Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge.

Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emerging contaminants. Data-driven machine learning (ML) models serve as promising complements. However, reported quantitative structure-biodegradation relationships or ML models on anaerobic biodegradation are mostly based on small data sets (<100 records) and neglect experimental conditions, usually achieving compromised predictions. This work aimed to develop ML models for predicting the biodegradation half-lives of organic pollutants in anaerobic environments (i.e., sediment/soil and sludge). Focusing on important features of both chemicals and experimental conditions, we first curated two data sets, one for sediment/soil (SED) and the other for sludge (SLD), covering 978 records for 206 chemicals from the literature, and then conducted a meta-analysis. Next, we built a binary classification (half-life of 30 days as the cutoff) model with an accuracy of 81% and a regression model with R2 of 0.56 for SED based on LightGBM (80% and 0.31 for SLD based on Extra tree, respectively). The model interpretations underscored the significance of experimental conditions (e.g., temperature and inoculum dosage), as evidenced by their high feature importance, and the models were found to correctly capture the effects of chemical substructures, for example, branched structures and aromatic rings prolonged half-lives while methyl group and ortho-substitution on rings shortened half-lives. The applicability domains of the models were also defined, resulting in reasonable prediction for the half-lives of 41% (SED) or 67% (SLD) of over 4000 persistent, bioaccumulative, and toxic chemicals. Overall, this study pioneers ML models for predicting the anaerobic degradation half-lives, offering valuable support for future evaluation and implementation of chemical anaerobic biodegradation.

Efficient smartphone-based measurement of phosphorus in water.

Agricultural runoff is one of the main sources of excess phosphorus (P) in different water bodies, subsequently leading to eutrophication and harmful algal blooms. To effectively monitor P levels in water, there is a need for simple measurement tools and extensive public involvement to enable regular and widespread sampling. Several smartphone-based P measurement methods have been reported, which extract red-green-blue (RGB) values from colorimetric reactions to build statistical regression models for P quantification. However, these methods typically require meticulous light conditions, involve initial equipment investment, and have undergone limited testing for large-scale applications. To overcome these limitations, this study developed a smartphone-based, equipment-free and facile P colorimetric analysis method. Following the standard procedure of the ascorbic acid approach, colorimetric reactions were captured by a smartphone camera, and RGB values were extracted using Python code for modeling. Different indoor light conditions, phone types, containers, and types of water samples were examined, resulting in a collection of 1922 images. The best regression model, employing random forest with RGB values and container types as inputs, achieved an R2 of 0.97 and an RMSE of 0.051 for P concentrations ranging from 0.01 to 1.0 mg P/L. Additionally, the optimal classification model could estimate the level of P below 0.1 mg P/L with an accuracy of 95.2 (or 77.4 % for <0.05 mg P/L). The strong performance of the developed models, which are also available freely online, suggests an easy and effective tool for more frequent P measurement and greater public involvement.

Metabolomics-based estimation of activated sludge microbial composition and prediction of filamentous bulking.

Understanding the structure and activity of activated sludge (AS) microbiome is key to ensuring optimal operation of wastewater treatment processes. While high-throughput metagenomics offers a comprehensive view of AS microbiome, its cost and time demands warrant alternative approaches. This study employed machine learning methods to integrate metabolomic and metagenomic data, enabling predictions of selected microbial abundances from metabolite profiling. Model training relied on rich microbial and metabolite abundance data collected in an intensively sampled AS system, including a period of filamentous bulking, as well as a few other AS systems. Multiple linear regression out-competed other three algorithms in achieving relatively high prediction accuracy (R2 = 0.70±0.02) for the abundances of 10 selected, either keystone or core metagenome-assembled genomes (MAGs). The model predicted the abundances of filamentous Microtrichaceae and Thiotrichaceae during bulking with an error range of 14-17.8 %. This predictive power extends beyond the specific system studied, showcasing potentials for broader applications across other AS systems. Aspartate, glycine, and folate were the most influential metabolite features contributing to model performance, which were also effective indicators for filamentous bulking, with up to one week of early warning potential. This study pioneers the application of metabolomics for fast, relatively accurate and cost-effective prediction of AS community composition, enabling proactive management of AS systems towards improved efficiency and stability.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models.

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

Machine learning framework for predicting cytotoxicity and identifying toxicity drivers of disinfection byproducts.

Drinking water disinfection can result in the formation disinfection byproducts (DBPs, > 700 have been identified to date), many of them are reportedly cytotoxic, genotoxic, or developmentally toxic. Analyzing the toxicity levels of these contaminants experimentally is challenging, however, a predictive model could rapidly and effectively assess their toxicity. In this study, machine learning models were developed to predict DBP cytotoxicity based on their chemical information and exposure experiments. The Random Forest model achieved the best performance (coefficient of determination of 0.62 and root mean square error of 0.63) among all the algorithms screened. Also, the results of a probabilistic model demonstrated reliable model predictions. According to the model interpretation, halogen atoms are the most prominent features for DBP cytotoxicity compared to other chemical substructures. The presence of iodine and bromine is associated with increased cytotoxicity levels, while the presence of chlorine is linked to a reduction in cytotoxicity levels. Other factors including chemical substructures (CC, N, CN, and 6-member ring), cell line, and exposure duration can significantly affect the cytotoxicity of DBPs. The similarity calculation indicated that the model has a large applicability domain and can provide reliable predictions for DBPs with unknown cytotoxicity. Finally, this study showed the effectiveness of data augmentation in the scenario of data scarcity.

Phosphate removal by low-cost industrial byproduct iron shavings: Efficacy and longevity.

Iron shavings (IS) are low-cost industrial byproducts that show great potential in removing phosphorus (P) from contaminated water. This work investigates the effectiveness of IS for P (PO4-P) removal and emphasizes its pretreatment and longevity. A 4-d pretreatment of IS with 2.5 % NaCl resulted in a significant increase in P adsorption capacity, from approximately 1.0 to 2.5 mg/g. In column tests, the P removal efficiency remained above 60 % over 60 d, with a capacity of 4.1 mg P/g. Longevity tests involved seven adsorption-regeneration cycles, with an effective IS regeneration by 1 N NaOH and neutralization by HCl solution (pH=2), and the P adsorption capacity only slightly decreased from 2.14 to 1.75 mg P/g. To significantly improve the IS regeneration operation, we employed induction heating and compared an intermittent 10-s induction heating with an isothermal hot NaOH (85 ℃) treatment in 10-min desorption tests (95.3 % versus 56.6 % regeneration). We further found that IH completely regenerated IS in 5 min with 100 s of IH application, but 30 min were needed for hot NaOH (85 ℃) treatment. SEM/EDX, XRD, and XPS tests were conducted to track the changes in the morphology, crystallinity, and surface oxidation products of IS in the cycle tests. Notably, IS surface changed from coarse to smooth with fewer reactive sites and a higher conversion of amorphous Fe oxides to more crystalline Fe3O4, resulting in lower reactivity and fewer exposed Fe0 sites over multiple cycles. All of these mechanisms contributed to the deterioration in P removal capacity. Overall, this study provides a solid foundation for applying low-cost IS in effectively removing P from agricultural runoff.

Abiotic Reduction of Organic and Inorganic Compounds by Fe(II)-Associated Reductants: Comprehensive Data Sets and Machine Learning Modeling.

Iron-associated reductants play a crucial role in providing electrons for various reductive transformations. However, developing reliable predictive tools for estimating abiotic reduction rate constants (logk) in such systems has been impeded by the intricate nature of these systems. Our recent study developed a machine learning (ML) model based on 60 organic compounds toward one soluble Fe(II)-reductant. In this study, we built a comprehensive kinetic data set covering the reactivity of 117 organic and 10 inorganic compounds toward four major types of Fe(II)-associated reductants. Separate ML models were developed for organic and inorganic compounds, and the feature importance analysis demonstrated the significance of resonance structures, reducible functional groups, reductant descriptors, and pH in logk prediction. Mechanistic interpretation validated that the models accurately learned the impact of various factors such as aromatic substituents, complexation, bond dissociation energy, reduction potential, LUMO energy, and dominant reductant species. Finally, we found that 38% of the 850,000 compounds in the Distributed Structure-Searchable Toxicity (DSSTox) database contain at least one reducible functional group, and the logk of 285,184 compounds could be reasonably predicted using our model. Overall, the study is a significant step toward reliable predictive tools for anticipating abiotic reduction rate constants in iron-associated reductant systems.

Oxide- and Silicate-Water Interfaces and Their Roles in Technology and the Environment.

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by "anomalies" or "non-idealities" such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide- and silicate-water interfaces. This critical review discusses how science progresses from understanding ideal solid-water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. We anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

[Phenotypic and genetic analysis of a child with partial trisomy 7q].

OBJECTIVE: To define the nature and origin of a chromosomal aberration in a child with unexplained growth and development retardation, and to analyze its genotype-phenotype correlation. METHODS: A child who had presented at the Affiliated Children's Hospital of Zhengzhou University on July 9, 2019 was selected as the study subject. Chromosomal karyotypes of the child and her parents were determined with routine G-banding analysis. Their genomic DNA was also analyzed with single nucleotide polymorphism array (SNP array). RESULTS: Karyotyping analysis combined with SNP array suggested that the chromosomal karyotype of the child was 46,XX,dup(7)(q34q36.3), whilst no karyotypic abnormality was found in either of her parents. SNP array has identified a de novo 20.6 Mb duplication at 7q34q36.3 [arr[hg19] 7q34q36.3(138335828_158923941)×3] in the child. CONCLUSION: The partial trisomy 7q carried by the child was rated as a de novo pathogenic variant. SNP array can clarify the nature and origin of chromosomal aberrations. Analysis of the correlation between genotype and phenotype can facilitate the clinical diagnosis and genetic counseling.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

A de novo low-frequency mosaic variant of KIF1A causes hereditary spastic paraplegia: A literature review.

OBJECTIVE: The objective of this study was to investigate the pathogenesis and inheritance pattern of a Chinese Han family with hereditary spastic paraplegia and to retrospectively analyze the characteristics of KIF1A gene variants and related clinical manifestations. METHODS: High-throughput whole-exome sequencing was performed on members of a Chinese Han family with a clinical diagnosis of hereditary spastic paraplegia, and the sequencing results were validated by Sanger sequencing. Deep high-throughput sequencing was performed on subjects with suspected mosaic variants. The previously reported pathogenic variant loci of the KIF1A gene with complete data were collected, and the clinical manifestations and characteristics of the pathogenic KIF1A gene variant were analyzed. RESULTS: A pathogenic heterozygous variant located in the neck coil of the KIF1A gene (c.1139G>C, p.Arg380Pro) was identified in the proband and four additional members of the family. It was derived from the de novo low-frequency somatic-gonadal mosaicism of the proband's grandmother and had a rate of 10.95%. INTERPRETATION: This study helps us to better understand the pathogenic mode and characteristics of mosaic variants, and to understand the location and clinical characteristics of pathogenic variants in KIF1A.

[Clinical characteristics and genetic variant analysis of a child with Snijders Blok-Campeau syndrome].

OBJECTIVE: To analyze the clinical phenotype and genetic variant of a child with Snijders Blok-Campeau syndrome (SBCS). METHODS: A child who was diagnosed with SBCS in June 2017 at Henan Children's Hospital was selected as the study subject. Clinical data of the child was collected. Peripheral blood samples of the child and his parents were collected and the extraction of genomic DNA, which was subjected to trio-whole exome sequencing (trio-WES) and genome copy number variation (CNV) analysis. Candidate variant was verified by Sanger sequencing of his pedigree members. RESULTS: The main clinical manifestations of the child have included language delay, intellectual impairment and motor development delay, which were accompanied with facial dysmorphisms (broad forehead, inverted triangular face, sparse eyebrows, widely spaced eyes, narrow palpebral fissures, broad nose bridge, midface hypoplasia, thin upper lip, pointed jaw, low-set ears and posteriorly rotated ears). Trio-WES and Sanger sequencing revealed that the child has harbored a heterozygous splicing variant of the CHD3 gene, namely c.4073-2A>G, for which both of his parents were of wild-type. No pathogenic variant was identified by CNV testing. CONCLUSION: The c.4073-2A>G splicing variant of the CHD3 gene probably underlay the SBCS in this patient.

Short-term Lake Erie algal bloom prediction by classification and regression models.

The recent outbreaks of harmful algal blooms in the western Lake Erie Basin (WLEB) have drawn tremendous attention to bloom prediction for better control and management. Many weekly to annual bloom prediction models have been reported, but they only employ small datasets, have limited types of input features, build linear regression or probabilistic models, or require complex process-based computations. To address these limitations, we conducted a comprehensive literature review, complied a large dataset containing chlorophyll-a index (from 2002 to 2019) as the output and a novel combination of riverine (the Maumee & Detroit Rivers) and meteorological (WLEB) features as the input, and built machine learning-based classification and regression models for 10-d scale bloom predictions. By analyzing the feature importance, we identified 8 most important features for the HAB control, including nitrogen loads, time, water levels, soluble reactive phosphorus load, and solar irradiance. Here, both long- and short-term nitrogen loads were for the first time considered in HAB models for Lake Erie. Based on these features, the 2-, 3-, and 4-level random forest classification models achieved an accuracy of 89.6%, 77.0%, and 66.7%, respectively, and the regression model achieved an R2 value of 0.69. In addition, long-short term memory (LSTM) was implemented to predict temporal trends of four short-term features (N, solar irradiance, and two water levels) and achieved the Nash-Sutcliffe efficiency of 0.12-0.97. Feeding the LSTM model predictions for these features into the 2-level classification model reached an accuracy of 86.0% for predicting the HABs in 2017-2018, suggesting that we can provide short-term HAB forecasts even when the feature values are not available.

TNAP-a potential cytokine in the cerebral inflammation in spastic cerebral palsy.

Objective: Several studies have shown the significance of neuroinflammation in the pathological progress of cerebral palsy (CP). However, the etiology of CP remains poorly understood. Spastic CP is the most common form of CP, comprising 80% of all cases. Therefore, identifying the specific factors may serve to understand the etiology of spastic CP. Our research aimed to find some relevant factors through protein profiling, screening, and validation to help understand the pathogenesis of cerebral palsy. Materials and methods: In the current study, related clinical parameters were assessed in 18 children with spastic CP along with 20 healthy individuals of the same age. Blood samples of the spastic CP children and controls were analyzed with proteomics profiling to detect differentially expressed proteins. On the other hand, after hypoxic-ischemic encephalopathy (HIE) was induced in the postnatal day 7 rat pups, behavioral tests were performed followed by detection of the differentially expressed markers and inflammatory cytokines in the peripheral blood and cerebral cortex of the CP model rats by Elisa and Western blot. Independent sample t-tests, one-way analysis of variance, and the Pearson correlation were used for statistical analysis. Results: Through proteomic analysis, differentially expressed proteins were identified. Among them, tissue-nonspecific alkaline phosphatase (TNAP), the gene expression product of alkaline phosphatase (ALPL), was downregulated in spastic CP. In addition, significantly lower TNAP levels were found in the children with CP and model rats. In contrast, compared with the sham rats, the model rats demonstrated a significant increase in osteopontin and proinflammatory biomarkers in both the plasma and cerebral cortex on the ischemic side whereas serum 25 hydroxyvitamin D and IL-10 were significantly decreased. Moreover, serum TNAP level was positively correlated with serum CRP and IL-10 in model rats. Conclusion: These results suggest that TNAP is the potential molecule playing a specific and critical role in the neuroinflammation in spastic CP, which may provide a promising target for the diagnosis and treatment of spastic CP.

Moderating role of compassion in the link between fear of Coronavirus disease and mental health among undergraduate students.

Background: The societal challenges presented by fear related to the coronavirus disease (COVID-19) pandemic may present unique challenges for an individual's mental health. However, the moderating role of compassion in the relationship between fear of COVID-19 and mental health has not been well-studied. The present study aimed to explore the association between fear of COVID-19 and mental health, as well as test the buffering role of compassion in this relationship. Methods: The participants in this study were 325 Iranian undergraduate students (228 females), aged 18-25 years, who completed questionnaires posted on social networks via a web-based platform. Results: The results showed that fear of COVID-19 was positively related with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The results also showed that compassion was negatively associated with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The interaction-moderation analysis revealed that compassion moderated the relationship between fear of COVID-19 and subscale of mental health. Conclusion: Results highlight the important role of compassion in diminishing the effect of fear of COVID-19 on the mental health (physical symptoms, social function, depressive symptoms, and anxiety symptoms) of undergraduate students.

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water.

Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R2 = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH3 > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pKa and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion.

BACKGROUND: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. RESULTS: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. CONCLUSION: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric.

Solute descriptors have been widely used to model chemical transfer processes through poly-parameter linear free energy relationships (pp-LFERs); however, there are still substantial difficulties in obtaining these descriptors accurately and quickly for new organic chemicals. In this research, models (PaDEL-DNN) that require only SMILES of chemicals were built to satisfactorily estimate pp-LFER descriptors using deep neural networks (DNN) and the PaDEL chemical representation. The PaDEL-DNN-estimated pp-LFER descriptors demonstrated good performance in modeling storage-lipid/water partitioning coefficient (log Kstorage-lipid/water), bioconcentration factor (BCF), aqueous solubility (ESOL), and hydration free energy (freesolve). Then, assuming that the accuracy in the estimated values of widely available properties, e.g., logP (octanol-water partition coefficient), can calibrate estimates for less available but related properties, we proposed logP as a surrogate metric for evaluating the overall accuracy of the estimated pp-LFER descriptors. When using the pp-LFER descriptors to model log Kstorage-lipid/water, BCF, ESOL, and freesolve, we achieved around 0.1 log unit lower errors for chemicals whose estimated pp-LFER descriptors were deemed "accurate" by the surrogate metric. The interpretation of the PaDEL-DNN models revealed that, for a given test chemical, having several (around 5) "similar" chemicals in the training data set was crucial for accurate estimation while the remaining less similar training chemicals provided reasonable baseline estimates. Lastly, pp-LFER descriptors for over 2800 persistent, bioaccumulative, and toxic chemicals were reasonably estimated by combining PaDEL-DNN with the surrogate metric. Overall, the PaDEL-DNN/surrogate metric and newly estimated descriptors will greatly benefit chemical transfer modeling.

A comprehensive kinetic model for phenol oxidation in seven advanced oxidation processes and considering the effects of halides and carbonate.

As one of the most powerful approaches to mechanistically understanding complex chemical reactions and performing simulations or predictions, kinetic modeling has been widely used to investigate advanced oxidation processes (AOPs). However, most of the available models are built based on limited systems or reaction mechanisms so they cannot be readily extended to other systems or reaction conditions. To overcome such limitations, this study developed a comprehensive model on phenol oxidation using over 540 reactions, covering the most common reaction mechanisms in nine AOPs-four peroxymonosulfate (PMS), four peroxydisulfate (PDS), and one H2O2 systems-and considering product formation and the effects of co-existing anions (chloride, bromide, and carbonate). Existing models in the literature were first gathered and then revised by correcting inaccurately used reactions and adding other necessary reactions. Extensive model tuning and validation were next conducted by fitting the model against experimental data from both this study and the literature. The effects of anions were found to follow PDS/CuO > H2O2/UV > other PDS or PMS systems. Halogenated organic byproducts were mainly observed in the PMS systems in the presence of halides. Most of the 543 reactions were found to be important based on the sensitivity analysis, with some anions-involved reactions being among the most important, which explained why these anions substantially altered some of the reaction systems. With this comprehensive model, a deep understanding and reliable prediction can be made for the oxidation of phenol (and likely other phenolic compounds) in systems containing one or more of the above AOPs.

Enhanced dewaterability of waste activated sludge by UV assisted ZVI-PDS oxidation.

Ultraviolet (UV) assisted zero-valent iron (ZVI)-activated sodium persulfate (PDS) oxidation (UV-ZVI-PDS) was used to treat waste activated sludge (WAS) in this study. The dewaterability performance and mechanism of WAS dewatering were analyzed. The results showed that UV-ZVI-PDS can obtain better sludge dewatering performance in a wide pH range (2.0-8.0). When the molar ratio of ZVI/PDS was 0.6, UV was 254nm, PDS dosage was 200 mg/g TS (total solid), pH was 6.54, reaction time was 20 min, the CST (capillary suction time) and SRF (specific resistance to filtration) were decreased by 64.0% and 78.2%, respectively. The molar ratio of ZVI/PDS used in this paper is much lower than that of literatures, and the contents of total Fe and Fe2+ in sludge supernatant remained at a low level, as 3.7 mg/L and 0.0 mg/L. The analysis of extracellular polymeric substances (EPS), scanning electron microscope (SEM) and particle size distribution showed that the EPS could be effectively destroyed by UV-ZVI-PDS, the sludge flocs broken down into smaller particles, cracks and holes appeared, and then the bound water was released. At the same time, the highly hydrophilic tightly bound-EPS (TB-EPS) were converted into loosely bound EPS (LB-EPS) and soluble EPS (S-EPS). During sludge pretreated by UV-ZVI-PDS, positively charged ions, such as Fe2+, Fe3+ and H+, produced in the reaction system could reduce the electronegativity of sludge surface, promote sludge particles aggregation, and then enhanced the sludge dewaterability.