• Title/Summary/Keyword: Statistical extrapolation method (SEM)

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Comparative Study of Probabilistic Ecological Risk Assessment (PERA) used in Developed Countries and Proposed PERA approach for Korean Water Environment (확률생태위해성평가(PERA) 선진국 사례분석 및 국내수계에 적합한 PERA 기법 제안)

  • An, Youn-Joo;Nam, Sun-Hwa;Lee, Woo-Mi
    • Journal of Korean Society on Water Environment
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    • v.25 no.4
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    • pp.494-501
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    • 2009
  • Probabilistic Ecological risk assessment (PERA) is extensive approach to qualify and quantify risk on the multi species based on species sensitivity distribution (SSD). As a while, deterministic ecological risk assessment (DERA) considers the comparison of predicted no-effect concentration (PNEC) and predicted exposure concentration (PEC). DERA is used to determine if there is potential risk or no risk, and it doesn't consider the nature variability and the species sensitivity. But PERA can be more realistic and reasonable approach to estimate likelihood or risk. In this study, we compared PERA used in developed countries, and proposed PERA applicable for the Korean water environment. Taxonomic groups were classified as "class" level including Actinopterygill, Branchiopoda, Chlorophyceae, Maxillapoda, Insects, Bivalvia, Gastropoda, Secernentea, Polychaeta, Monocotyldoneae, and Chanophyceae in this study. Statistical extrapolation method (SEM), statistical extrapolation method $_{acutechronicratio}$ ($SEM_{ACR}$) and assessment factor method (AFM) were used to calculate the ecological protective concentration based on qualitative and quantitative levels of taxonomic toxicity data. This study would be useful to establish the PERA for the protection of aquatic ecosystem in Korea.

Derivation of Ecological Protective Concentration using the Probabilistic Ecological Risk Assessment applicable for Korean Water Environment: (I) Cadmium

  • Nam, Sun-Hwa;Lee, Woo-Mi;An, Youn-Joo
    • Toxicological Research
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    • v.28 no.2
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    • pp.129-137
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    • 2012
  • Probabilistic ecological risk assessment (PERA) for deriving ecological protective concentration (EPC) was previously suggested in USA, Australia, New Zealand, Canada, and Netherland. This study suggested the EPC of cadmium (Cd) based on the PERA to be suitable to Korean aquatic ecosystem. First, we collected reliable ecotoxicity data from reliable data without restriction and reliable data with restrictions. Next, we sorted the ecotoxicity data based on the site-specific locations, exposure duration, and water hardness. To correct toxicity by the water hardness, EU's hardness corrected algorithm was used with slope factor 0.89 and a benchmark of water hardness 100. EPC was calculated according to statistical extrapolation method (SEM), statistical extrapolation $method_{Acute\;to\;chronic\;ratio}$ ($SEM_{ACR}$), and assessment factor method (AFM). As a result, aquatic toxicity data of Cd were collected from 43 acute toxicity data (4 Actinopterygill, 29 Branchiopoda, 1 Polychaeta, 2 Bryozoa, 6 Chlorophyceae, 1 Chanophyceae) and 40 chronic toxicity data (2 Actinopterygill, 23 Branchiopoda, 9 Chlorophyceae, 6 Macrophytes). Because toxicity data of Cd belongs to 4 classes in taxonomical classification, acute and chronic EPC (11.07 ${\mu}g/l$ and 0.034 ${\mu}g/l$, respectively) was calculated according to SEM technique. These values were included in the range of international EPCs. This study would be useful to establish the ecological standard for the protection of aquatic ecosystem in Korea.

Statistical models from weigh-in-motion data

  • Chan, Tommy H.T.;Miao, T.J.;Ashebo, Demeke B.
    • Structural Engineering and Mechanics
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    • v.20 no.1
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    • pp.85-110
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    • 2005
  • This paper aims at formulating various statistical models for the study of a ten year Weigh-in-Motion (WIM) data collected from various WIM stations in Hong Kong. In order to study the bridge live load model it is important to determine the mathematical distributions of different load affecting parameters such as gross vehicle weights, axle weights, axle spacings, average daily number of trucks etc. Each of the above parameters is analyzed by various stochastic processes in order to obtain the mathematical distributions and the Maximum Likelihood Estimation (MLE) method is adopted to calculate the statistical parameters, expected values and standard deviations from the given samples of data. The Kolmogorov-Smirnov (K-S) method of approach is used to check the suitability of the statistical model selected for the particular parameter and the Monte Carlo method is used to simulate the distributions of maximum value stochastic processes of a series of given stochastic processes. Using the statistical analysis approach the maximum value of gross vehicle weight and axle weight in bridge design life has been determined and the distribution functions of these parameters are obtained under both free-flowing traffic and dense traffic status. The maximum value of bending moments and shears for wide range of simple spans are obtained by extrapolation. It has been observed that the obtained maximum values of the gross vehicle weight and axle weight from this study are very close to their legal limitations of Hong Kong which are 42 tonnes for gross weight and 10 tonnes for axle weight.