Predicting and comparing chronic water quality criteria from physicochemical properties of transition metals

Wang, Ying; Xu, Zihan; Rume, Tanjena; Li, Xiaomin; Fan, Wenhong

doi:10.1016/j.chemosphere.2019.125465

Cited by 5 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Seven physicochemical parameters, including atomic number (AN), relative atomic weight (AW), atomic radius (AR), atomic ionization potential (AN/ΔIP), covalent index (X 2 r), second ionization energy (I 2 ) and electrochemical potential (E 0 ), were found to be reasonably correlated with the corrected net accumulation of five heavy metals in the absence of nano-TiO 2 (Correlation> 0.5 and P < 0.1; Table S4). The results showed that the physicochemical properties affecting the net accumulation of different metals in Daphnia magna were similar to the properties identified in our previous studies on metal toxicity in the absence of nano-TiO 2 (Wang et al 2016(Wang et al , 2020.…”

Section: Development Of Qsbar Models In the Absence Of Nano-tiosupporting

confidence: 84%

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study

et al. 2023

Self Cite

View full text Add to dashboard Cite

Section: Development Of Qsbar Models In the Absence Of Nano-tiosupporting

confidence: 84%

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study

et al. 2023

Self Cite

View full text Add to dashboard Cite

“…The large area of the tanks provides a high percentage of oxygen and is sufficient for the needs of shrimp during the period of their rearing (Wang et al, 2020;Riandi et al, 2021). This study considers the breeding of shrimp in Brackish water because it is located within a water salinity ranging between 0.5-18.0 g.L -1 .…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study of Bacterial Contamination from Shrimp Ponds in Two Different Environments

Al-Maliky¹,

Hashim²,

Al-Sheraa³

2023

SJA

View full text Add to dashboard Cite

The bacterial content of the water of Metapenaeus affinis and Macrobrachium nipponense juvenile shrimp cultured in two salinities (3 and 15 g.L -1 ) were observed during 45 days. The initial and final weights of M. affinis were (0.939 ± 0.069 and 1.581 ± 0.183 g) in 3 g.L -1 , and (0.939 ± 0.065 and 2.274 ± 0.294 g) in 15 g.L -1 . While for M. nipponense, it was (0.914 ± 0.086 and 1.308 ± 0.070 g) in 3 g.L -1 , and (0.908 ± 0.070 and 1.086 ± 0.107 g) in 15 g.L -1 . The environmental factors were temperature 24-29 ˚C, acidity 7-8, and dissolved oxygen 5-10 g.L -1 . Bacterial contamination (CFU m.L -1 10⁶) was evident in treatment D and included four types of bacteria: Bacillus spp., Escherchia coli, Staphylococcus spp., Streptococcus spp., and Enterococcus spp. The survival rates of M. affinis during different breeding periods in the two salinities remained as follows:

show abstract

“…132 A Williams plot illustrates the distribution of data points, and any points outside the leverage value and ±3σ are considered as less reliable. 133,134 In addition to the definition of the AD based on the leverage method, Euclidean-based methods can also be used to detect outliers. Euclidean-based methods calculate the distance from every other point to a particular point in the data set.…”

Section: The Applicability Domainmentioning

confidence: 99%

“…162 The relevance of illumination and nanotoxicity has been confirmed in our previous study, indicating the need to consider exposure conditions in future modeling studies. 20 The "Trojan-horse effect" represents the mechanism by which NMs act as carriers 133,163 or that bind external contaminants 135,164 and then−because of the nanospecific features penetrates a cell membrane and then release toxic chemicals at higher local concentrations than they would achieve otherwise. 153 Multiple factors have been proven to be related to the Trojan horse effect of NMs, including their size, shape, surface charge and surface modification.…”

Section: Miscellaneousmentioning

confidence: 99%

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective

Zhou,

Wang,

Peijnenburg

et al. 2024

Environ. Sci. Technol.

View full text Add to dashboard Cite

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

show abstract

Predicting and comparing chronic water quality criteria from physicochemical properties of transition metals

Cited by 5 publications

References 16 publications

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study

A Comparative Study of Bacterial Contamination from Shrimp Ponds in Two Different Environments

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective

Contact Info

Product

Resources

About