Fall on same lev­el — Bro­ken wrist 3  5%
Fall on same lev­el — Bro­ken col­lar­bone 3  5%
Fall on same lev­el — Torn rota­tor cuff 2  5%
Fall on same lev­el — Bruis­es 1  90%
Fall on same lev­el — Head Injury 3 1%
Fall on same lev­el — Head Injury 4   0.0075%
Fall on same lev­el — Lac­er­a­tions to hands 2 90%

The per­cent­ages for fatal­i­ties and frac­tures we tak­en rough­ly from [1]. Ok, so we can look at a table like this and say that cuts and bruis­es are the most like­ly types of injury in this case. We can either decide to group them for the over­all risk score, or we can score each indi­vid­u­al­ly, result­ing in adding two sep­a­rate line items to the risk reg­is­ter. I’m going to use the oth­er para­me­ters from [2] for this exam­ple, and devel­op an exam­ple risk reg­is­ter, Table 4. In Table 4,

Se = Sever­i­ty

Pr = Prob­a­bil­i­ty of the Haz­ardous Event

Fr = Fre­quen­cy and Dura­tion of Expo­sure

Av = Pos­si­bil­i­ty to Avoid or Lim­it Harm

The algo­rithm I am using to eval­u­ate the risk is R = Se x [Pr x (Fr + Av)] [1]. Note that where I have com­bined the two poten­tial injuries into one line item (Item 1 in the reg­is­ter), I have select­ed the high­est sever­i­ty of the com­bined injuries. The less like­ly sever­i­ties, and in par­tic­u­lar the fatal­i­ties, have been ignored. You can click on  Table 4 to see a larg­er, more read­able ver­sion.

Table 4 - Example Risk Register
Table 4 — Exam­ple Risk Reg­is­ter

Note that I did not reduce the Se scores in the Final Risk Score, because I have not made changes to the slip/trip and fall haz­ards, only to the like­li­hood of the injury occur­ring. In all cas­es, we can show a sig­nif­i­cant risk reduc­tion after mit­i­ga­tion. I’m not going to get into risk eval­u­a­tion (i.e., Is the risk effec­tive­ly con­trolled?) in this par­tic­u­lar arti­cle, but the fact that you can show a sig­nif­i­cant risk reduc­tion is impor­tant. There are lots of con­sid­er­a­tions in deter­min­ing if the risk has been effec­tive­ly con­trolled.


Con­sid­er­a­tion of the prob­a­bil­i­ty of cer­tain kinds of injuries occur­ring must be con­sid­ered when esti­mat­ing risk. This process is large­ly undoc­u­ment­ed but nev­er­the­less occurs. When risk ana­lysts are con­sid­er­ing the sever­i­ty of injury from any giv­en haz­ard, this arti­cle gives the read­er one pos­si­ble approach than could be used to select the types of injuries most like­ly to occur before scor­ing the rest of the risk para­me­ters.


[1] D. Nix, ‘Eval­u­a­tion of Prob­lems and Chal­lenges in CSA Z434-14 Annex DVA Task-Based Risk Assess­ment Method­ol­o­gy’, 2015.

[2] Nation­al Floor Safe­ty Insti­tute (NFSI), ‘Quick Facts — Slips, Trips, and Falls’, 2015. [Online]. Avail­able: [Accessed: 21- Jul- 2015].

[3] ‘Safe­ty of machin­ery – Func­tion­al safe­ty of safe­ty-relat­ed elec­tri­cal, elec­tron­ic and pro­gram­ma­ble elec­tron­ic con­trol sys­tems. IEC 62061.’, Inter­na­tion­al Elec­trotech­ni­cal Com­mis­sion (IEC), Gene­va, 2005.


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The Probability Problem

This entry is part 9 of 8 in the series Risk Assess­ment

In Occu­pa­tion­al Health and Safe­ty (OHS), risk is a func­tion of sever­i­ty of injury and the prob­a­bil­i­ty of the injury occur­ring. Under­stand­ing the sever­i­ty por­tion is usu­al­ly fair­ly easy, although advanced tech­nolo­gies, like lasers for instance, take advanced haz­ard analy­sis meth­ods to assess cor­rect­ly. Risk vs. Cost and EffortProb­a­bil­i­ty, on the oth­er hand, is a  chal­lenge. Math­e­mat­i­cal­ly, prob­a­bil­i­ty cal­cu­la­tions go from sub­lime­ly sim­ple to insane­ly com­plex. The sim­plest forms, like the prob­a­bil­i­ty of get­ting a num­ber from 1 to 6 when you throw a sin­gle die, can be under­stood pret­ty eas­i­ly. On any one throw of the die, you have a 1 in 6 chance of get­ting any one par­tic­u­lar num­ber, assum­ing the die is not weight­ed in any way. When we’re talk­ing about OHS risk, it’s nev­er that easy.

The First Problem: No Data

Risk assess­ment can be done quan­ti­ta­tive­ly, that is, using numer­ic data. This approach is often tak­en when numer­ic data is avail­able, or where rea­son­able numer­ic esti­mates can be made. The prob­lem with using numer­ic esti­mates in quan­ti­ta­tive assess­ments, is this: Math lends cred­i­bil­i­ty to the answer for most peo­ple. Con­sid­er these two state­ments:

  1. After ana­lyz­ing the avail­able infor­ma­tion, we believe that the risk is pret­ty low, because it is unlike­ly that the reac­tor will melt down.
  2. After ana­lyz­ing the avail­able infor­ma­tion, we believe that the risk of a fatal­i­ty in the sur­round­ing pop­u­la­tion is very low, because the prob­a­bil­i­ty that the reac­tor will melt down is less than 1 in 1 mil­lion.

Which of these state­ments sounds more ‘cor­rect’ or more ‘author­i­ta­tive’ to you?

Attach­ing num­bers to the state­ment makes it sound more author­i­ta­tive, even if there is no reli­able data to back it up! If you are going to attempt to use quan­ti­ta­tive esti­mates in a risk assess­ment, be sure you can back the esti­mates up with ver­i­fi­able data. Fre­quent­ly there is no numer­ic data, and that forces you to move from a quan­ti­ta­tive approach to semi-quan­ti­ta­tive approach, mean­ing that num­bers are assigned to esti­mates, usu­al­ly on a scale, like 1–5 or 1–10 rep­re­sent­ing least like­ly to most like­ly, or a ful­ly qual­i­ta­tive approach, mean­ing that the scales are only descrip­tive, like ‘unlike­ly, like­ly, very like­ly’. These kinds of assess­ments are much eas­i­er to make as long as the scales used are well designed, with clear descrip­tions for each incre­ment in the scale, because the data used in the assess­ment is the opin­ion of the asses­sors. Here’s an exam­ple, tak­en from Chris Steel’s 1990 arti­cle [1]:

Table 1: LO Like­li­hood of Occur­rence
Scale Val­ue
Impos­si­ble, can­not hap­pen
Almost impos­si­ble, pos­si­ble in extreme cir­cum­stances
High­ly unlike­ly, though con­ceiv­able
Unlike­ly, but could occur
Pos­si­ble, but unusu­al
Even chance, could hap­pen
Prob­a­ble, not sur­prised
Like­ly, to be expect­ed
Cer­tain, no doubt

Some peo­ple might say that this scale is too com­plex, or that the descrip­tions are not clear enough. I know that the sub­tleties some­times get lost in trans­la­tion, as I dis­cov­ered when try­ing to train a group of non-native-eng­lish-speak­ing engi­neers in the use of the scale. Lin­guis­tic chal­lenges can be a major hur­dle to over­come! Sim­pler scales, like that used in CSA Z432 [2], can be eas­i­er to use, but may result in gaps that are not eas­i­ly dealt with. For exam­ple:

Table 2: Avoid­ance [2,Table A.2]
Not like­ly
Can­not move out of way; or inad­e­quate reac­tion time; or machine speed greater than 250 mm/s.
Can move out of way; or suf­fi­cient warning/reaction time; or machine speed less than 250 mm/s.

A scale like the pre­vi­ous one may not be spe­cif­ic enough, or fine enough (some­times referred to as  ‘gran­u­lar­i­ty’, or in this case ‘gran­u­lar enough’) too be real­ly use­ful. There are soft­ware pack­ages for risk assess­ment avail­able as well. One pop­u­lar prod­uct called CIRSMA™, uses a scale that looks like this:

Table 3: Prob­a­bil­i­ty of the Haz­ardous Event
Para­me­ter Selec­tion
Pos­si­ble — Eas­i­ly able to avoid
Nor­mal­ly used to describe haz­ardous motions or events that:
  • Occur in plain view of the exposed per­son, and occur at a speed of less than 125 mm / sec­ond;
  • Can be read­i­ly fore­seen or detect­ed by the exposed per­son before the haz­ardous event occurs;
  • Are a result of the actions of the antic­i­pat­ed exposed per­son and are under the direct con­trol of the exposed per­son; and
  • Cir­cum­stances can be eas­i­ly and read­i­ly mod­i­fied or cor­rect­ed to avoid harm once a haz­ardous sit­u­a­tion has mate­ri­al­ized.
Pos­si­ble — Poten­tial­ly able to avoid
Nor­mal­ly used to describe haz­ardous motions or events that:
  • Occur in plain view of the exposed per­son, and occur at a speed of more than 125 mm / sec­ond but less than 250 mm / sec­ond;
  • Could pos­si­bly be fore­seen or detect­ed by the exposed per­son before the haz­ardous event occurs;
  • Are a result of the actions of the antic­i­pat­ed exposed per­son and are par­tial­ly under the con­trol of the exposed per­son; and
  • Cir­cum­stances can pos­si­bly be mod­i­fied or cor­rect­ed in order to avoid harm once a haz­ardous sit­u­a­tion has mate­ri­al­ized.
Unlike­ly — Unable to Avoid
Nor­mal­ly used to describe haz­ardous motions/events that:
  • Occur either in plain view of the exposed per­son and occur at a speed of more than 250 mm / sec­ond or not in plain view of the exposed per­son and occur at a speed of less than 250 mm / sec­ond;
  • Are not like­ly to be fore­seen or detect­ed by the exposed per­son before the haz­ardous event occurs; and
  • Are not a result of the actions of the antic­i­pat­ed exposed per­son but could be par­tial­ly under the con­trol of the exposed per­son.
Impos­si­ble — Injury is Unavoid­able
Nor­mal­ly used to describe haz­ardous motion/events that:
  • Regard­less of the loca­tion of the haz­ard, occur at such a speed that the exposed per­son would have lit­tle or no oppor­tu­ni­ty to escape harm;
  • Could not be fore­seen or detect­ed by the exposed per­son before the haz­ardous event occurs;
  • Are not a result of the actions of the antic­i­pat­ed exposed per­son and are not under the con­trol of the exposed per­son; and
  • Cir­cum­stances can­not to be mod­i­fied or cor­rect­ed in order to avoid harm once a haz­ardous sit­u­a­tion has mate­ri­al­ized.

A scale like this is more descrip­tive than the CSA scale, but less gran­u­lar and a bit eas­i­er to use than the Steel table.

Prob­a­bil­i­ty is also influ­enced by Fre­quen­cy of Expo­sure to the haz­ard, and each of the tools men­tioned above have scales for this para­me­ter as well. I’m not going to spend any time on those scales here, but know that they are sim­i­lar to the ones dis­played in terms of gran­u­lar­i­ty and clar­i­ty.

The Second Problem: Perception

This is the real­ly big prob­lem, and it’s one that even the great­est minds in risk assess­ment and com­mu­ni­ca­tion have yet to solve effec­tive­ly. Peo­ple judge risk in all sorts of ways, and the human mind has an out­stand­ing abil­i­ty to mis­lead us in this area. In a recent arti­cle pub­lished in the June-2012 issue of Man­u­fac­tur­ing Automa­tion Mag­a­zine, Dick Mor­ley talks about the ‘Mon­ty Hall prob­lem’ [3]. In this arti­cle, Mor­ley quotes colum­nist Mar­i­lyn vos Savant from her ‘Ask Mar­i­lyn’ col­umn in Parade Mag­a­zine:

Sup­pose you’re on a game show and you are giv­en the choice of three doors. Behind one door is a car, behind the oth­ers, goats. You pick a door, say, num­ber one (but the door is not opened). And the host, who knows what’s behind the doors, opens anoth­er door, say, num­ber three, which has a goat. He says to you, ‘Do you want to pick door num­ber two?’ Is it to your advan­tage to switch your choice?”

Here is where things start to go astray. If you keep your orig­i­nal choice, your chance of win­ning the car is 1:3, since the car could be behind any of the three doors. If you change your mind, your chances of win­ning the car become 2:3, since you know what is behind one door, and could the­o­ret­i­cal­ly choose that one, or choose one of the oth­er two. Since you know for cer­tain that a goat is behind door three, that choice is guar­an­teed. Choose Door Three and get a goat. But if you choose to change your deci­sion, your chances go from 33% to 66% in one move, yet most peo­ple get this wrong. Math­e­mat­i­cal­ly it’s easy to see, but humans tend to get emo­tion­al­ly dis­tract­ed at times like this, and make the wrong choice. Accord­ing to Mor­ley, stud­ies show that pigeons are actu­al­ly bet­ter at this than humans! When we start to talk about risk in abstract num­bers, like ‘one fatal­i­ty per year per 1 mil­lion pop­u­la­tion’ or stat­ed anoth­er way, ‘1 x 10-6 fatal­i­ties per year’ [4], peo­ple lose track of what this could mean. We like to our­selves with time frame attached to these things, so we might tell our­selves that, since it’s June now and no one has died, that some­how the risk is actu­al­ly half of what was stat­ed, since half the year is gone. In fact, the risk is exact­ly the same today as it was on Jan­u­ary 1, assum­ing noth­ing else has changed.

In a recent court case involv­ing a work­place fatal­i­ty, one expert wit­ness devel­oped a the­o­ry of the risk of the fatal­i­ty using the Human Fac­tors approach com­mon­ly used in the process and nuclear indus­tries. Using esti­mates that had no sup­port­ing data, he came to the con­clu­sion that the like­li­hood of a fatal­i­ty on this par­tic­u­lar machine was 1 x 10-8, or rough­ly two orders of mag­ni­tude less than being hit by light­ning. In an inter­nal HSE report in the UK the fol­low­ing chart In OHS, we believe that if a haz­ard exists, it will even­tu­al­ly do harm to some­one, as it did in this case. We know with­out a doubt that a fatal­i­ty has occurred. The manufacturer’s sales depart­ment esti­mat­ed that there were 80–90 units of the same type in the mar­ket­place at the time of the fatal­i­ty. If we use that esti­mate of the num­ber of that mod­el of machine in the mar­ket­place, we could cal­cu­late that the risk of a fatal­i­ty on that mod­el as 1:80 or 1:90 (8 x 10-1 or 9 x 10-1), sig­nif­i­cant­ly greater than the risk of being struck by light­ning, and more than sev­en orders of mag­ni­tude more than esti­mat­ed by the expert wit­ness. Esti­mat­ing risk based on unproven data will result in under­es­ti­ma­tion of the risk and over­con­fi­dence in the safe­ty of the work­ers involved.


Once a risk assess­ment is com­plet­ed and the appro­pri­ate risk con­trols imple­ments fol­low­ing the Hier­ar­chy of Con­trols, the resid­ual risk must be com­mu­ni­cat­ed to the peo­ple who are exposed to the risk. This allows them to make an informed deci­sion about the risk, choos­ing to do the task, mod­i­fy the task or not do the task at all. This is called ‘informed con­sent’, and is exact­ly the same as that used by doc­tors when dis­cussing treat­ment options with patients. If the risk changes for some rea­son, the change also needs to be com­mu­ni­cat­ed. Com­mu­ni­ca­tion about risk helps us to resist com­pla­cen­cy about the risks that we deal with every day, and helps to avoid con­fu­sion about what the risk ‘real­ly is’.

Risk Perception

Risk per­cep­tion is an area of study that is try­ing to help us to bet­ter under­stand how var­i­ous kinds of risks are per­ceived, and per­haps how best to com­mu­ni­cate these risks to the peo­ple who are exposed. In a report pre­pared at the UK’s Health and Safe­ty Lab­o­ra­to­ry in 2005 [5], authors Williams and Wey­man dis­cuss sev­er­al ways of assess­ing risk per­cep­tion.

One approach, described by Renn [6], attempts to chart four dif­fer­ent aspects of risk per­cep­tion in people’s think­ing.

Perspectives on Risk
Fig. 1 — Graph­ing Risk Per­cep­tion Fac­tors

An exam­ple of these fac­tors plot­ted on a graph is shown in Fig. 2 below. The data points plot­ted on the chart are devel­oped by sur­vey­ing the exposed pop­u­la­tion  and then chart­ing the fre­quen­cy of their respons­es to the ques­tions.

Fig. 2 — Graph­ing ‘Dread’

There are two fac­tors chart­ed on this graph. On the ver­ti­cal axis, ‘Fac­tor 2’ is the per­cept­abil­i­ty of the risk, or how eas­i­ly detect­ed the risk is. On the hor­i­zon­tal axis is ‘Fac­tor 1 — The Dread Risk’, or how much ‘dread’ we have of cer­tain out­comes. In Fig. 3 below you can see the assign­ment of fac­tors to the pos­i­tive and neg­a­tive direc­tions on these axes.

Fig. 3 — Dread Fac­tors



At this point, I can say that we are a long way from being able to use this approach effec­tive­ly when con­sid­er­ing machin­ery safe­ty, but as prac­ti­tion­ers, we need to being to con­sid­er these approach­es when we com­mu­ni­cate risk to our cus­tomers, users and work­ers.


When you are think­ing about risk, it’s impor­tant to be clear about the basis for the risk you are con­sid­er­ing. make sure that you are using valid, ver­i­fi­able data, espe­cial­ly if you are cal­cu­lat­ing a numer­ic val­ue to rep­re­sent the prob­a­bil­i­ty of risk. Where numer­ic data isn’t avail­able, use the semi-quan­ti­ta­tive and qual­i­ta­tive scor­ing tools that are avail­able to sim­pli­fy the process and enable you to devel­op sound eval­u­a­tions of the risk involved.

Need more help? Con­tact me! Doug Nix


[1]     C. Steel. “Risk esti­ma­tion.” The Safe­ty and Health Prac­ti­tion­er, pp. 20–22, June, 1990.

[2]     Safe­guard­ing of Machin­ery, CSA Stan­dard Z432-1994 (R1999).

[3]     R. Mor­ley. “Ana­lyz­ing Risk: The Mon­ty Hall prob­lem.” Man­u­fac­tur­ing Automa­tion, June, 2012. p.26.

[4]     J. D. Rim­ing­ton and S. A. Har­bi­son, “The Tol­er­a­bil­i­ty of Risk from Nuclear Pow­er Sta­tions,” Health and Safe­ty Exec­u­tive, Her Majesty’s Sta­tion­ary Office, Lon­don, UK, 1992.

[5]     J. Williamson and A. Wey­man, “Review of the Pub­lic Per­cep­tion of Risk, and Stake­hold­er Engage­ment”,  The Crown, Lon­don, UK. Rep. HSL/2005/16, 2005.

[6]     O. Renn, “The Role of Risk Per­cep­tion for Risk Man­age­ment.” in P.E.T. Douben (ed.): Pol­lu­tion Risk Assess­ment and Man­age­ment. Chich­ester et. al (John Wiley & Sons 1998), pp. 429–450

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