Positive effect of adding probabilistic models to traditional models in the application of risk assessment of Chinese coking plants
Yanqing, R; Linfang, W; Jinhua, D
| HERO ID | 12033152 |
|---|---|
| In Press | No |
| Year | 2022 |
| Title | Positive effect of adding probabilistic models to traditional models in the application of risk assessment of Chinese coking plants |
| Authors | Yanqing, R; Linfang, W; Jinhua, D |
| Journal | Environmental Monitoring and Assessment |
| Volume | 194 |
| Issue | 10 |
| Page Numbers | 759 |
| Abstract | To date, the known models applied in China to risk assessment and cleanup level estimation still have uncertainties. To solve this problem, this study combined the advantages of the traditional model and the probabilistic risk assessment model to create a new model that fits China's exposure scenarios and enhance the accuracy of health risk assessment and cleanup level estimation. The results of applying the traditional model to the health risk assessment and cleanup level estimate in coking plants showed that the selection of point estimates influenced the results, which increased the uncertainty in the outcome of the risk assessment and cleanup level estimates. The risk assessment result of the new model adopted the 95th percentile distribution range to establish a confidence interval to solve the uncertainty of the traditional model. The cancer risk results calculated using the new model were one-fifth to one-third of those calculated using the traditional model. The results showed that using the new model could eliminate the conservativeness of the traditional model. For the cleanup level estimation, the cleanup levels calculated by the new model can control the risk by 95% for the coking plant, but the results calculated by the traditional model can only control the risk by 69-80%. Therefore, the cleanup levels obtained using the traditional model may underestimate the exposure risk of pollutants. The concentration of contaminants in the surface soil was the most sensitive variable in terms of risk outcomes, but the most important parameter for cleanup level estimation was exposure duration. This study highlighted the positive role of the new model in improving the accuracy of risk assessments and cleanup level estimation. |
| Doi | 10.1007/s10661-022-10346-8 |
| Url | https://www.ncbi.nlm.nih.gov/pubmed/36087246 |
| Is Certified Translation | No |
| Dupe Override | No |
| Is Public | Yes |
| Keyword | Coke; Environmental Monitoring; Models, Statistical; Risk Assessment; Cleanup level; Coking plant; Health risk assessment; Sensitivity analysis |