Dataset Information

Validation of AIST-SHANEL Model Based on Spatiotemporally Extensive Monitoring Data of Linear Alkylbenzene Sulfonate in Japan: Toward a Better Strategy on Deriving Predicted Environmental Concentrations.

ABSTRACT: Strategies for deriving predicted environmental concentrations (PECs) using environmental exposure models have become increasingly important in the environmental risk assessment of chemical substances. However, many strategies are not fully developed owing to uncertainties in the derivation of PECs across spatially extensive areas. Here, we used 3-year environmental monitoring data (river: 11?702 points; lake: 1867 points; sea: 12 points) on linear alkylbenzene sulfonate (LAS) in Japan to evaluate the ability of the National Institute of Advanced Industrial Science and Technology (AIST)-Standardized Hydrology-Based Assessment Tool for the Chemical Exposure Load (SHANEL) model developed to predict chemical concentrations in major Japanese rivers. The results indicate that the estimation ability of the AIST-SHANEL model conforms more closely to the actual measured values in rivers than it does for lakes and seas (correlation coefficient: 0.46; proportion within the 10× factor range: 82%). In addition, the 95th percentile, 90th percentile, 50th percentile, and mean values of the distributions of the measured values (14 µg/L, 8.2 µg/L, 0.88 µg/L, and 3.4 µg/L, respectively) and estimated values (19 µg/L, 13 µg/L, 1.4 µg/L, and 4.2 µg/L, respectively) showed high concordance. The results suggest that AIST-SHANEL may be useful in estimating summary statistics (e.g., 95th and 90th percentiles) of chemical concentrations in major rivers throughout Japan. Given its practical use and high accuracy, these environmental risk assessments are suitable for a wide range of regions and can be conducted using representative estimated values, such as the 95th percentile. Integr Environ Assess Manag 2019;15:750-759. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

SUBMITTER: Nishioka T

PROVIDER: S-EPMC6852430 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Validation of AIST-SHANEL Model Based on Spatiotemporally Extensive Monitoring Data of Linear Alkylbenzene Sulfonate in Japan: Toward a Better Strategy on Deriving Predicted Environmental Concentrations.

Nishioka Tohru T Iwasaki Yuichi Y Ishikawa Yuriko Y Yamane Masayuki M Morita Osamu O Honda Hiroshi H

Integrated environmental assessment and management 20190809 5

Strategies for deriving predicted environmental concentrations (PECs) using environmental exposure models have become increasingly important in the environmental risk assessment of chemical substances. However, many strategies are not fully developed owing to uncertainties in the derivation of PECs across spatially extensive areas. Here, we used 3-year environmental monitoring data (river: 11 702 points; lake: 1867 points; sea: 12 points) on linear alkylbenzene sulfonate (LAS) in Japan to evalua ...[more]

PMID: 31050181

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Project description:BackgroundThe relationship between the Epworth sleepiness scale (ESS) and the apnea-hypopnea index (AHI) is uncertain and even poor. The major problem associated with the ESS might be a lack of consideration of weight in prediction in clinical practice. Would awarding different item-scores to the four scales of ESS items to develop a weighted ESS scoring system improve the accuracy of the AHI prediction? It is warranted to explore the intriguing hypotheses.MethodsSeven hundred fifty-six adult patients with suspicion of obstructive sleep apnoea syndrome (OSAS) were prospectively recruited to a derivation cohort. This was tested against a prospective validation cohort of 810 adult patients with suspected OSAS. Each ESS item’s increased odds ratio for the corresponding AHI was calculated using univariate logistic regression. The receiver operating characteristic curves were created and the areas under the curves (AUCs) were calculated to illustrate and compare the accuracy of the indices.ResultsThe higher the ESS item-score, the closer the relationship with the corresponding AHI. The odds ratios decreased as a result of the increased AHI. The ESS items were of unequal weight in predicting the corresponding AHI and a weighted ESS was developed. The coincidence rates with the corresponding AHI, body mass indices, and neck circumferences rose as the scores increased, whereas nocturnal nadir oxygen saturations decreased, and the weighted ESS was more strongly associated with these indices, compared with the ESS. The capability in predicting the patients without OSAS or with severe OSAS was strong, especially the latter, and the weighted ESS orchestrated manifest improvement in screening the patients with simple snoring. The patterns of sensitivities, specificities, and Youden’s indices of the four ranks of weighted ESS for predicting the corresponding AHI were better than those of the ESS, and the AUCs of weighted ESS were greater than the corresponding areas of ESS in the two cohorts.ConclusionsThe weighted ESS orchestrated significant improvement in predicting the AHI, indicating that the capability in predicting the patients without OSAS or with severe OSAS was strong, which might have implications for clinical triage decisions to prioritize patients for polysomnography.

Dataset Information

Validation of AIST-SHANEL Model Based on Spatiotemporally Extensive Monitoring Data of Linear Alkylbenzene Sulfonate in Japan: Toward a Better Strategy on Deriving Predicted Environmental Concentrations.

Publications

Validation of AIST-SHANEL Model Based on Spatiotemporally Extensive Monitoring Data of Linear Alkylbenzene Sulfonate in Japan: Toward a Better Strategy on Deriving Predicted Environmental Concentrations.

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