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Report Number: 11331 June 2011

Risk Levels for Customer Self Isolation and Restoration

Versus Established Industry Practice: Phase 2

Confidential, Restricted to: Scotia Gas Networks Access Restricted

& GL Noble Denton

Prepared for: Prepared by:

Scotia Gas Networks GL Industrial Services UK Ltd trading as GL Noble Denton

95 Kilbirnie Street, Holywell Park Glasgow, Ashby Road G5 8JD. Loughborough Leicestershire

LE11 3GR United Kingdom

Tel: Fax:

E-mail:

Website: www.gl-nobledenton.com

Customer Reference:

This Report is protected by copyright and may not be reproduced in whole or in part by any means without the approval in writing of GL Noble Denton. No Person, other than the Customer for whom it has been prepared, may place reliance on its contents and no duty of care is assumed by GL Noble Denton toward any Person other than the Customer. This Report must be read in its entirety and is subject to any assumptions and qualifications expressed therein. Elements of this Report contain detailed technical data which is intended for analysis only by persons possessing requisite expertise in its subject matter.

GL Noble Denton is the trading name of GL Industrial Services UK Ltd Registered in England and Wales No. 3294136 Registered Office: Holywell Park, Ashby Road, Loughborough, Leicestershire, LE11 3GR UK

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http:www.gl-nobledenton.com

Report Number: 11331 Issue: 1.0

Report Issue / Amendment Record

Report Title: Risk Levels for Customer Self Isolation and Restoration Versus Established Industry Practice: Phase 2

Report Number: 11331 Project Title: SGN - Risk Levels for Customer Self Isolation and Restoration

Project SAP Code: 1/17478

Amendment details

Issue Description of Amendment Originator/Author

2.0 Comments received from SGN addressed

2.1 Comments received from SGN addressed

Report approval

Issue Checked by Approved by Date

1.0 7th July 2011

2.0 26th July 2011

2.1 2nd August 2011

Previous issues of this document shall be destroyed or marked SUPERSEDED

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Project Code: 1/17478

Distribution

Name Company

Scotia Gas Networks

95 Kilbirnie Street, Glasgow, G5 8JD.

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Executive Summary

SGN have established a methodology of determining an appropriate approach for customer self-isolation and restoration following a gas supply outage. To utilise this methodology they have requested GL to determine:

• The predicted number of fatalities caused by gas incidents

• The predicted number of fatalities caused by the effects of weather

during a gas supply outage, for:

• Established approaches to managing the incident

• Alternative approaches to managing the incident.

This will involve generating 2 statistically robust graphs that can be used in conjunction with SGN’s methodology to determine the most appropriate approach to adopt during an incident.

A feasibility report was written which details the decision process related to a loss of supply incident, and indicates, at each stage, whether the probability of each stage occurring can be based upon existing data or engineering judgement. The purpose of this report was to determine the feasibility of modelling the process.

The second phase covered in this report, utilises all available information and assumptions to build the models that have been used to generate the required graphs. Scenarios and assumptions agreed under Phase 1 have been used within the model. A sensitivity analysis has also been carried out on the inputs. This report provides the required graphs and states all assumptions made, the reasoning behind the assumptions and the results of sensitivity analyses.

Conclusions

Predicted Number of Gas Safety Related Fatalities


Non-Damaging Contaminants Present

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000

Number of Properties where Gas Supply Failure Occurred

Nu

mb

er o

f F

atal

itie

s

Customer Self Isolation & Restoration Used Qualified Personnel used for Isolation and Restoration No Meter Isolation Carried Out

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Fatalities from Cold Weather

Predicted Number of Cold Weather Related Fatalities

0 10 20 30 40 50 60

Duration of Gas Supply Failure (Days)

Nu

mb

er o

f F

atal

itie

s / 1

000

Aff

ecte

d P

rop

erti

es

Band 1 : Below 5 ºC

Band 2 : 5 - 9 ºC

Band 3 : 9 - 12 ºC

Band 4 : 12 - 16 ºC

Band 5 : Above 16 ºC

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Contents

1 Introduction .................................................................................................................................. 1

2 Predicted Number of Gas Safety Related Fatalities .................................................................. 3

2.1 Data................................................................................................................................................... 3

2.1.1 Loss of Gas Supply..................................................................................................... 3

2.1.2 Customer 1: Customer Self Isolation & Restoration (Non-damaging Contaminants).. 4

2.1.3 Instructions C1: Customer Self Isolation & Restoration – Procedure when Appliances Shut Down (Non-damaging contaminants)........................................................................................ 6

2.1.4 Customer Absent: No Contact Made With Customer Prior to Reconnection .............. 7

2.1.5 Risk of Gas: Risk Resulting From Gas Release During Customer Absence .............. 9

2.1.6 Risk of Gas 2: Risk Resulting From Gas Release During Customer Presence ........ 11

2.1.7 Instructions C2: Customer Self Isolation & Restoration – Procedure when Appliances Switched On (Non-damaging contaminants) ................................................................................... 12

2.1.8 Engineer 1: Engineer Purge and Relight (Non-damaging Contaminants)................. 13

2.1.9 Instructions E: Engineer Purge and Relight – Procedure (Non-damaging contaminants) 14

2.1.10 Engineer 2: Engineer Purge and Relight (Damaging Contaminants)........................ 15

2.1.11 Instructions E2: Engineer Purge and Relight – Procedure (Damaging Contaminants)16

2.2 No Meter Isolation ......................................................................................................................... 17

2.3 Results ........................................................................................................................................... 18

2.4 Sensitivity Analysis....................................................................................................................... 19

2.4.1 Best / Worst Case – All inputs .................................................................................. 19

2.4.2 Single Input Sensitivity Analysis – Customer Self Isolation ...................................... 21

2.4.3 Discussion ................................................................................................................ 21

2.4.4 Single Input Sensitivity Analysis – No Meter Isolation .............................................. 22

2.4.5 Discussion ................................................................................................................ 22

3 Fatalities From Cold Weather ....................................................................................................23

3.1 Data................................................................................................................................................. 23

3.1.1 Proportion of population with underlying illnesses .................................................... 23

3.1.2 Average Indoor Temperature .................................................................................... 23

3.1.3 Information on insulation within properties................................................................ 23

3.1.4 Indoor temperature drop with no heating .................................................................. 24

3.1.5 Additional fatalities .................................................................................................... 25

3.1.6 Age profile of population ........................................................................................... 26

3.1.7 Proportion of properties with central heating, gas fires etc ....................................... 26

3.1.8 Proportion of properties with electric heating ............................................................ 26

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3.1.9 Information relating to issue of electric heaters......................................................... 26

3.1.10 Average Household Size .......................................................................................... 26

3.2 Results ........................................................................................................................................... 27

3.3 Sensitivity Analysis....................................................................................................................... 28

3.3.1 Best / Worst Case – All inputs .................................................................................. 28

3.3.2 Single Input Sensitivity Analysis ............................................................................... 29

3.3.3 Discussion ................................................................................................................ 29

4 Conclusions ................................................................................................................................30

4.1 Predicted Number of Gas Safety Related Fatalities ................................................................... 30

4.2 Fatalities from Cold Weather........................................................................................................ 30

5 Recommendations......................................................................................................................32

6 References ..................................................................................................................................33

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1


Introduction

SGN have established a methodology of determining an appropriate approach for customer self-isolation and restoration following a gas supply outage. To utilise this methodology they have requested GL to determine:

• The predicted number of fatalities caused by gas incidents

• The predicted number of fatalities caused by the effects of weather

during a gas supply outage, for:

• Established approaches to managing the incident

• Alternative approaches to managing the incident.

This will involve generating two statistically robust graphs in the format shown (Figure 1 and Figure 2) that can be used in conjunction with SGN’s methodology to determine the most appropriate approach to adopt during an incident.

The first graph will outline the relationship between the scale of the outage, the methodology adopted to isolate and restore the gas supply, and the predicted number of fatalities from a gas incident (e.g. fire / explosion caused by unburnt gas entering a property), in the following format (note axes are indicative only):


0

1

2

3

4

0 1000000

No of Prope rtie s w he re

Ga s Supply Fa ilure Occurre d

No

of

Fata

liti

es No Meter Isolation Carried Out

Cus tomer Self Isolation &

Res toration used

Qualified Personnel us ed for

Isolation & Restoration

Figure 1: Predicted Number of Gas Safety Related Fatalities

Consideration will be made to various scenarios to include isolation and restoration by:

• Qualified personnel;

• Customer;

• No meter isolation carried out.

The second graph (Figure 2) will outline the relationship between the anticipated duration of the outage, the forecast weather conditions, and the predicted number of fatalities caused by the lack of heating in the properties affected by the outage in the following format (note axes are indicative only:

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Predicted Number Cold Weather related Fatalities

0

50

100

150

200

250

0 50

Dura tion of Ga s Supply Failure (Da ys)

No

of

Fata

liti

es

/ 1

000

aff

ecte

d p

rop

ert

ies

Temperature below 5C

Temperature 5 - 9 C

Temperature 9 - 12 C

Temperature 12 - 16 C

Temperature above 16 C

Figure 2: Predicted Number of Cold Weather Fatalities

The work has been undertaken in two separate phases, the first of which consisted of a feasibility study. This phase detailed the information search carried out, data obtained, what assumptions need to be made and how the models will be developed. The suitability of the available data was discussed and colour coded to signify the level of confidence in the data:

• Green - data is available

• Amber - no data or limited data is available but it can be inferred from engineering knowledge (objective)

• Red - no data is available and assumptions will need to be made (subjective)

The second phase, details of which are included within this report, will utilise all available information and assumptions to build the models that can be used to generate the required graphs. Details of the data used, the assumptions made and the model used are all included in this report along with the two graphs required. A sensitivity analysis is also included to support the assumptions used and highlight any issues.


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2 Predicted Number of Gas Safety Related Fatalities

A spreadsheet model was built in Excel in order to predict the number of fatalities resulting from isolation and restoration following a loss of supply incident. Isolation and restoration by the customer and by qualified personnel have been considered within the model.

2.1 Data

As detailed in the feasibility study, the availability of data is broken down into three levels:

• Green - data is available

• Amber - no data or limited data is available but it can be inferred from engineering knowledge (objective)

• Red - no data is available and assumptions will need to be made (subjective)

2.1.1 Loss of Gas Supply

This is the top level of the analysis and looks at the cause of the supply loss and, as a result, whether customer self isolation and restoration is possible.

If damaging contaminants are present (e.g. water or dust) or the supply loss results from over pressurisation of the system, customer self isolation and restoration is not considered feasible. If the supply loss affects a large number of properties, it may be necessary to consider customer self isolation and restoration but a large number of factors will then need to be considered, e.g. if there is water in the gas meter it will need to be replaced and an engineer purge and relight will then be undertaken. Customer self isolation and restoration in the event of damaging contaminants being present is not considered here.

In the case of non-damaging contaminants being in the system (or no contaminants), even in the case of an engineer purge and relight, some customers may choose to undertake the process themselves. This is considered within the model.

2.1.1.1 Data

Data Required Level of Availability Comments

Cause of Supply Loss (presence of contaminants etc)

Green Historical data is available detailing previous incidents where gas supply is lost. The cause of the incident is also available.

Number of properties affected Green Historical data is available detailing previous incidents where gas supply is lost. The number of properties affected is also available.

Available engineers and number of properties each engineer could visit in a day

Green Information supplied by SGN.

Proportion of customers refusing engineer purge and relight and undertaking operation themselves

Amber No available information but reasonable assumptions can be made.

Table 1: Data required: Loss of Gas Supply


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2.1.1.2 Cause of Supply Loss (presence of contaminants etc.)

Historical data is available detailing previous incidents where gas supply is lost. The cause of the incident is also available.

It is not considered necessary to use this information as it is assumed that the scenarios generated in this analysis and shown in graphical form will assume that a loss of supply has occurred. As a result, the cause of the supply loss will be known and data from the relevant graph used.

2.1.1.3 Number of properties affected

Historical data is available detailing previous incidents where gas supply is lost. The number of properties affected is also available.

The model will generate the predicted number of fatalities for a given number of properties affected.

The following bands have been recommended by SGN:

• 50,000 properties.

2.1.1.4 Available engineers and number of properties each engineer could visit in a day

SGN have supplied this information.

2.1.1.5 Proportion of customers refusing engineer purge and relight and undertaking operation themselves

No available information but reasonable assumptions can be made. Assuming an engineer purge and relight is being undertaken, it has been assumed that 1% of customers will refuse and carry out the procedure themselves.

There is also the possibility that should a customer self isolation and restoration operation be undertaken, there will be a number of people, such as those on the vulnerable persons register, who will require assistance from an engineer. This will need to be dependent upon the number of properties affected. If a large number of properties are affected, the purpose of a customer isolation and restoration operation is to speed up the reconnection process and reduce the number of engineers required.

2.1.2 Customer 1: Customer Self Isolation & Restoration (Non-damaging Contaminants)

Once it is established that it is safe for customer self isolation and restoration to be undertaken, the service needs to be isolated. The scenarios considered are:

• The customer can be contacted prior to the supply being reinstated to enable the service to be isolated;

• A neighbour can be contacted and can provide access;

• The service is outside the property and is accessible to an engineer;


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• The service cannot be isolated.

The probabilities will be dependent upon the duration of the loss of supply. The service needs to be isolated prior to reconnection and so can occur at any point during the loss.

If the service cannot be isolated, a problem will only occur on reinstatement of the supply if appliances within the property are switched on, e.g. if a gas fire has been left on and there is no flame supervision device on the appliance.

2.1.2.1 Data


Probability Service Isolated Red This will be dependent upon whether the customer is at home, where the ECV is situated and whether access can be obtained in any other way. No statistics are available but assumptions can be made.

Status of appliances Red No statistics are available but assumptions can be made.

Table 2: Data Required - Customer Self Isolation & Restoration (Non-damaging Contaminants)

2.1.2.2 Probability Service Isolated

This will be dependant upon whether the customer is at home, where the ECV is situated and whether access can be obtained in any other way. No statistics are available but assumptions can be made. The duration of supply loss will need to be considered. No data is available on the duration of the supply loss from previous incidents.

Assumptions:

• The probability of contact being made with the householder, who subsequently isolates the service, is considered to be dependent upon the duration of the supply loss. The isolation can take place at any point during the supply loss, as long as it is performed prior to reinstatement of the gas supply. No statistics are available.

• If the customer cannot be contacted it is assumed that there is a chance that a neighbour can be contacted, who has access to the property.

• The ECV is outside – this will be dependent upon network, type of property etc.

• The ECV is accessible - e.g. access gate not locked etc;

• The ECV is considered operational - if a spanner etc is required, this will be relayed to the customer and help given when necessary. It is considered reasonable that a customer has access to the meter box where applicable, e.g. knows where the key is or can obtain the key within required time scales.

Probabilities have also been assumed for the length of the supply loss.

2.1.2.3 Status of Appliances

It is assumed that once the supply has been isolated, the customer will check that appliances have been turned off, with an assumption of a probability that appliances will be turned off by the customer and the rest by a neighbour with access.


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A sensitivity analysis can be performed to allow for different times of year, e.g. summer holidays when more people will be away.

2.1.3 Instructions C1: Customer Self Isolation & Restoration – Procedure when Appliances Shut Down (Non-damaging contaminants)

This scenario considers the risks associated with customer self isolation and restoration assuming no damaging contaminants are in the system and all appliances are switched off prior to the reinstatement of the gas supply. The probabilities will be the same whether or not the supply was isolated via the ECV prior to reinstatement, with the exception of whether the ECV is turned fully on or partially on. It is assumed that if the ECV was not isolated, it will have previously been turned fully on.

2.1.3.1 Data


Is the ECV turned fully on or partially on? Amber No data is available but engineering judgement can be used to quantify the risk of not turning the ECV fully on; i.e. the likelihood of only turning the ECV partially on is considered low.

Probabilities related to relighting Amber Engineering judgement can be used to quantify the probability that the appliance will light within the 5 minutes stated. Previous reports and investigations carried out state that the probability of flash burns will be low.

Risk of Explosion Amber Previous reports give details of tests carried out identifying the risk of flash back and resulting explosions. Risk is stated as very low but not quantified.

Risk of Fatality Green Previous reports give details of hazard rates for different appliances; Probability of fatality as a result of a natural gas explosion available from HSE.

Table 3: Data - Customer Self Isolation & Restoration – Procedure when Appliances Shut Down (Non-damaging contaminants)

2.1.3.2 Is the ECV turned fully on or partially on?

No data is available but engineering judgement can be used to quantify the risk of not turning the ECV fully on; i.e. the likelihood of only turning the ECV partially on is considered to be low.

Assumptions:

• Probability customer follows instructions and turns ECV fully on

• Probability customer only turns ECV partially on

2.1.3.3 Probabilities related to relighting

Engineering judgement can be used to quantify the probability that the appliance will light within the 5 minutes stated. It is assumed that the probability that the appliance fails to light will be higher when the ECV has only been turned partially on as the gas will take longer to reach the appliance. Both probabilities are considered to be low. Previous reports and investigations carried out state that the probability of flash back will be low.

If the customer fails to light the appliance, there is a further probability that they fail to turn off the gas supply to the appliance. Access Restricted Page 6

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Assumptions:

• Assuming ECV fully on, probability appliance lights successfully

• Assuming ECV partially on, probability appliance lights successfully

• If appliance lights successfully, there is no risk of fatality

• Assuming ECV fully on, probability of flashback

• Assuming ECV partially on, probability of flashback

• Assuming ECV fully on, probability customer cannot light appliance

• Assuming ECV partially on, probability customer cannot light appliance

• Probability customer turns appliance off following failure to light

2.1.3.4 Risk of Explosion

This is a calculated value built up from the probabilities within this report.

2.1.3.5 Risk of Fatality from Flashback

In the unlikely event of flashback occurring during the relight operation, there is a very small possibility of an accident leading to fatality (e.g. a fall). In this case, only one fatality per incident can occur.

Assumptions:

• Probability of a fatality as a result of flashback

This probability is based on:

Probability fall as a result of flashback X Probability fatality as a result of a fall

Incidents relating to relighting of appliances shows falls, whilst recognised as a possibility, are extremely rare and no falls occurred in 20 incidents. Assuming a worst case scenario of a fall occurring in one of the next 20 reports gives an upper bound on the probability of 0.025, with a lower bound of 0.

Information is available from the DTI giving the proportion of fatalities compared to falls not resulting in fatalities - 0.0018279 of all falls in the home result in a fatality.

The probability of a fatality as a result of flashback is arbitrarily set.

2.1.4 Customer Absent: No Contact Made With Customer Prior to Reconnection

In the case where the service cannot be isolated and the customer is not present in the property on reinstatement, there is a risk associated with appliances being left on and a resulting gas release once the gas supply is reconnected. This will apply to both customer self isolation and restoration (which will be undertaken on the customer’s return) and also a purge and relight conducted by an engineer.


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2.1.4.1 Data


Population of appliances Green Population numbers are available for 2005.

Probability appliance left on Amber Engineering judgement can be used to determine the probability an appliance is left on in a vacant property, based on the use, time of year etc. The probability that the appliance was on and the property occupied but not subsequently turned off will need to be considered.

Probability flame supervision device fitted Green / Amber Can be determined based on regulations and age split of population. Regulations known but less certainty about the age split of appliances.

Table 4: No Contact Made With Customer Prior to Reconnection

Six gas appliances are considered – water heater, gas fire, wall heater, gas hob, boiler and gas oven. These are further subdivided by type (e.g. flued / unflued). Probabilities are based on the population of appliances, and the probability of the appliance being left on (based on its use, time of year etc).

If the appliance was left on prior to the gas supply being lost, and a flame supervision device is fitted to the appliance, there is no risk of a gas release on reinstatement. The presence of a flame supervision device will be dependent on regulations, age of appliance etc.

Assumptions that will need to be made include whether all windows in a vacant property are closed, whether the property is monitored as per guidelines, and whether any fitted flame supervision devices are working correctly (based on the assumption that they ‘fail safe’).

2.1.4.2 Population of appliances

Population numbers are available for 2005.

Appliance Type Population Probability Appliance

Fitted

Built In Hob 5,392,000 0.2451

Freestanding Hob 6,560,000 0.2982

Built In Oven 785,000 0.0357

Freestanding Oven 6,560,000 0.2982

Gas Fire - Flued 11,700,000 0.5318

Hall Heaters - Unflued 1,148,000 0.0522

Gas Fire - Unflued 250,000 0.0114

Boiler 20,370,000 0.9259

Water Heater 1,259,000 0.0572

Table 5: Populations of Gas Appliances

The probability that the appliance is fitted in a property is calculated from the population of appliances divided by the total number of households in the UK with a mains gas supply.

Assumption:

• Total Number of Households in UK with a mains gas system = 22,000,000


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2.1.4.3 Probability appliance left on

Engineering judgement has been used to determine the probability that an appliance is left on in a vacant property, based on the use, length of supply loss etc.

The population of gas hobs has been further divided based on the number of hobs left on (a maximum of four is assumed).

It has been assumed that grills pose less risk than ovens and as such have not been treated separately. Similarly open grills in freestanding cookers will provide no greater risk than four hobs being left on. The probabilities of these being left on allow for the probability of a grill being left on.

2.1.4.4 Probability flame supervision device fitted

The probability that a flame supervision device is fitted has been determined from regulations and the expected age split of the population.

2.1.5 Risk of Gas: Risk Resulting From Gas Release During Customer Absence

Assuming the service has not been isolated, one or more appliances (with no flame supervision devices) have been left switched on in the property and the property is unoccupied at the time of the reinstatement of the gas supply, there is a high likelihood of gas being released into the property. Assuming the correct monitoring procedures have been undertaken, the gas should be detected. The probability of the gas being detected is thus dependent on whether monitoring is undertaken, how rapid the gas release is etc. If gas is detected, appropriate action is taken to secure the gas supply – either forcibly entering the property or cutting and capping of the service pipe.

If gas is released but not detected, it is then possible for a flammable mixture to be formed within the property. This will be dependent on the appliance in question, the rate of gas release, the length of time the property is vacant etc. If a flammable mixture does form, an ignition source is still required before an explosion can occur. If the property is vacant, the source of ignition could come from a refrigerator, thermostat, timer or mains powered smoke alarm. If the property is occupied, then a light being turned on as the consumer returns home could be the source. An explosion in a vacant house will then have different consequences to one in an occupied house. Similarly an explosion in a terraced house could have different consequences to an explosion in a detached house.

The main difference to the risk when the customer is absent is that, instead of the gas being detected through monitoring of the property, it will now be detected by the householder, usually through smell. It is assumed that if gas is smelt, appropriate action is taken, such as isolating the supply at the ECV, turning off the appliance in question and opening windows and doors.


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2.1.5.1 Data


Gas detected through monitoring Amber It is known that if gas is released into a vacant property and monitoring is undertaken, there is a high probability that it will be detected.

Flammable mixture forms Green Gas flow rates for different appliance types are known as are the probabilities of flammable mixtures being formed.

Ignition Source Red Assumptions will need to be made as to where the source of ignition could come from in a vacant property.

Risk of fatality Red A number of assumptions will be required, such as whether the house is occupied at the time of the explosion and the type of house etc.

Table 6: Data Required - Risk Resulting From Gas Release During Customer Absence

2.1.5.2 Gas detected through monitoring

It is known that if gas is released into a vacant property and monitoring is undertaken, there is a high probability that it will be detected.

Assumptions:

• Probability that monitoring takes place

• Probability that gas is detected through monitoring is assumed by the volume percentage concentration of gas.

• Length of time monitoring occurs

• Probability that appropriate action is taken if gas is smelt / identified during monitoring

2.1.5.3 Flammable mixture forms

A calculation to determine whether a flammable mixture forms is used. This is linked to the gas release rate, the size of the room and the ventilation rate. It is assumed that all windows and doors are closed as gas release is most likely in an unattended property. Other factors include the presence of a flue and the amount of gas that will pass up the flue.

Assumptions:

• Gas Release Rate and the presence of a flue – these are dependent upon the appliance type:

• Typical Room Size

• Typical ventilation rate

• Percentage of gas passing up flue

2.1.5.4 Ignition Source

There are a number of sources of ignition within a vacant property. These include fridges, timers, thermostats, mains powered smoke alarms. Previous research states that released gas will eventually move throughout the property even if internal doors are closed.


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Assumptions:

• Probability a source of ignition occurs

2.1.5.5 Risk of Fatality

Utilising the documented assumptions, the model will determine the probability that an explosion occurs.

Assumptions:

• Fatality rate per explosion

2.1.6 Risk of Gas 2: Risk Resulting From Gas Release During Customer Presence

The main difference to the risk when the customer is absent (section 2.1.5) is that instead of the gas being detected through monitoring of the property it will now be detected by the householder, usually through smell. It is assumed that if gas is smelt, appropriate action is taken, such as isolating the supply at the ECV, turning off the appliance in question and opening windows and doors.

2.1.6.1 Data


Gas detected through smell Amber There is a high probability that if gas is released into a property following reinstatement of the gas supply, and the property is occupied, the householder will smell the gas and take action before a flammable mixture can form.

Flammable mixture forms Green Gas flow rates for different appliance types are known as are the probabilities of flammable mixtures being formed.

Ignition Source Red Assumptions will need to be made as to where the source of ignition could come from.

Risk of fatality Amber A number of assumptions will be required, such as the type of house etc.

Table 7: Data Required - Risk Resulting From Gas Release During Customer Presence

2.1.6.2 Gas detected through smell

There is a high probability that if gas is released into a property following reinstatement of the gas supply, and the property is occupied, the householder will smell the gas and take action before a flammable mixture can form. Research has shown that allowing for the rate at which odorant is added to gas supplies in the UK, there will be a readily detected warning concentration at approximately 1% gas in air. A number of further factors need to be considered such as:

• It should be noted that humans have a wide range of sensitivity to their sense of smell with about 1 person in 500 having no sense of smell.

• There has been no noted difference in sensitivity with gender.

• In general, people aged over 50 show a loss of sensitivity and ability to detect smells.

• Smokers show a temporary loss in sensitivity while they are actually smoking otherwise they have comparable sensitivities to non-smokers.


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• Nasal fatigue (short term loss of sensitivity to a constant odour level following exposure to that odour level) can occur in a few minutes but full sensitivity can be regained in a few minutes if the odour is removed.

• The odour can be masked by other smells, for example air fresheners and strongly perfumed cleaning materials.

• There is also the possibility that people do not identify a particular odour as being characteristic of the odorant added to natural gas and hence do not associate this odour with a gas leak.

Assumptions:

• Probability customer will smell any gas released

• Probability that appropriate action is taken if gas is smelt / identified during monitoring

• Length of time between restoration of gas supply to the property and relight occurs

2.1.6.3 Flammable mixture forms

Details of the calculation to determine whether a flammable mixture forms can be found is used. This is linked to the gas release rate, the size of the room and the ventilation rate. It is assumed that all windows and doors are closed as gas release is most likely in an unattended property. Other factors include the presence of a flue and the amount of gas that will pass up the flue.

2.1.6.4 Ignition Source

The probability of a source of ignition is considered to be higher in an occupied property than in an unoccupied property, assuming additional electrical sources will be live e.g. light switches, televisions etc.

Assumptions:

• Probability a source of ignition occurs

2.1.6.5 Risk of fatality

Utilising the documented assumptions the model will determine the probability an explosion occurs.

Assumptions:

• Fatality rate per explosion

2.1.7 Instructions C2: Customer Self Isolation & Restoration – Procedure when Appliances Switched On (Non-damaging contaminants)

This scenario considers the risks associated with customer self isolation and restoration assuming no damaging contaminants are in the system and one or more appliances are switched on prior to the reinstatement of the gas supply. It is assumed that the ECV has been isolated prior to the reinstatement of the mains gas supply.

Scenarios resulting in a risk of fatality include the possibility that the customer does not check all appliances are off before restoring the gas supply or fails to complete the restoration once started, thus leaving the gas supply on.


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2.1.7.1 Data


Appliances checked prior to ECV turned on Red Probability that instructions are followed.

ECV is turned fully on or partially on Red Purely subjective probability based on human error.

Gas relights successfully Green Previous reports have concluded that a purge and relight where no damaging contaminants are present carries no greater risk than turning off the gas supply whilst on holiday. This infers that there is a very high likelihood of the gas relighting with no incident.

Flash flame occurs Amber Again previous reports have investigated this possibility, particularly when there is air in the system.

Operation is unsuccessful Red There is no evidence of this occurring, but it will be dependent on factors outside the scope of this work, as a result of the purging etc carried out upstream of the meter. This scenario cannot be ignored and so a very low probability will be included to ensure it is factored in.

Table 8: Data Required - Customer Self Isolation & Restoration – Procedure when Appliances Switched On (Non-damaging contaminants)

2.1.7.2 Appliances checked prior to ECV turned on

This is the probability that the instructions for the restoration are followed correctly. A number of factors can be included such as age of householder, but for simplicity an average value is used.

Assumptions:

• Probability all appliances checked and turned off prior to reinstatement

2.1.7.3 ECV is turned fully on or partially on

See section 2.1.3.2.



2.1.8 Engineer 1: Engineer Purge and Relight (Non-damaging Contaminants)

Prior to an engineer carrying out a purge and relight, the presence of the customer needs to be identified – can access be obtained to isolate the service to the property at the ECV, e.g. is the customer present, can access be obtained via a neighbour, is the ECV outside and accessible etc. This will be dependent upon the duration of loss of supply.

The scenarios considered are:

• The customer can be contacted prior to the supply being reinstated to enable the service to be isolated;

• A neighbour can be contacted and can provide access;

• The service is outside the property and is accessible to an engineer;

• The service cannot be isolated.


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The probabilities will be dependent upon the duration of loss of supply. The service needs to be isolated prior to reconnection and so can occur at any point during the loss.

If the service cannot be isolated, a problem will only occur on reinstatement of the supply if appliances within the property are switched on, e.g. if a gas fire has been left on and there is no flame supervision device on the appliance.

2.1.8.1 Data


Probability Service Isolated Red This will be dependent upon whether the customer is at home, where the ECV is situated and whether access can be obtained in any other way.

Status of appliances Red No statistics are available but assumptions can be made based on duration of supply loss, time of year, time loss occurred etc.

Table 9: Data Required - Engineer Purge and Relight (Non-damaging Contaminants)




It is assumed that once the supply has been isolated, the customer will check that appliances have been turned off, assume a probability that appliances will be turned off by the customer, and 0.95 by a neighbour with access. It is assumed that these probabilities are higher than during customer isolation (see section 2.1.2.3) due to the presence of an engineer to ensure the correct procedures are followed.

2.1.9 Instructions E: Engineer Purge and Relight – Procedure (Non-damaging contaminants)

This scenario considers the risks associated with isolation and restoration by an engineer assuming no damaging contaminants are in the system and all appliances are switched off prior to the reinstatement of the gas supply.

2.1.9.1 Data


Is the ECV turned fully on or partially on? Green It is assumed that the ECV will be turned fully on in all cases.

Probabilities related to relighting Amber Engineering judgement can be used to quantify the probability that the appliance will light within the 5 minutes stated. Previous reports and investigations carried out state that the probability of flash burns will be low. Probabilities of related incidents will be lower than for customer self isolation and restoration.

Risk of Fatalities Amber Previous reports give details of tests carried out identifying the risk of flash back and resulting explosions. Probabilities of related incidents will be lower than for customer self isolation and restoration.

Table 10: Data Required - Engineer Purge and Relight – Procedure (Non-damaging contaminants)


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It is assumed that the ECV will be turned fully on in all cases by the engineer.


Engineering judgement can be used to quantify the probability that the appliance will light within the 5 minutes stated. Previous reports and investigations carried out state that the probability of flash back will be low.

If the engineer fails to light the appliance, there is a further probability that they fail to turn off the gas supply to the appliance.

Assumptions:

• Probability that appliance lights successfully

• If appliance lights successfully, there is no risk of fatality

• Probability of flashback

• Probability that engineer cannot light appliance

• Probability that engineer turns appliance off following failure to light

2.1.9.4 Risk of fatalities

In the unlikely event of flashback occurring during the relight operation, there is a very small possibility of an accident leading to fatality (e.g. a fall). In this case only one fatality per incident can occur.

Assumptions:

• Probability of a fatality as a result of flashback

2.1.10 Engineer 2: Engineer Purge and Relight (Damaging Contaminants)

For the purpose of this work, the risks of an engineer carrying out a purge and relight when damaging contaminants are present are considered to be the same as for an engineer carrying out a purge and relight when no damaging contaminants are present. Checking and replacement of the meter are ignored in this analysis.

2.1.10.1 Data


Probability Service Isolated Red This will be dependent upon whether the customer is at home, where the ECV is situated and whether access can be obtained in any other way. No statistics are available but assumptions can be made.

Status of appliances Red No statistics are available but assumptions can be made based on duration of supply loss, time of year, time loss occurred etc.

Table 11: Data Required - Engineer Purge and Relight (Damaging Contaminants)


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2.1.11 Instructions E2: Engineer Purge and Relight – Procedure (Damaging Contaminants)

Additional checks need to be carried out prior to the purge and relight operation, such as checking the meter and replacing if necessary. Fatalities due to this operation are not considered here.

Very little information is available about the procedure carried out when damaging contaminants are present. It can be assumed that any corresponding probabilities relating to fatalities will be very low when the procedure is carried out by a competent engineer.

2.1.11.1 Data


Is the ECV turned fully on or partially on? Green It is assumed that the ECV will be turned fully on in all cases.

Probabilities related to relighting Amber Engineering judgement can be used to quantify the probability that the appliance will light within the 5 minutes stated. Previous reports and investigations carried out state that the probability of flash burns will be low.

Risk of Fatalities Amber Previous reports give details of tests carried out identifying the risk of flash back and resulting explosions.

Table 12: Data Required - Engineer Purge and Relight – Procedure (Damaging Contaminants)


It is assumed that the ECV will be turned fully on in all cases by the engineer.



2.1.11.4 Risk of fatalities



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2.2


No Meter Isolation

A further scenario requested by SGN is that of no meter isolation. The current scenarios assume that if possible, the gas supply is isolated at the meter before the gas supply is restored whenever possible. This leaves four scenarios:

i. Service isolated and appliances shut down

ii. Service isolated and appliances left on

iii. Service not isolated and appliances shut down

iv. Service not isolated and appliances left on

Of these four scenarios, (iii) and (iv) already consider the option that the service has not been isolated at the meter. The risk under scenario (i) will be unchanged if the service is not isolated as all appliances are shut down. Any increase in risk will therefore be as a result of scenario (ii). The model has been modified to consider this increased risk.

Assumptions:

• It is assumed that if meter isolation does not take place, no monitoring of individual properties will take place.

• It is assumed that customer restoration will take place with the same assumptions previously considered.

• There is now no issue with the ECV only being turned partially on. It is assumed that prior to the supply loss the ECV was fully on.


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2.3 Results

Based on all the inputs given in section 2.1, graphs representing the predicted number of gas safety related fatalities for non-damaging contaminants and damaging contaminants are shown in Figure 3 and Figure 4 respectively.



0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000


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Figure 3: Predicted Number of Gas Safety Related Fatalities (Non-Damaging Contaminants)


Damaging Contaminants Present

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000


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Qualified Personnel used for Isolation and Restoration

Figure 4: Predicted Number of Gas Safety Related Fatalities (Damaging Contaminants)


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2.4 Sensitivity Analysis

2.4.1 Best / Worst Case – All inputs

An initial sensitivity analysis has been carried out for isolation and restoration where no damaging contaminants are present. All assumptions have been assigned a ‘worst-case’ value and a ‘best-case’ value. Where the data has been coded green (see section 2.1), no sensitivity has been carried out. The other values have been modified as considered to be appropriate. Plots are shown in Figure 5, Figure 6 and Figure 7.

It should be noted that these are extreme cases as it is extremely unlikely that all inputs will meet the worst case criteria or the best case criteria.

Predicted Number of Gas Safety Related Fatalities (Non-Damaging Contaminants Present)

Sensitivty Analysis - Customer Self Isolation and Restoration

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000


Nu

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Customer Base

Customer Worst

Customer Best

Figure 5: Sensitivity Analysis on Predicted Number of Gas Safety Related Fatalities (Non-Damaging Contaminants) Customer Self Isolation and Restoration


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Sensitivty Analysis - Engineer Isolation and Restoration

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000


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Engineer Base

Engineer Worst

Engineer Best

Figure 6: Sensitivity Analysis on Predicted Number of Gas Safety Related Fatalities (Non-Damaging Contaminants) Engineer Isolation and Restoration


Sensitivty Analysis - Customer Self Isolation and Restoration

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000


Nu

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Customer Base

Customer Worst

Customer Best

Figure 7: Sensitivity Analysis on Predicted Number of Gas Safety Related Fatalities (Non-Damaging Contaminants) No Meter Isolation


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2.4.2 Single Input Sensitivity Analysis – Customer Self Isolation

The impact of a single input can be examined by changing the inputs one by one. The base case is based on a supply loss to 25,000 properties with no damaging contaminants present and assuming a customer self isolation and restoration is undertaken.

2.4.3 Discussion

The sensitivity analysis shows only three inputs having a significant impact on the predicted number of fatalities:

• Probability the ECV is turned fully on

• The likelihood of the restoration being carried out successfully being changed as follows:

• Risk of fatality from flashback

The process carried out will not pick up combinations of changes that will have an impact. For example increasing the fatality rate has minimal effect because the probability of a fatality occurring is so small; similarly changing an input that increases the probability of a fatality occurring will not have much impact when the rate of fatality is so small. Section 2.4.1 shows the effect when all inputs are changed. In order to identify small combinations of inputs having an effect a full probabilistic model would be required which uses probability distributions for each individual input.


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2.4.4 Single Input Sensitivity Analysis – No Meter Isolation

The impact of a single input can be examined by changing the inputs one by one. The base case is based on a supply loss to 25,000 properties with no damaging contaminants present and assuming a customer self isolation and restoration is undertaken but no meter isolation.

2.4.5 Discussion

The sensitivity analysis shows only two inputs having a significant impact on the predicted number of fatalities:

• Risk of fatality from flashback

• When the probability that gas is detected is halved, the risk increases. It should be noted that this scenario is highly unlikely and is for illustrative purposes only.

The process carried out will not pick up combinations of changes that will have an impact. For example increasing the fatality rate has minimal effect because the probability of a fatality occurring is so small; similarly changing an input that increases the probability of a fatality occurring will not have much impact when the rate of fatality is so small. Section 2.4.1 shows the effect when all inputs are changed. In order to identify small combinations of inputs having an effect a full probabilistic model would be required which uses probability distributions for each individual input.


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3 Fatalities From Cold Weather

3.1 Data


Proportion of population with underlying illnesses

Amber No direct statistics available but estimates can be derived using available information such as EWM rates.

Average Indoor Temperatures Green Data available

Information on insulation within properties

Green Data available

Indoor temperature drop with no heating Red Assumption - after a week without heating, the temperature inside a property will be the same as the outside temperature;

Additional Fatalities Amber Information is available that should enable this to be modelled with a degree of accuracy.

Age profile of population Green Data available

Proportion of properties with central heating, gas fires etc

Green Data available

Proportion of properties with electric heating

Green Data Available

Information relating to issue of electric heaters and / or electric cooking appliances

Red Assumptions will be made and sensitivity investigated to both numbers available for issue and also for related safety issues.

Table 13: Data Required - Fatalities From Cold Weather

3.1.1 Proportion of population with underlying illnesses

This is no longer considered necessary due to the availability of other data. The fatality rates used will be averages across the whole population.

3.1.2 Average Indoor Temperature

Literature [2] gives an average indoor temperature of 17.5 ºC.

3.1.3 Information on insulation within properties

Information is available [5] relating to levels of insulation as reported in 2006:

Cavity Wall Insulation

• 39.3% of properties have cavity wall insulation

Double Glazing

• 84.1% of properties have some level of double glazing

• 42.7% of properties have more than 80% of rooms double glazed

• 10% of properties have between 60% and 79% of rooms double glazed

Loft Insulation

• 94.9% of properties are insulated to some degree

• 57.3% of properties have more tan 100mm of loft insulation


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Draught Proofing

• 88% of properties have double glazed or single glazed windows that have been draught stripped

• 54.6% of properties have more than 60% of rooms draught proofed

16.3% of properties are considered to be fully insulated; i.e. at least 100mm loft insulation, where there is a loft, cavity wall insulation where there are cavity walls, and at least 80% of rooms with double glazing.

7.4% of properties have no insulation; i.e. no loft insulation, no cavity wall insulation and no double glazing.

Assumptions:

• Based on information available regarding full insulation and zero insulation, a distribution has been assumed for the intermediate levels of insulation.

3.1.4 Indoor temperature drop with no heating

The temperature inside an unheated building is usually around 2.78 ºC higher than the outside air temperature [6].

No information was available as to how quickly the temperature drops inside a house following the heating failure following a gas supply loss.

It has been assumed that the rate as which a house cools down is related to the level of insulation within the property.

Assumptions:

• It is assumed that a fully insulated property will take a number of days to cool down to the outdoor air temperature + 2.78 ºC

• It is assumed that property with no insulation will take less days to cool down to the outdoor air temperature + 2.78 ºC

• A linear relationship has been assumed for the intermediate levels

• A weighted average is then used to predict the average time for a house to cool

• Based on an average indoor temperature of 17.5 ºC, Figure 8 shows the average indoor temperature in the days following a gas supply loss for the 5 outdoor temperature bands required by SGN.

Time For Temperature to Drop

0

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10

15

20

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Tem

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Band 1 Band 2 Band 3 Band 4 Band 5

Figure 8: Time for Indoor Temperature to Drop


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It is assumed that where the outside temperature is above 17.5 ºC, the average indoor temperature of 17.5 ºC is maintained within the property.

The temperature bands used are:

o Band 1 : Below 5 ºC

o Band 2 : 5 - 9 ºC

o Band 3 : 9 - 12 ºC

o Band 4 : 12 - 16 ºC

o Band 5 : Above 16 ºC

The mid temperature has been used (0 ºC is used for band 1 and 18 ºC is used for band 5).

3.1.5 Additional fatalities

The number of excess winter deaths (EWD) gives an indication of the level of deaths relating to cold weather.

In common with other countries, England and Wales experience higher levels of mortality in the winter than in the summer. A measure of this increase is provided by the Office for National Statistics (ONS) on an annual basis, in the form of the excess winter mortality (EWM) figure and index. The current ONS standard method defines the winter period as December to March, and compares the number of deaths that occurred in this winter period with the average number of deaths occurring in the preceding August to November and the following April to July:

EWM = winter deaths – average non-winter deaths

This produces a number of excess winter deaths that is rounded to the nearest 10.

The level of EWD in England, Wales and Scotland since 1991/92 is shown in Table 14.

Excess Winter Deaths

1991 / 1992 34,990

1992 / 1993 25,800

1993 / 1994 28,620

1994 / 1995 29,710

1995 / 1996 43,900

1996 / 1997 51,370

1997 / 1998 25,640

1998 / 1999 51,660

1999 / 2000 53,720

Excess Winter Deaths

2000 / 2001 27,150

2001 / 2002 29,110

2002 / 2003 26,530

2003 / 2004 26,320

2004 / 2005 34,430

2005 / 2006 27,080

2006 / 2007 26,530

2007 / 2008 26,910

2008 / 2009 40,210

Table 14: Excess Winter Deaths in England and Wales since 1991/1992

Further information [7] states that the 25% of coldest homes were observed to have about 20% greater associated risk of excess winter deaths for residents than the 25% warmest. Based on this information, it is assumed that the coldest homes account for 10% more winter deaths than average and the warmest homes account for 10% less winter deaths than average.

If heating is removed from a property due to a gas supply loss, it can be assumed that additional fatalities will occur (based on the average level).


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To relate the number of deaths to the temperature, the mean temperature across the months November March have been used [8]. The temperatures for November have been included, as cold weather fatalities due to respiratory diseases can occur up to 12 days after the peak cold occurs.

The EWM rate (EWM / Population – see Table 15) against mean temperature is then modelled to provide a relationship between temperature and excess deaths.

Country Population (ONS 2009)

England 51,809,700

Wales 2,999,300

Scotland 5,194,000

Total 60,003,000

Table 15: Population Estimates – 2009

The level of cold weather deaths is assumed to drop to a level greater than 0% as, for example, even mildly cool indoor temperatures of 16 – 18 ºC can trigger hypothermia.

3.1.6 Age profile of population

This is no longer considered necessary due to the availability of other data. The fatality rates used will be averages across the whole population.

3.1.7 Proportion of properties with central heating, gas fires etc

The number of properties with central heating is considered to be relevant as the calculations relate to fatalities following the loss of heating to properties supplied by gas. The proportion of properties with electric heaters as secondary heating is considered in section 3.1.8.

3.1.8 Proportion of properties with electric heating

Information is available [3] that states that 27.2% of properties have no secondary heating. There is no available information as to the fuel type of the secondary heating. It is assumed that 50% is gas and 50% is non-gas (e.g. electric).

3.1.9 Information relating to issue of electric heaters

A sensitivity study will be used to increase the level of non-gas secondary heaters (section 3.1.8) to account for issue of additional heaters.

3.1.10 Average Household Size

The average household size in 2005 is estimated at 2.3 [5].


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3.2 Results

Based on all the inputs given in section 3.1, the graph representing predicted fatalities from cold weather as a result of a gas supply loss is shown in Figure 9.


0 10 20 30 40 50 60


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Band 2 : 5 - 9 ºC

Band 3 : 9 - 12 ºC

Band 4 : 12 - 16 ºC


Figure 9: Predicted Number of Cold Weather Related Deaths per 1,000 properties


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3.3 Sensitivity Analysis

3.3.1 Best / Worst Case – All inputs

An initial sensitivity analysis has been carried out where all assumptions have been assigned a ‘worst-case’ value and a ‘best-case’ value. Where the data has been coded green (see section 2.1), no sensitivity has been carried out. The other values have been modified as considered to be appropriate. Plots are shown in Figure 10 (Band 1) and Figure 11 (Band 4).

It should be noted that these are extreme cases as it is extremely unlikely that all inputs will meet the worst case criteria or the best case criteria.

Predicted Number of Cold Weather Related Fatalities - Sensitivity Analysis

Band 1

Below 5 ºC

0 10 20 30 40 50 60

Duration of Gas Supply Loss (Days)

Pre

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Nu

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Base Case

Worst Case

Best Case

Figure 10: Predicted Cold Weather Fatalities; Sensitivity Analysis (Band 1)

Predicted Number of Cold Weather Related Fatalities - Sensitivity Analysis

Band 4

12 - 16 ºC

0 10 20 30 40 50 60

Duration of Gas Supply Loss (Days)

Pre

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Base Case

Worst Case

Best Case

Figure 11: Predicted Cold Weather Fatalities; Sensitivity Analysis (Band 4)


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3.3.2 Single Input Sensitivity Analysis

The impact of a single input can be examined by changing the inputs one by one. The base case is based on an outdoor temperature of 5 – 9 ºC and supply loss duration of 25 days.

3.3.3 Discussion

The sensitivity analysis in section 3.3.2 in which the input values are changed one by one, shows only three inputs having a significant impact on the predicted number of fatalities:

• Indicative temperature used to represent temperature band – increasing the value from the bottom end of the range (5ºC) to the top end (9ºC) decreases the predicted number of fatalities.

• Increasing the minimum excess death rate increases the predicted number of fatalities.

• Increasing the additional fatality rate in the coldest homes from 10% to 15% increases the predicted number of fatalities.


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4.2


4 Conclusions

4.1 Predicted Number of Gas Safety Related Fatalities

Figure 12 shows the graph requested by SGN to illustrate the predicted number of gas safety related fatalities following a gas supply loss resulting in non-damaging contaminants in the supply, as a result of customer self isolation and restoration, restoration by a qualified engineer and also the case where no meter isolation is undertaken. The inputs on which this graph is based are covered in detail in section 2.1.



0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000


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Figure 12: Predicted Number of Gas Safety Related Fatalities (Non-Damaging Contaminants)

Fatalities from Cold Weather

Figure 13 shows the graph requested by SGN to illustrate the predicted number of cold weather related fatalities per 1,000 properties based on five temperature bands. The inputs on which this graph is based are covered in detail in section 3.1.


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0 10 20 30 40 50 60


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Band 2 : 5 - 9 ºC

Band 3 : 9 - 12 ºC

Band 4 : 12 - 16 ºC


Figure 13: Predicted Number of Cold Weather Related Deaths per 1,000 properties


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5


Recommendations

1. The sensitivity analyses carried out for the two plots (sections 2.4 and 3.3) show three inputs in each case having more impact on the results than the other inputs. To ensure the graphs are as robust as possible, more work could be carried out to determine more accurate estimates for these inputs:

• Probability ECV is turned fully on by customer / Likelihood of restoration being carried out successfully - Engineers to be surveyed and / or asked for feedback to determine the likelihood that the ECV is turned fully on by the customer and restoration is subsequently carried out successfully

• Risk of fatality from flashback – Extended literature research to determine level of occurrences of fatalities from flashback in other countries could be carried out. The probability of occurrence, has been gauged based on judgement rather than physical data. The extended literature research to define the probability and the upper bound would increase the understanding of the impact of this variable.

• Indicative temperature for temperature band (cold weather graph) – Improvement of the estimate is not required. As the estimated fatality rate varies across the bands used, narrower bands could be used.

• Minimum Excess Death Rate – Further analysis to examine the relationship between the minimum excess death rate and temperature could be carried out. A simple linear trend is currently used to model the relationship. Examination of additional factors could determine an improved model.

• Additional fatality rate in coldest homes - Although the sensitivity analysis has been carried out using 15% compared to the 10% base case, the 10% value is based on analysis carried out by the ONS and the sensitivity analysis is purely indicative.

2. The worst and best case sensitivity analyses provide upper and lower limits for the predicted number of fatalities. These represent extreme cases which are highly unlikely to occur. If more realistic limits are required, development of a probabilistic model should be considered.


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6 References

[1] A review of emergency response procedures for incidents involving the loss and subsequent restoration of the low pressure natural gas supply. November 1996

[2] How warm is your home? BBC News Magazine; 3rd March 2011.

[3] Energy Use in Homes 2007: A series of reports on domestic energy use in England: Space and Water Heating; Tad Nowak, BRE Housing; 2009.

[4] Energy Use in Homes 2007: A series of reports on domestic energy use in England: Thermal Insulation; Tad Nowak, BRE Housing; 2009.

[5] Domestic Energy Fact File 2008; J I Utley and L D Shorrock; BRE; 2008.

[6] Thermal Heat Loss – How to reduce heat loss From your home and save money;

http://www.home-heating-systems-and-solutions.com/thermal-heat-loss.html

[7] Measuring the health impact of temperatures in dwellings; J Rudge and R Gilchrist; London Metropolitan University, UK.

[8] UK Mean Temperatures 1910 – 2010;

http://www.metoffice.gov.uk/climate/uk/datasets/Tmean/date/UK.txt

[9] Excess winter mortality in England and Wales, 2009/10 (provisional) and 2008/09 (final); Office for National Statistics; 23rd November 2010.

[10] Healthuse – Health and Fitness Tips; http://www.healthuse.com/here-some-cold-weather-tips.html

[11] Appendix 1: The Case Studies; The Longford Gas Plant Accident; http://www.health.vic.gov.au/environment/downloads/risk_communication_app1.pdf


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report number: 11331 june 2011 risk levels for customer ... › gas › supply ›...

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