Modeling Ozone Reaction Rate Constants of Micropollutants Using Quantitative Structure-Property Relationships

Jin, Xiaohui; Peldszus, Sigrid; Sparkes, Douglas

doi:10.1080/01919512.2014.910444

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…This model requires rate constants for each micropollutant screened as input. If these rate constants are not available in the literature, then k OH values can be predicted using the model developed in this paper, whereas k O3 values can be obtained using an earlier developed QSPR model (Jin et al, 2014).…”

Section: Practical Applications In the Water Industrymentioning

confidence: 99%

Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling

Jin¹,

Peldszus

Huck

2015

Chemosphere

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Section: Practical Applications In the Water Industrymentioning

confidence: 99%

Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling

Jin¹,

Peldszus

Huck

2015

Chemosphere

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“…Algorithms. All satisfactory QSPRs for reaction with ozone (O 3 ) were regressions, such as the MLR QSPRs by Jin et al (2012) (r 2 ¼ 0.964), 124 Sudhakaran and Amy (2013) (N ¼ 55; r 2 ¼ 0.832) 101 and Zhu et al (2014) (N ¼ 27; r 2 ¼ 0.562-0.723) 125 for diverse compounds. Inter-dataset correlations have been found between QSPR endpoints, including the correlation found by Lee and von Gunten (2012) for O 3 /ClO 2 and O 3 /HOCl for aromatics (both r 2 ¼ 0.94).…”

Section: Ozonation (O 3 )mentioning

confidence: 94%

A review of quantitative structure–property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Nolte

Ragas

2017

Environ. Sci.: Processes Impacts

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Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

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“…The data set for k O3 (in the unit of M –1 ·s –1 ) is collected from published literature. ,, The experimental k O3 values are approximately at pH = 7 and 22 ± 3 °C. If there are multiple k O3 values for one compound, an average value is taken.…”

Section: Methodsmentioning

confidence: 99%

“…In this regard, the QSPR method based on the concept that the molecular properties/activities are determined by their structures has been gaining popularity . Thus, far, numerous QSPR models have been developed for evaluating the aqueous phase hydroxyl radical involved reaction rate constants, ,− while QSPR models built for calculating the ozonation second-order rate constant ( k O3 ) are relatively few. ,, Recently, our group proposed a method used combinational quantum chemical and norm descriptors to develop a QSPR model for k • OH prediction, the model showed very promising results, which provides a good thought to develop models for evaluating the k O3 values.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Predicting Rate Constants of Ozone Reaction with Micropollutants

Shi

Jiang

Wang

et al. 2022

Ind. Eng. Chem. Res.

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The second-order rate constants of organic contaminants degraded by ozone (k O3 ) are of great importance for evaluating their treatment efficiency and optimizing treatment processes. In this work, several supervised machine learning (ML) algorithms, including multiple linear regression (MLR), support vector machine with radial basis function kernels (SVM-RBF), decision tree (DT), random forest (RF), and deep neutral network (DNN) methods, were used to develop quantitative structure− property relationship (QSPR) models for the estimation of log k O3 . What is more, a series of quantum chemical and newly proposed norm descriptors was successfully used in developing ML models as inputs. The statistical parameters correlation coefficient (R 2 ), mean square error (MSE), mean absolute error (MAE), and external validation parameter (Q ext 2 ) were used to evaluate the accuracy, robustness, and predictability of the as-developed models, suggesting that the nonlinear models (especially for the RF model) have better performance in predicting log k O3 values than the linear model. It is expected that the proposed norm descriptors can be employed to evaluate other reaction rate constants or chemical properties.

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Modeling Ozone Reaction Rate Constants of Micropollutants Using Quantitative Structure-Property Relationships

Cited by 13 publications

References 31 publications

Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling

Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling

A review of quantitative structure–property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Supervised Machine Learning Algorithms for Predicting Rate Constants of Ozone Reaction with Micropollutants

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