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Handling Hazards: natural and otherwise University of Waterloo Ontario, Canada December 3rd 2010 Richard Wilson Mallinckrodt Professor of Physics (emeritus)

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Presentation on theme: "Handling Hazards: natural and otherwise University of Waterloo Ontario, Canada December 3rd 2010 Richard Wilson Mallinckrodt Professor of Physics (emeritus)"— Presentation transcript:

1 Handling Hazards: natural and otherwise University of Waterloo Ontario, Canada December 3rd 2010 Richard Wilson Mallinckrodt Professor of Physics (emeritus) Harvard University

2 We always want to find a cause.. Usually to blame some one. (often to collect money!) “Come, and let us cast lots, that we may know for whose cause this evil is upon us” “and the lot fell on Jonah” Jonah 1:7

3 1000 years ago various jurisdictions tortured a suspect. If he died under torture: too bad If he confessed he was guilty.

4 As late as 1950 UK common law considered Acts of God differently from acts of man A brick falling from the roof was an Act of God with no one to blame UNLESS You had put up a warning sign: beware of falling bricks. Then you knew it was dangerous

5 Perrenial conflict Rights of an individual vs Rights of Society This conflict must be faced: not ignored Quarantine vaccination Profiling (racial or otherwise)

6 Since about 1970 analysts do not make the distinction Natural Hazards can be analyzed and precursers found Prevention of adverse consequences is similar for natural and man made hazards

7 The Biggest Risk to Life is Birth. Birth always leads to death! We talk about premature death. Polls say Risk is Increasing (next slide) but history says the opposite. What do the polls mean?

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10 Note the little peak about What is it due to? First world war? NO! Flu epidemic! On the next slide we see the double dip in life expectancy for France. For France flu was as bad as the war!

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12 WHAT IS LIFE EXPECTANCY ? An artificial construct assuming that the probability of dying as one ages is the same as the fraction of people dying at the same age at the date of one’s birth.

13 The specific death rate Peaks, and the life expectancy at birth has a dip at 1919 world wide influenza epidemic. BUT anyone born in 1919 will not actually see this dip. Peculiarity of definition of life expectancy

14 Half the “Beijing men’ were teenagers. This puts life expectancy about 15 Roman writings imply a life expectancy of 25. Sweden started life expectancy statistics early. Russia has been going down since 1980

15 Risk is Calculated in Different Ways and that influences perception and decisions. (1) Historical data (2) Historical data where Causality is difficult (3) Analogy with Animals (4) Event tree if no Data exist

16 Risk is different for different measures of risk. Different decision makers will use different measures depending on their constituency

17 RISK MEASURES (continued) Loss of Life Expectancy (LOLE) Years of Life Lost (YOLL) Man Days Lost (MDL) Working Days Lost (WDL) Public Days Lost (PDL) Quality Adjusted Life Years (QALY) Disability Adjusted Life Years (DALY) Different decisions may demand different measures

18 LOLE from cigarette smoking In USA 600 billion cigarettes made (presumably smoked) 400,000 people have premature death (lung cancer, other cancers, heart) 1,500,000 cigarettes per death Each death takes about 17 years (8,935,200 minutes) off life or 6 minutes per cigarette ABOUT THE TIME IT TAKES TO SMOKE ONE (easy to remember)

19 Risks calculated from History seems simple. BUT The number of people dying and the number of persons in the risk pool often come from different data bases. Also units are often different

20 Forecasting the future based upn the past Weather forecasting vs Fortune telling 1835 UK vagrancy act: fortune telling illegal Islam had a similar restriction Scientists claim they are different BUT 2008 Climate gate runs into the same problem.

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22 Risks of New Technologies Old fashioned approach. Try it. If it gives trouble, fix it. E.g The first passenger railroad (Liverpool to Manchester) killed (a member of parliament) on the first day!

23 Risks of New technologies We now want more safety New technologies can kill more people at once. We do not want to have ANY history of accidents.

24 Plot an EVENT TREE Start with an accident initiator Consider a way of mitigation effects What happens when that does not work? First done for nulear power: Rasmussens's reactor safety study 1975

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26 We try to get each step independent of the others. (Design a reactor that way) Then the probabilities MULTIPLY

27 Accident frequency = P1 X P2 X P3 X P4 Flood Earthquake Sabotage (terrorism) can couple to steps TTHIS HELPS US TO FOCUS

28 Evacuation Plan April 27th

29 1100 buses left Kiev April 27th

30 The event tree analysis SHOULD have been used by NASA in the 1980s and it would have avoided the Challenger disaster

31 LNG facilities Chemical refinery accidents NASA adopt these procedures HOPEFULLY SOON Building Industry Oil well drilling

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37 Annual Occupation Fatality Rates (US)

38 Epidemiology Associate Death (or other Measure) to Postulated Cause Is it statistically significant? Are there alternative causes (confounders)? THINK. No case where cause is accepted unless there is a group where death rate has doubled. Risk Ratio (RR) > 2

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40 We contrast two types of medical response to pollutants. ACUTE TOXIC EFECT A dose within a day causes death within a few days (causality easy to establish) CHRONIC EFFECT lower doses repeated give chronic effects (cancer, heart) within a lifetime. (Causality hard to establish)

41 ANALOGY of animals and humans Rodents do not look like people Start with Acute toxic effects data from paper of Rhomberg and Wolf (next slide)

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43 Two problems in human diseases Effect is often delayed from the Cause then Causality is hard to prove. Proof of an effect is at high dose we want to know effect at low dose

44 Early Optimism Based on Poisons There is a threshold below which nothing happens __________ J.G. Crowther 1924 Probability of Ionizing a Cell is Linear with Dose

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47 CRITICAL ISSUES FOR LINEARITY at low doses THE POLLUTANT ACTS IN THE SAME WAY AS WHATEVER ELSE INFLUCENCES THE CHRONIC OUTCOME (CANCER) RATE CHRONIC OUTCOMES (CANCERS) CAUSED BY POLLUTANTS ARE INDISTINGUISHABLE FROM OTHER OUTCOMES implicit in Armitage and Doll (1954) explicit in Crump et al. (1976) extended to any outcome Crawford and Wilson (1996)

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49 Characteristics One dose or dose accumulated in a short time KILLS 1/10 the dose repeated 10 times DOES NOT KILL

50 Typically an accumulated Chronic Dose equal to the Acute LD 50 gives CANCER to 10% of the population. Assumed to be proportional to dose E.g. LD 50 for radiation is about 350 Rems. At an accumulated exposure of 350 Rems about 10% of exposed get cancer. What does that say for Chernobyl? (more or less depending on rate of exposure)

51 50% of all chemicals tested are carcinogenic in rodents Scientific issue is the dose and the risk Which are regulated is a political issue.

52 Holiday Dinner Menu showing the natural carcinogens that are present Appetizers CREAM OF MUSHROOM SOUP hydrazines CARROTS aniline, caffeic acid CHERRY TOMATOES benzaldehyde, caffeic acid, hydrogen peroxide, quercetin glycosides CELERY caffeic acid, furan derivatives, psoralens Assorted Nuts MIXED ROASTED NUTS aflatoxin, furfural Green Salad TOSSED LETTUCE AND ARUGULA WITH BASIL-MUSTARD VINAIGRETTE allyl isothiocyanate, caffeic acid, estragole, methyl eugenol Entrees ROAST TURKEY heterocyclic amines BREAD STUFFING (WITH ONIONS, CELERY, BLACK PEPPER & MUSHROOMS) acetaldehyde, ethyl alcohol, benzo(a)pyrene, ethyl carbamate, furan derivatives, furfural, hydrazines, d-limonene, psoralens, quercetin glycosides, safrole CRANBERRY SAUCE furan derivatives

53 OR PRIME RIB OF BEEF WITH PARSLEY SAUCE benzene, heterocyclic amines, psoralens Vegetables BROCCOLI SPEARS allyl isothiocyanate BAKED POTATO ethyl alcohol, caffeic acid SWEET POTATO ethyl alcohol, furfural Bread BOLLS WITH BUTTER acetaldehyde, benzene, ethyl alcohol, benzo(a)pyrene, ethyl carbamate, furan derivatives, furfural Desserts PUMPKIN PIE benzo(a)pyrene, coumarin, methyl eugenol, safrole APPLE PIE acetaldehyde, caffeic acid, coumarin, estragole, ethyl alcohol, methyl eugenol, quercetin glycosides, safrole Fruit Tray FRESH APPLES, GRAPES, MANGOS, PEARS, PINEAPPLE acetaldehyde, benzaldehyde, caffeic acid, d-limonene, estragole, ethyl acrylate, quercetin glycosides Beverages RED WINE ethyl alcohol, ethyl carbamate COFFEE benzo(a)pyrene, benzaldehyde, benzene, benzofuran, caffeic acid, catechol, 1,2,5,6- dibenz(a)anthracene, ethyl benzene, furan, furfural, hydrogen peroxide, hydroquinone, d-limonene, 4-methyicatechol TEA benzo(a)pyrene, quercetin glycosides JAMINE TEA benzyl acetate

54 NATURALLY OCCURRING MUTAGENS AND CARCINOGENS FOUND IN FOODS AND BEVERAGES ACETALDEHYDE (apples, bread, coffee, tomatoes)-mutagen and potent rodent carcinogen AFLATOXIN (nuts)-mutagen and potent rodent carcinogen; also a human carcinogen ALLYL ISOTHIOCYANATE (arugula, broccoli, mustard)-mutagen and rodent carcinogen ANILINE (carrots)-rodent carcinogen BENZALDEHYDE (apples, coffee, tomatoes)-rodent carcinogen BENZENE (butter, coffee, roost beef-rodent carcinogen BENZO(A)PYRENE (bread, coffee, pumpkin pie, rolls, tea)-mutagen and rodent carcinogen BENZOFURAN (coffee)-rodent carcinogen BENZYL ACETATE jasmine tea)-rodent carcinogen CAFFEIC ACID (apples, carrots, celery, cherry tomatoes, coffee, grapes, lettuce, mangos, pears, potatoes)-rodent carcinogen CATECHOL (coffee)-rodent carcinogen COUMARIN (cinnamon in pies)-rodent carcinogen 1,2,5,6-DIBENZ(A)ANTHRACENE (coffee)-rodent carcinogen ESTRAGOLE (apples, basil)-rodent carcinogen ETHYL ALCOHOL (bread, red wine, rolls)-rodent and human carcinogen ETHYL ACRYLATE (pineapple)-rodent carcinogen ETHYL BENZENE (coffee)-rodent carcinogen ETHYL CARBAMATE (bread, rolls, red wine)-mutagen and rodent carcinogen FURAN AND FURAN DERIVATIVES (bread, onions, celery, mushrooms, sweet potatoes, rolls, cranberry sauce, coffee)- (many are mutagens) FURFURAL (bread, coffee, nuts, rolls, sweet potatoes)-furan derivative and rodent carcinogen HETEROCYCLIC AMINES (roast beef, turkey)-mutagens and rodent carcinogens HYDRAZINES (mushrooms)-mutagens and rodent carcinogens HYDROGEN PEROXIDE (coffee, tomatoes)-mutagen and rodent carcinogen HYDROQUINONE (coffee)-rodent carcinogen D-LIMONENE (black pepper, mangos)-rodent carcinogen 4-METHYLCATECHOL (coffee)-rodent carcinogen METHYL EUGENOL (basil, cinnamon and nutmeg in apple and pumpkin pies)-rodent carcinogen PSORALENS (celery, parsley)-mutagens, rodent and human carcinogens QUERCETIN GLYCOSIDES (apples, onions, tea, tomatoes)-mutagens and rodent carcinogens SAFROLE (nutmeg in apple and pumpkin pies, black pepper)-rodent carcinogen

55 You MUST do the best engineering calculation you can BUT if you cannot express it simply People with think you dont understand it AND THEY ARE PROBABLY RIGHT

56 Example: Risk of a Space Probe major risk: Probe (powered by Plutonium) reenters the earth’s atmosphere burns up spreads its plutonium widely over everyone Causes an increase in lung cancer

57 3 Steps (1) What is the probability of reentry (2) What is the distribution of Plutonium (3) Compare with what we know

58 Probability of probe hitting the earth’s atmoshere in swing-by Orbit calculated can be corrected. No large correction (avoiding saboteur) 1 in 10,000,000

59 Russian workers in Ozersk --10 microCurie Pu (measurement in skeleton) Double risk of lung cancer) Pu in atmoshere from bomb explosions leads to 30 picocurie in each of us 30,000 times less lung cancer of a heavy cigarette smoker. This multiplies the risk of the sattelite hitting the earth (P <10^-11) (Accurate calculation 10^-14)

60 ExAMPLE OF 2 POSSIBLE TERRORIST ACTIONS GO TO Vatican2009.ppt

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62 If the distribution of fiber type, size and shape is identical in the occupational epidemiological studies and in the environmental samples of interest, extrapolation to low doses is comparatively simple

63 Note that the incremental Risk can actually be greater than the simple linearity assumption of a non-linear biological dose- response is assumed

64 Assumptions for animal analogy with cancer: A man eating daily a fraction F of his body weight is as likely to get cancer (in his lifetime) as an animal eating daily the fraction f of his body weight.

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67 Transparency of Allen et al.

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