Dataset Information

Chlorpyrifos degradation via photoreactive TiO2 nanoparticles: Assessing the impact of a multi-component degradation scenario.

ABSTRACT: High concentrations of pesticides enter surface waters following agricultural application, raising environmental and human health concerns. The use of photoreactive nanoparticles has shown promise for contaminant degradation and surface water remediation. However, it remains uncertain how the complexity of natural waters will impact the photodegradation process. Here, we investigate the photoreactivity of titanium dioxide nanoparticles, the capability to degrade the pesticide chlorpyrifos, and the effect of and impact on bacteria during the photodegradation process. Loss of chlorpyrifos in solution resulted solely from photocatalytic oxidation, with 80% degradation observed after 24?h in our reactor, either in the presence or absence of bacteria. Degradation of chlorpyrifos to chlorpyrifos oxon and 3,5,6-trichloro-2-pyridinol was observed via LC/MS-MS and effectively modeled for the given reactor conditions. Bacterial inactivation occurred over 60?min and was not impacted by the presence of chlorpyrifos. The relative affinity of bacteria and chlorpyrifos for the nanoparticle surface decreased the amount of Reactive Oxygen Species (ROS) detected in the bulk by up to 94%, suggesting that ROS measurements in simplified systems may overestimate the reactivity of photoreactive nanoparticles in complex environments.

SUBMITTER: Farner Budarz J

PROVIDER: S-EPMC5995634 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Chlorpyrifos degradation via photoreactive TiO<sub>2</sub> nanoparticles: Assessing the impact of a multi-component degradation scenario.

Farner Budarz Jeffrey J Cooper Ellen M EM Gardner Courtney C Hodzic Emina E Ferguson P Lee PL Gunsch Claudia K CK Wiesner Mark R MR

Journal of hazardous materials 20171211

High concentrations of pesticides enter surface waters following agricultural application, raising environmental and human health concerns. The use of photoreactive nanoparticles has shown promise for contaminant degradation and surface water remediation. However, it remains uncertain how the complexity of natural waters will impact the photodegradation process. Here, we investigate the photoreactivity of titanium dioxide nanoparticles, the capability to degrade the pesticide chlorpyrifos, and t ...[more]

PMID: 29254886

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Project description:Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

Dataset Information

Chlorpyrifos degradation via photoreactive TiO2 nanoparticles: Assessing the impact of a multi-component degradation scenario.

Publications

Chlorpyrifos degradation via photoreactive TiO<sub>2</sub> nanoparticles: Assessing the impact of a multi-component degradation scenario.

Similar Datasets

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