Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models

Petoumenou, Maria I.; Pizzo, Fabiola; Cester, Josep; Fernández, Alberto; Benfenati, Emilio

doi:10.1016/j.envres.2015.08.008

Cited by 26 publications

(16 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Our results (Table 6) agree with Petoumenou et al [19], who examined the performance of VEGA and EPISuite on data provided by the industry. • The RMSE values of TEST and EPISuite we found are similar with those reported in Table 1.…”

Section: Discussionsupporting

confidence: 90%

“…This depends on the type of chemical families, but also on the user choices about the Applicability Domain (AD) thresholds (since for some of the tools the AD is not clearly defined), and the exclusion of compounds already present in the model's training set. The work of Petoumenou et al [19], is the only one to evaluate data coming from the industrial context, i.e. extracted from the European Chemical Agency (ECHA) database [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

Lunghini

Marcou

Azam

et al. 2019

SAR and QSAR in Environmental Research

View full text Add to dashboard Cite

The bioconcentration factor (BCF), a key parameter required by the REACH regulation, estimates the tendency for a xenobiotic to concentrate inside living organisms. In silico methods can be valid alternatives to costly data measurements. However, in the industrial context, these theoretical approaches may fail to predict BCF with reasonable accuracy. We analyzed whether models built on public data only have adequate performances when challenged to predict industrial compounds. A new set of 1129 compounds has been collected by merging publicly available datasets. Generative Topographic Mapping was employed to compare this chemical space with a set of new compounds issued from the industry. Some new chemotypes absent in the training set (such as siloxanes) have been detected. A new BCF model has been built using ISIDA (In SIlico design and Data Analysis) fragment descriptors, support vector regression and random forest machinelearning methods. It has been externally validated on: (i) collected data from the literature and (ii) industrial data. The latter also served as benchmark for the freely available tools VEGA, EPISuite, TEST, OPERA. New model performs (RMSE of 0.58 log BCF units) comparably to existing ones but benefits of an extended applicability, covering the industrial set chemical space (78% data coverage).

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

Lunghini

Marcou

Azam

et al. 2019

SAR and QSAR in Environmental Research

View full text Add to dashboard Cite

show abstract

“…Over the past few decades, researchers and energy experts are closely collaborating in a wide range of research activities to mitigate the harmful impacts of power generation to ensure sustainable power generation and environmental protection. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

View full text Add to dashboard Cite

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

show abstract

“…Establishment of the biological accumulation factor BCF was calculated as the ratio of the average analyte level in a selected tissue of an organism, expressed as mg/kg wet weight (C shellfish ), to the analyte concentration in water, expressed as mg/L (C seawater ) (Petoumenou et al, 2015;Gissi et al, 2015). The accumulation ability can be influenced by various biological and ecological factors, including geographic area, age, size, sex, tissue type, diet and temporal distribution (Bragigand et al, 2006;Dirtu et al, 2016).…”

Section: Early Warning Values For Sem In Seawatermentioning

confidence: 99%

Environmental status and early warning value of the pollutant Semicarbazide in Jincheng and Sishili Bays, Shandong Peninsula, China

Tian

Gong

et al. 2017

Science of The Total Environment

View full text Add to dashboard Cite

• An ultra performance liquid chromatography-tandem mass spectrometry was developed for determining Semicarbazide, with short time, high sensitivity and accurate characterization.• The temporal and spatial distribution of Semicarbazide in seawater was measured and the highly polluted areas identified and traced.• The distribution of Semicarbazide in sediment and shellfish was also analyzed.• An early warning value 0.05 μg/L in seawater was deduced. A verified method for measuring Semicarbazide (SEM) in seawater, sediments, and shellfish was developed based on ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). A total of 30 stations were radially distributed in Jincheng and Sishili Bays in the Bohai and Yellow Seas, and 1025 monitoring data were collected in 41 voyages, 615 seawater samples, 320 sediment samples and 90 shellfish samples. The concentration ranged from 0.011 μg/L to 0.093 μg/L and 0 to 0.75 μg/kg in seawater and shellfish respectively, but SEM in sediment was all below the limit of detection. Temporal and spatial distribution of SEM was investigated using multivariate analysis to estimate the degree of SEM pollution. Based on the SEM concentration in the three sample types, together with our previous findings, early warning values were deduced for SEM in seawater, and the developed method overcame shortcomings with existing technologies. The results may be helpful to draft national baseline values for SEM in seawater and sediments, and provide a scientific basis for assessing the impacts of SEM on marine ecology and human health. G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o

show abstract

Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models

Cited by 26 publications

References 22 publications

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Environmental status and early warning value of the pollutant Semicarbazide in Jincheng and Sishili Bays, Shandong Peninsula, China

Contact Info

Product

Resources

About