A Quantitative Risk Assessment Model for Domino Accidents of Hazardous Chemicals Transportation

Round 1

Reviewer 1 Report

This manuscript proposed a quantitative risk assessment model for the risk of multi-vehicle incidents in relating to hazardous material transportations and a case study was carried out in Jinshan District using statistical data and real data. However, major revision is a must before considered for publication: 

1.      The content of the manuscript is not well organized. The index of sections is confusing, it seems to be falsely numbered.

2.      The coordinates and font in figure 9 is too small to read

3.      No explicit conclusion is acquired, is the manuscript un-finished?

4.      The author mentioned that the prediction accuracy of BN model reached 73.3%, how good is the results, is there any comparison or evaluation?

5.      Some figures in the manuscript are not well explained, or even mentioned. E.g. The meaning of the numbers on radar plot in Fig. 17 is unclear; In Fig 12, the shape of personnel risk contours (the extrude part) is not clearly analyzed, etc.

6.      How is the ALARP determined, the manuscript didn’t mention.

7.      Jet fire is directional, the manuscript didn’t elaborate the details of jet fire scenario mentioned in Fig. 10. Is the jet horizontal, or vertical?

Author Response

Dear teacher, thank you for your review. Your advise have greatly helped me to improve my paper. The following are the modifications I made according to your suggestions.

1. I'm sorry that there was an oversight when adjusting the template format of the previous manuscript, the number of the section has been corrected.
2. Figure 9 has been replaced in which the font is enlarged.
3. A conclusion has been added to the new manuscript. The previous journal template not conclude this chapter. For your viewing convenience, here is the content of the new conclusion:

In this study, a quantitative risk assessment model for hazardous chemical transportation has been established. This model uses dynamic Bayesian networks to predict the frequency of hazardous chemical accidents. The study collected 367 hazardous chemical accidents from 2017 to 2021, including human factors, external factors, vehicle factors, environmental factors and road factors. These data are used to train the structure and parameters of Bayesian networks. This network and the vehicle state information uploaded by the vehicle terminal constitute a dynamic Bayesian network, which makes the prediction of accident frequency more convincing. The results show that driver status and weather conditions will increase the frequency of hazardous chemical accidents. Road type has a greater impact on risk, because urban roads are more densely populated and have more traffic flow. The model also quantitatively assesses the risk of dominoes when multiple hazardous chemical vehicles gather. When vehicles gather, potential domino accidents cause more serious consequences. These results have guiding significance for enterprises and governments to prevent hazardous chemical transportation accidents. Enterprises and governments should strengthen the training of drivers, choose to transport hazardous materials on sunny days, and avoid urban roads and business areas. When multiple vehicles carrying hazardous chemicals come too close, the government should warn drivers to drive carefully through on-board terminals.

 

4. The data in the test set were used to verify the accuracy of the model, Table 11 presents a comparison of the predicted and actual scenarios

Driver	Vehicle	External factors	Type of road	light	Weather		actual result	predicted result
normal	normal	none	national highway	day		foggy	Leakage		Leakage
improper operation	normal	none	provincial road	night		sunny	Leakage		Leakage
speeding	normal	none	national highway	day		runny	Leakage		No leakage
normal	normal	none	backroad	day		sunny	No leakage		No leakage
improper operation	normal	none	backroad	day		sunny	Leakage		Leakage
improper operation	normal	none	rural road	day		rainy	exposion		exposion

 

5. Figure is interpreted in more detail as follow :

In order to analyze which factors have a significant effect on PLL. This model compares the PLL corresponding to different node states, such as weather, the state of driver, the type of road and person density. It can be seen from the Figure 18 that the potential loss of life (/year) is fifty times higher on snow days than on sunny days. Potential loss of life reaches  when the vehicle is driving on urban roads. These values are increases over the normal node state, the risk will also increase due to poor weather conditions. When the type of road is rural road and the density of people is business area, the risk increases exponentially, the reason is that complex roads increase the frequency of traffic accidents. When the driver is in fatigue driving, speeding or improper operation, the potential loss of life is,,. Human factors have a great impact on the risk of road transport of dangerous chemicals, it is necessary for enterprises to train their employees.

Figure 13 shows the personal risk contours calculated from eq. 12. Part of the curve is bulging in the figure because the wind is causing the vapor cloud to spread in this direction, the risk is higher in this area.

6. According to GB 36894-2018 "Hazardous Chemicals Production Equipment and Storage Facilities risk basis" and HSE, the lower limit of risk (N, F) of China's social risk standard is (10, 10e-6), the upper limit of risk (N, F) is (10, 10e-4), and the slope is -1. The British social risk standard is the lower limit (50, 10e-6), the upper limit (50, 1e-4), and the slope is -1.
7. In the study, the Angle between the direction of the jet fire and the horizontal line was 30 degrees.Figure 11 is the comparison of the result of jet fire and the result of Aloha simulation. The heat radiation is greater in the direction of the jet fire and the jet fire has strong thermal radiation at 20m.

Reviewer 2 Report

The authors studied numerically the domino accident of hazardous chemicals transportation. A QRA-based model was used to estimate the risk of multi-vehicle incidents. It is followed by the estimation of the frequency of potential scene based on a Bayesian network. This approach can serve as a guide for the government and enterprises. What’s more, this quantitative risk assessment model considers multiple hazardous materials vehicles, which is a good perspective. However, there are some problems to be solved.

1.      The introduction section is long enough but its diversity about different methods to analyze accidents of hazardous chemicals transportation is very weak. To improve the introduction part, the authors should add more references and literature in relation to current research status of hazardous chemicals transportation accidents. Similarly, there are some papers that would be better to be mentioned in this manuscript to help readers to be familiar with domino effect and Bayesian Network.

2.      Multiple reference sources are missing in this manuscript, the authors should check them. If the authors use reference management software, please ensure that all field codes are removed before submitting the electronic manuscript.

3.      There are a lot of typos and grammatical errors. The language of the paper needs to be revisited.

4.      What specific improvements do the authors consider regarding the methodology?

5.      Figure 3, 4, 5, 9, and 10 can be designed better than what they are now.

6.      The numbers in line 303, I would like to know the reason of choosing those numbers. Defend your design by presenting logical design considerations.

7.      The quality of the figures should be enhanced. For example, Figure 10 is hardly recognizable.

8.      In section 3.4, the authors pointed out that considering the domino scenario when the distance between two vehicles is 20m. Why is it 20 rather than any other distance? Authors should argue their choice of this parameter. Also, what triggers the domino effect?

9.      The authors mentioned that they considered the factors in real-time, but the rest of the manuscript fails to deliver the notion of dynamic.

Author Response

Dear teacher, thank you for your review. Your advise have greatly helped me to improve my paper. The following are the modifications I made according to your suggestions.

1The new submission complements the literature on different methods of analysis of hazardous chemical transport accidents, and the introduction literature on dynamic Bayesian networks and dominoes has also been added to the article.

2 The new manuscript has solved the problem of missing references

3 The new manuscript is revised by my tutor

4 This study developed a model for quantitative assessment of the risk of hazardous chemical transportation accidents. The model uses dynamic Bayesian networks to predict hazardous chemical accident frequency. The study collected 367 hazardous chemical incidents between 2017 and 2021, Data include human factors, external factors, vehicle factors, environmental factors, road factors, consequences of the accident. The data is used to train the structure and parameters of Bayesian networks. The network combined with the vehicle state information uploaded by the vehicle terminal constitutes the dynamic Bayesian network, which makes the predicted accident frequency more convincing. The model quantitatively assessed the domino risk when multiple hazardous chemical vehicles gathered. Research shows that driver status, weather conditions and road types have a greater impact on risk. A domino accident occurs when the vehicles gather, causing more serious consequences. It has guiding significance for enterprises and government.

5 Figures 3, 4, 5, 9, and 10 have been revised in the new manuscript. There is a connection between these pictures

6 You may be talking about the data in Tables 3 and 4, which are derived from references : “Weng, J.; Gan, X.; Zhang, Z. A Quantitative Risk Assessment Model for Evaluating Hazmat Transportation Accident Risk. SafetyScience 2021, 137, 105198,doi:10.1016/j.ssci.2021.105198”  and “P.A.M. Uijt de Haag, B.J.M.ALe. Guidelines for Quantitative Risk Assessment(Purple book)[M].The Hague(NL), Committee for the Prevention of Disasters, 2005” .

7 the figure 10 has been redrawn in the new manuscript, new figure has a larger font.

8 China's traffic regulations stipulate that the speed of vehicles on urban roads cannot exceed 40km/h and the safe distance between vehicles is 20m. Table 12 lists the accident scenarios that could lead to a domino effect.

Accident scenario	Frequency	Thermal radiation/overpressure value	The probability of escalation	PLL without domino	PLL with domino	Hole size
Fireball	7.38e-6	38.2	0.23	16	32	rupture
VCE	9.43e-07	201.08Kpa	0.99	318	321	rupture
Jet fire	5.72e-5	1.05	0.005	1	19	0.00635m
VCE	2.59e-8	2.98Kpa	4.61e-6	1	20	0.00635m
Flash fire	3.29e-8	/	0	1	1	0.00635m
Jet fire	1.00e-5	14.80	0.03	1	19	0.0508m
VCE	1.00e-5	83Kpa	0.99	32	45	0.0508m
Flash fire	5.23e-6	/	0	4	4	0.0508m
Jet fire	2.01e-6	14.80	0.03	1	19	0.1524m
VCE	9.12e-10	12.49 Kpa	0.17	135	137	0.1524m
Flash fire	7.34e-10	/	0	9	9	0.1524m

 

9 In the study, real-time means that Jinshan Hazardous Chemicals platform can obtain the location information and speed information of vehicles from the terminal equipment of vehicles in real time, as well as real-time weather conditions. These observations are used to update node states of dynamic Bayesian networks. Get the latest conditional probability table of the accident node

Reviewer 3 Report

This subject could be interesting to the readers in the research field of Hazardous material / transportation safety. However there are serious problems in the manuscript as followings;

1. Reference - I couldn't follow up references in the main body of the paper. Everything says "Error! Reference 333 source not found"

2. There is No conclusion part in the manuscript.

3. I can't understand how trailers with hazardous materials are nearby every time. What's the probability they are next enough to generate domino accidents? 

4. FN curves are presented but how they were made seems very vague in the paper.

5. In table 2, "External effects" - none. It doesn't make sense.

6. "boom" is not scientific word. It should be "explosion" or something.

Author Response

Dear teacher, thank you for your review. Your advise have greatly helped me to improve my paper. The following are the modifications I made according to your suggestions.

 

1. The Reference problem has been solved
2. A conclusion has been added to the new manuscript. For your viewing convenience, here is the content of the new conclusion:

In this study, a quantitative risk assessment model for hazardous chemical transportation has been established. This model uses dynamic Bayesian networks to predict the frequency of hazardous chemical accidents. The study collected 367 hazardous chemical accidents from 2017 to 2021, including human factors, external factors, vehicle factors, environmental factors and road factors. These data are used to train the structure and parameters of Bayesian networks. This network and the vehicle state information uploaded by the vehicle terminal constitute a dynamic Bayesian network, which makes the prediction of accident frequency more convincing. The results show that driver status and weather conditions will increase the frequency of hazardous chemical accidents. Road type has a greater impact on risk, because urban roads are more densely populated and have more traffic flow. The model also quantitatively assesses the risk of dominoes when multiple hazardous chemical vehicles gather. When vehicles gather, potential domino accidents cause more serious consequences. These results have guiding significance for enterprises and governments to prevent hazardous chemical transportation accidents. Enterprises and governments should strengthen the training of drivers, choose to transport hazardous materials on sunny days, and avoid urban roads and business areas. When multiple vehicles carrying hazardous chemicals come too close, the government should warn drivers to drive carefully through on-board terminals.

3. Jinshan Hazardous Chemicals Platform has the information of all hazardous chemical vehicles in Jinshan District. The platform lists inflammable, explosive and toxic dangerous chemicals as major risk sources, and will focus on monitoring these vehicles.

       The following table lists the possibility of a domino accident

Accident scenario	Frequency	Thermal radiation/overpressure value	The probability of escalation	PLL without domino	PLL with domino	Hole size
Fireball	7.38e-6	38.2	0.23	16	32	rupture
VCE	9.43e-07	201.08Kpa	0.99	318	321	rupture
Jet fire	5.72e-5	1.05	0.005	1	19	0.00635m
VCE	2.59e-8	2.98Kpa	4.61e-6	1	20	0.00635m
Flash fire	3.29e-8	/	0	1	1	0.00635m
Jet fire	1.00e-5	14.80	0.03	1	19	0.0508m
VCE	1.00e-5	83Kpa	0.99	32	45	0.0508m
Flash fire	5.23e-6	/	0	4	4	0.0508m
Jet fire	2.01e-6	14.80	0.03	1	19	0.1524m
VCE	9.12e-10	12.49 Kpa	0.17	135	137	0.1524m
Flash fire	7.34e-10	/	0	9	9	0.1524m

 

4. FN curves is determined according to eq.12 and Table 12

Accident scenario	Frequency	Thermal radiation/overpressure value	The probability of escalation	PLL without domino	PLL with domino	Hole size
Fireball	7.38e-6	38.2	0.23	16	32	rupture
VCE	9.43e-07	201.08Kpa	0.99	318	321	rupture
Jet fire	5.72e-5	1.05	0.005	1	19	0.00635m
VCE	2.59e-8	2.98Kpa	4.61e-6	1	20	0.00635m
Flash fire	3.29e-8	/	0	1	1	0.00635m
Jet fire	1.00e-5	14.80	0.03	1	19	0.0508m
VCE	1.00e-5	83Kpa	0.99	32	45	0.0508m
Flash fire	5.23e-6	/	0	4	4	0.0508m
Jet fire	2.01e-6	14.80	0.03	1	19	0.1524m
VCE	9.12e-10	12.49 Kpa	0.17	135	137	0.1524m
Flash fire	7.34e-10	/	0	9	9	0.1524m

 

5. “External effect” was used to verify the accuracy of the model, as shown in Table 10.

Driver	Vehicle	External factors	Type of road	light	Weather		actual result	predicted result
normal	normal	none	national highway	day		foggy	Leakage		Leakage
improper operation	normal	none	provincial road	night		sunny	Leakage		Leakage
speeding	normal	none	national highway	day		runny	Leakage		No leakage
normal	normal	none	backroad	day		sunny	No leakage		No leakage
improper operation	normal	none	backroad	day		sunny	Leakage		Leakage
improper operation	normal	overtake	rural road	day		rainy	exposion		exposion

 

6. “boom” has been replaced by “explosion”

Reviewer 4 Report

The paper "A quantitative risk assessment model for domino accident of hazardous chemicals transportation" analyses from a quantitative point of view the domino effect of an accident involving the transport of dangerous goods by road on other vehicles, also transporting dangerous goods. This approach is considered interesting for publication in the journal Processes, however, the authors must respond to certain aspects in order to improve their article:

 

With reference to the introduction: 

 

(1) The study is focused on China; it would be interesting to provide descriptions of other accidents that have occurred in similar situations involving more than one lorry. Even with substances other than those considered, e.g. ammonium nitrate with a high number of road accidents.

 

(2) The domino effect is a relevant aspect of your publication, it is recommended that it is documented in a more concrete way. References such as:

 

·      Li, J., Reniers, G., Cozzani, V., & Khan, F. (2017). A bibliometric analysis of peer-reviewed publications on domino effects in the process industry. Journal of Loss Prevention in the Process Industries, 49, 103-110.

 

·      Zarei, E., Gholamizadeh, K., Khan, F., & Khakzad, N. (2022). A dynamic domino effect risk analysis model for rail transport of hazardous material. Journal of Loss Prevention in the Process Industries, 74, 104666.

 

In reference to the methodology:

 

(3) Point 2.2. of domino effect is not understood in this section, this aspect should be developed more extensively in the introduction (see comment 2).

 

(4) The tools used, ALOHA and CFD, should be part of the methodology and not of the introduction, as they are the instrument to develop the applied part of the paper.

 

(5) Although reference is made to the Chinese criteria on acceptable individual risk, it is considered interesting to establish whether these are in line with other international standards (European, American, ...). This observation is general to all the criteria indicated, in order to be able to extrapolate the results obtained.

 

With reference to the results and discussion:

 

(6) On the substances considered (ammonia and 1,3-butadiene) it would be interesting to present a table with their main physico-chemical and hazard characteristics.

 

(7) The presentation of results is correct. However, it should be improved by comparing its results with other similar ones, even if they do not correspond to the domino effect in transport. It is considered that the results obtained can lead to a better discussion focused on establishing measures for the prevention, control and mitigation of this type of accidents.

 

(8) Following comment (7), it may be interesting to present the results and their discussion in a single section, improving the part corresponding to the discussion of results.

 

On the conclusions:

 

(9) A section referring to conclusions is not presented. It is considered necessary, making special mention of the lessons learned, the prevention/control/mitigation measures that should be considered and indicating the imitations of this study in terms of its extrapolation to other environments.

 

Author Response

Dear teacher, thank you for your review. Your advise have greatly helped me to improve my paper. The following are the modifications I made according to your suggestions:

1. New manuscript provide descriptions of other accidents that have occurred in similar situations involving more than one lorry. At 14:00 on March 1, 2014, two semi-trailer trucks transporting methanol collided in the Yanhou tunnel of Jincheng section of Shanxi Jinji Expressway, causing a total of 40 fatalities, 12 injuries and 42 vehicles burned down, with direct economic losses of 81.97 million CNY.

The study was based on vehicle data taken from Kingsoft's hazardous chemicals platform. Jinshan Hazardous Chemicals Platform has the information of all hazardous chemical vehicles in Jinshan District. The platform lists inflammable, explosive and toxic dangerous chemicals as major risk sources, and will focus on monitoring these vehicles. The study on ammonium nitrate will be considered in future research.

2. I have read the two references you recommended and have quoted them into the new manuscript.

 

3. The new manuscript has cited the domino effect related articles to discuss the domino accident.

 

4. The new manuscript has put this part into the method

 

5. According to GB 36894-2018 "Hazardous Chemicals Production Equipment and Storage Facilities risk basis" and HSE, the lower limit of risk (N, F) of China's social risk standard is (10, 10e-6), the upper limit of risk (N, F) is (10,10e-4), and the slope is -1. The British social risk standard is the lower limit (50, 10e-6), the upper limit (50, 1e-4), and the slope is -1.Figure 19 and figure 20 show FN curves under different national standards.

 

6. The chemical properties of ammonia and 1,3-butadiene are listed in Table:

The explosive limit of 1,3_butadiene	1.1%~16.1%
The density of 1,3_butadiene	0.62g/m3
The melting point of 1,3_butadiene	-108.9 ℃
Molecular weight of 1,3_butadiene	54.09
Heat of combustion of butadiene	2541.0Kj/mol
Molecular weight of liquid ammonia	17.04
The density of liquid ammonia(25℃)	0.6g/m3
Materials toxicity constant ammonia	a=-15.6, b=1, n=2

 

7. The new manuscript adds comparisons to other studies in the results and discussions. The reference number is 42, 43, 44.

 

8. The new manuscript summarizes the research in conclusion. For your viewing convenience, here is the content of the new conclusion:

In this study, a quantitative risk assessment model for hazardous chemical transportation has been established. This model uses dynamic Bayesian networks to predict the frequency of hazardous chemical accidents. The study collected 367 hazardous chemical accidents from 2017 to 2021, including human factors, external factors, vehicle factors, environmental factors and road factors. These data are used to train the structure and parameters of Bayesian networks. This network and the vehicle state information uploaded by the vehicle terminal constitute a dynamic Bayesian network, which makes the prediction of accident frequency more convincing. The results show that driver status and weather conditions will increase the frequency of hazardous chemical accidents. Road type has a greater impact on risk, because urban roads are more densely populated and have more traffic flow. The model also quantitatively assesses the risk of dominoes when multiple hazardous chemical vehicles gather. When vehicles gather, potential domino accidents cause more serious consequences. These results have guiding significance for enterprises and governments to prevent hazardous chemical transportation accidents. Enterprises and governments should strengthen the training of drivers, choose to transport hazardous materials on sunny days, and avoid urban roads and business areas. When multiple vehicles carrying hazardous chemicals come too close, the government should warn drivers to drive carefully through on-board terminals.

 

9. The new manuscript summarizes the research in conclusion. Future research will apply the method to domino accidents in chemical factory.

Round 2

Reviewer 3 Report

Reference & conclusion issues were solved. 

 

Reviewer 4 Report

After the changes made, the article is considered suitable for publication.