# Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

## 3. Materials and Methods

#### 3.1. Bayesian Network Model

#### 3.1.1. Bayesian Network Composition

#### 3.1.2. Causal Reasoning

^{N}of the occurrence of a risk event obtained by Bayesian network inference is related to the log probability P as:

#### 3.1.3. Diagnostic Reasoning

#### 3.2. Conditional Probability Calculation

#### 3.2.1. The Evaluation Level Establishment

#### 3.2.2. Fuzzy Language Acquisition

#### 3.2.3. Expert Language Defuzzification

#### 3.2.4. Calculation of the Conditional Probability

#### 3.3. Simulation of Sudden Water Pollution Accidents

^{2}, and an average water depth ranging from 1.5 to 3 m. The distance between the Baiyangdian Lake Bridge and Shaochedian along the Fuhe River measures 3750 m. Situated within the Baiyangdian Lake Baojing Line, the region is susceptible to sudden water pollution incidents. The bridge spans a total length of 1203.88 m and possesses a width of 19.5 m, as depicted in Figure 4.

_{0}represents the measured pollutant concentration of the initial section (kg/L), k is the comprehensive self-purification coefficient of pollutants (1/d), and x is the river section distance downstream of the sewage outlet (km). The k of pollutants can be assumed as 0.0213 [63]. The scenario settings and fundamental parameters for the simulation of oil transportation accidents on the cross-tributary bridges of Baiyangdian Lake are presented in Table 3.

#### 3.4. The Proposed Emergency Indicator System

## 4. Results and Discussion

#### 4.1. Risk Factors Identification

#### 4.2. Conditional Probability

_{X1}= (0.3 + 0.5 × 4 + 0.7 × 2, 0.5 + 0.7 × 4 + 0.9 × 2, 0.7 + 0.9 × 4 + 1.0 × 2)/7 = (0.53, 0.73, 0.90)

_{X1}= (0.53 + 2 × 0.73 + 0.90)/4 = 0.72

#### 4.3. Risk Assessment Based on Bayesian Network Model

#### 4.3.1. Causal Reasoning Results

_{A}) was determined to be 0.115, as depicted in Figure 6. Additionally, by considering the relationship between natural probability, logarithmic probability, and risk level from Table 1, the value of P was calculated to be 4.061. This value indicates a high risk level for the transportation of dangerous goods, suggesting a probable occurrence with a high probability.

#### 4.3.2. Diagnostic Reasoning Results

#### 4.4. Accident Simulation Results, Emergency Prevention and Control Measures

#### 4.4.1. Accident Simulation

#### 4.4.2. Risk Emergency Prevention and Control

## 5. Conclusions

_{A}) value of 0.115 and logarithmic probability P value of 4.061. The risk level was relatively high, indicating that accidents posed a potential threat to water environment and human health. (4) Vehicle emergent factors, vehicle wear factors, and weather factors had a greater impact on the occurrence of accidents, with the decreasing order of X4 > X3 > X7. (5) The combination of X1, X4, X6, and X7 contributed to an accident the most. This showed that the highest probability (0.142) of an accident occurred in the region where the driver was not in good condition, the vehicle and tank were worn, and the weather was bad. (6) When the vehicle and the tank were in good condition, the most likely combination of conditions leading to the accident was: X1, X7, X9, X10. This indicated that the probability of an accident was the highest (0.117) on the cross-tributary bridges with poor driver status, bad weather conditions, and no street lighting at night. (7) Emergency prevention and control measures proved to be effective approaches to mitigating the risk of sudden water pollution accidents.

## Supplementary Materials

**a**) Bayesian diagnosis reasoning results of dangerous goods transportation accidents 1 (

**b**) Bayesian diagnosis reasoning results of dangerous goods transportation accidents 2 (

**c**) Bayesian diagnosis reasoning results of dangerous goods transportation accidents 3.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Table 1.**The probability level and risk level of natural probability (P

^{N}) and logarithmic probability (P).

Probability Level | P^{N} Interval | P Interval | Description | Risk Level |
---|---|---|---|---|

5 | 0.3–1 | 5.0–4.5 | Probably | High |

4 | 0.03–0.3 | 4.5–3.5 | May | Higher |

3 | 0.003–0.03 | 3.5–2.5 | Occasionally | Medium |

2 | 0.0003–0.003 | 2.5–1.5 | Not too possible | Lower |

1 | <0.0003 | <1.5 | Impossible | Low |

Risk Level | Triangular Fuzzy Number | Probability Range |
---|---|---|

Very low (VL) | (0.0, 0.0, 0.1) | <1% |

Low (L) | (0.0, 0.1, 0.3) | 1~10% |

Flat low (FL) | (0.0, 0.1, 0.3) | 10~33% |

Medium (M) | (0.3, 0.5, 0.7) | 33~66% |

Flat high (FH) | (0.5, 0.7, 0.9) | 66~90% |

High (H) | (0.7, 0.9, 1.0) | 90~99% |

Very high (VH) | (0.9, 1.0, 1.0) | >99% |

Scene Setting | Oil Spill (T) | Density (kg/m^{3}) | Leak Time (h) |
---|---|---|---|

Scenario 1 | 10 | 722 | 0.5 |

Scenario 2 | 25 | 722 | 0.5 |

Evidence Node | Expert Advice | ||||||
---|---|---|---|---|---|---|---|

X1 | FH | H | M | FH | FH | FH | H |

X2 | L | VL | VL | L | L | L | L |

X3 | M | VH | M | M | M | H | M |

X4 | FL | L | M | L | FL | M | FL |

X5 | FL | M | VH | FH | H | FH | M |

X6 | VL | L | L | L | L | VL | L |

X7 | FL | L | M | FL | M | L | FL |

X8 | FH | VH | M | H | L | FH | H |

X9 | L | M | L | FH | H | H | FH |

X10 | H | M | FH | M | M | H | M |

Risk Level | Triangular Fuzzy Number | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 |
---|---|---|---|---|---|---|---|---|---|---|---|

Very low (VL) | (0.0, 0.0, 0.1) | / | 5 | / | / | / | 2 | / | / | / | / |

Low (L) | (0.0, 0.1, 0.3) | / | 2 | / | 2 | / | 5 | 2 | 1 | 2 | / |

Relatively low (FL) | (0.0, 0.1, 0.3) | / | / | / | 3 | 1 | / | 3 | / | / | / |

Medium (M) | (0.3, 0.5, 0.7) | 1 | / | 5 | 2 | 2 | / | 2 | 1 | 1 | 4 |

Relatively high (FH) | (0.5, 0.7, 0.9) | 4 | / | / | / | 2 | / | / | 2 | 2 | 1 |

High (H) | (0.7, 0.9, 1.0) | 2 | / | 1 | / | 1 | / | / | 2 | 2 | 2 |

Very high (VH) | (0.9, 1.0, 1.0) | / | / | 1 | / | 1 | / | / | 1 | / | / |

Evidence Node | Average Triangular Fuzzy Number | N (Does Not Happen) | Y (Happen) |
---|---|---|---|

X1 | (0.53, 0.73, 0.90) | 0.28 | 0.72 |

X2 | (0.0, 0.03, 0.16) | 0.94 | 0.06 |

X3 | (0.44, 0.63, 0.79) | 0.38 | 0.62 |

X4 | (0.09, 0.21, 0.41) | 0.77 | 0.23 |

X5 | (0.47, 0.66, 0.81) | 0.35 | 0.65 |

X6 | (0.0, 0.03, 0.16) | 0.94 | 0.06 |

X7 | (0.09, 0.21, 0.41) | 0.77 | 0.23 |

X8 | (0.51, 0.69, 0.83) | 0.32 | 0.68 |

X9 | (0.39, 0.56, 0.73) | 0.44 | 0.56 |

X10 | (0.44, 0.64, 0.81) | 0.37 | 0.63 |

Scenario Setting | Oil Leakage Volume (T) | Density (kg/m^{3}) | Leakage Time (h) |
---|---|---|---|

Scenario 1 | 10 | 668 | 0.5 |

Scenario 2 | 25 | 668 | 0.5 |

Target Layer | First Level Index | Second Level Index |
---|---|---|

Emergency prevention index system | Warning source index | Accident type |

Pollutant type | ||

Occurrence region | ||

Early warning index | Affected population | |

Affected area | ||

Influence duration | ||

Region sort | ||

The maximum exceeding multiple of water quality |

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## Share and Cite

**MDPI and ACS Style**

Yang, Z.; Yan, X.; Tian, Y.; Pu, Z.; Wang, Y.; Li, C.; Yi, Y.; Wang, X.; Liu, Q.
Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake. *Water* **2023**, *15*, 2993.
https://doi.org/10.3390/w15162993

**AMA Style**

Yang Z, Yan X, Tian Y, Pu Z, Wang Y, Li C, Yi Y, Wang X, Liu Q.
Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake. *Water*. 2023; 15(16):2993.
https://doi.org/10.3390/w15162993

**Chicago/Turabian Style**

Yang, Zhimin, Xiangzhao Yan, Yutong Tian, Zaohong Pu, Yihan Wang, Chunhui Li, Yujun Yi, Xuan Wang, and Qiang Liu.
2023. "Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake" *Water* 15, no. 16: 2993.
https://doi.org/10.3390/w15162993