Open AccessA new risk probability calculation method for urban ecological risk assessment
Author(s) -
Changfeng Liu,
Weiping Chen,
Ying Hou,
Lingchao Ma
Publication year - 2020
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/ab6667
Subject(s) - probabilistic logic , environmental science , beijing , risk assessment , probability distribution , joint probability distribution , monte carlo method , computer science , statistics , econometrics , geography , mathematics , computer security , archaeology , china
The ecological risk associated with urbanization is of great concern where multiple stressors and risk receptors co-exist. Probabilistic risk characterization methods were rarely applied in past urban ecological risk assessments because of the difficulties in the derivation of theoretical probability distribution functions and the definite integral calculation. Therefore, we proposed a new method which is based on computer simulation and able to facilitate the calculation of risk probabilities. This method quantifies multiple ecological risk-related indicators using ecological models, implements Monte Carlo simulation to calculate the risk probability of single indicators, and applies the copula model to calculate the joint risk probability of multiple indicators. We conducted an assessment of urban ecological risk related to urban surface water environment in Beijing as a case study to validate this method. The results show that the means of surface runoff risk probability, total nitrogen pollutant load risk probability, and comprehensive (joint) risk probability were 0.33, 0.44, and 0.23, respectively, in the areas within Beijing Sixth Ring Road. All three types of risk were at moderate levels in the study areas, but exhibited high spatial heterogeneity and urban–suburban gradient. The average contributions of the three risk types were 25% (surface runoff risk), 32% (total nitrogen pollutant load risk), and 43% (comprehensive risk), indicating that the joint risk was overall the major risk type. In conclusion, our method considering multiple indicators and their probabilistic attributes can handle the uncertainties in ecological models and thus has potential to evaluate different types of urban ecological risks.