Evaluating the environmental parameters that determine aerobic biodegradation half-lives of pesticides in soil with a multivariable approach

A comprehensive prediction system for silkworm acute toxicity assessment of environmental and in-silico pesticides

The excessive application and loss of pesticides poses a great risk to the ecosystem, and the environmental safety assessment of pesticides is time-consuming and expensive using traditional animal toxicity tests. In this work, a pesticide acute toxicity dataset was created for silkworm integrating extensive experiments and various common pesticide formulations considering the sensitivity of silkworm to adverse environment, its economic value in China, and a gap in machine learning (ML) research on the toxicity prediction of this species, which addressed the previous limitation of only being able to predict toxicity classification without specific toxicity values. A new comprehensive voting model (CVR) was developed based on ML, combined with three regression algorithms, namely, Bayesian Ridge (BR), K Neighbors Regressor (KNN), Random Forest Regressor (RF) to accurately calculate lethal concentration 50 % (LC50). Three conformal models were successfully constructed, marking the first combination of conformal models with confidence intervals to predict silkworm toxicity. Further, the mechanism by analyzing structural alerts was summarized, and identified 25 warning structures, 24 positive compounds and 14 negative compounds. Importantly, a novel comprehensive prediction system was constructed that can provide LC50 and confidence intervals, structural alerts analysis, lipid-water partition coefficient (LogP) and similarity analysis, which can comprehensively evaluate the ecological toxicity risk of substances to make up for the incomplete toxicity data of new pesticides. The validity and generalization of the CVR model were verified by an external validation set. In addition, five new, low-toxic and green pesticide alternatives were designed through 50,000 cycles. Moreover, our software and ST Profiler can provide low-cost information access to accelerate environmental risk assessment, which can predict not only a single chemical, but also batches of chemicals, simply by inputting the SMILES / CAS / (Chinese / English) name of chemicals.

An approach for estimating microbial growth and chlorinated ethenes biotransformation in groundwater based on discrete observations

The bioremediation of chlorinated ethenes (CEs) contaminated groundwater is attracting increasingly attention in practical remediation projects. However, modelling of microbial metabolic processes under the constraints of substrate and environmental factors is inadequate. This study developed a new kinetic model, which incorporated the logistic model and Dual-Monod kinetic to represent the interaction between the controlled microbial growth and the bioavailable substrates in CE-contaminated groundwater. The proposed model was based on discrete observations to simulate microbial growth under the constraints of substrate and environmental conditions, reducing the amount of observational data required for the model. Meanwhile, the proposed model introduced two new kinetic parameters, the effective specific growth rate μeff and the real self-limiting coefficient of microbial growth keff,sl, to simplified the number of independent parameters. A parameter estimation method based on the quasi-Newton's algorithm for the proposed model was also developed. The model was validated based on the hypothetical data, experimental results, and a published dataset, demonstrated the successful simulation of microbial growth and the sequential biodegradation of PCE in groundwater systems (*E < 0.3). The monitoring duration and the sampling schedule have significant impacts on estimating the biological parameters, and large errors would be induced when the data from the periods of extremely low substrate concentration or microbial growth decline were involved in parameter estimation. The research suggested that the proposed kinetic model provided a new insight to express the limitation of microbial population growth due to the available substrates and environmental factors, and is hoping to be applied in actual CE-contaminated sites.

Impact of microplastics on nicosulfuron accumulation and bacteria community in soil-earthworms system

Microplastics (MPs) widely co-occur with various pollutants in soils. However, the data related to the impacts of MPs on terrestrial animal and microbial properties in pesticide-contaminated soils are few. In this study, the influence of MPs (0.01%, 0.1%, and 1%) on nicosulfuron concentrations in soil (10 µg/g) and earthworms were investigated, moreover, microbial community structure and diversity in soil and earthworm gut were also measured. After 30 days, the concentration of nicosulfuron in soil decreased to 1.27 µg/g, moreover, the residual concentration of nicosulfuron in soil (1%MPs and nicosulfuron) was only 44.8% of that in the single nicosulfuron treatment group. The accumulation of nicosulfuron in earthworms (1%MPs and nicosulfuron) was 7.37 µg/g, which was 1.82 times of that in the single nicosulfuron treatment group. In addition, 1% MPs decreased the richness and diversity of the soil and gut bacterial community in earthworms as well as altered microbial community composition, leading to the enrichment of specific microbial community. Our findings imply that MPs may change the migration of pesticides to terrestrial animal and as well as microbial diversity in earthworms and soil.

Occurrence, multiphase partition and risk assessment of organic amine pesticides in drinking water source of Xiang River, China

The extensive use of organic amine pesticides (OAPs) in agricultural practices has resulted in the contamination of water environments, posing threats to ecosystems and human health. This study focused on the Xiang River (XR), a representative drinking water source, as the research area to investigate the occurrence characteristics of 34 OAPs. Diphenylamine emerged as the most prevalent OAP in surface water due to industrial and agricultural activities, while cycloate dominated in sediments due to cumulative effects. Generally, the concentration of OAPs in a mixed tap water sample was lower than those in surface water samples, indicating OAPs can be removed by water plants to a certain extent. The water-sediment distribution coefficients (kd) of ΣOAPs were much less than 1 L/g, the majority of OAPs maintained relatively high concentrations in water samples instead of accumulating in sediments. Furthermore, risk assessment revealed that carbofuran showed a moderate risk to the aquatic environment, with a risk quotient of 0.23, while other OAPs presented minor risks. This study provided crucial insights for regional pesticide management and control in the XR basin, emphasizing the importance of implementing strategies to minimize the release of OAPs into the environment and protect human health.

The persistence of emamectin benzoate in marine sediments with different organic matter regimes, temperature conditions, and antibiotic presence

The objectives of this 318-day study are to determine half-lives of the anti-sea lice medication emamectin benzoate (EMB) under conditions present in sediments at aquaculture sites and document the degradation of EMB into its main metabolite desmethyl emamectin benzoate (DES). Tested conditions include different matrix types (sand, mud), two temperatures (4, 10 degrees), organic matter presence (fish feed waste and feces), and the presence of oxytetracycline. We document a transformation ratio of EMB to DES of 0.16 to 4.4 % and show that the co-presence of oxytetracycline increases EMB calculated half-lives to values >6000 days for mud matrices. EMB incubated in high organic enrichment regimes was not observed to degrade at 4 degrees. Multivariate analyses show interactions between sediment conditions (matrix, temperature, organic matter [OM], oxytetracycline) influence EMB persistence and DES:EMB ratios. Ranges of EMB half-lives and information on metabolites can be used to anticipate potential effects on marine communities.

Systematic Handling of Environmental Fate Data for Model Development-Illustrated for the Case of Biodegradation Half-Life Data

The assessment of environmental hazard indicators such as persistence, mobility, toxicity, or bioaccumulation of chemicals often results in highly variable experimental outcomes. Persistence is particularly affected due to a multitude of influencing environmental factors, with biodegradation experiments resulting in half-lives spanning several orders of magnitude. Also, half-lives may lie beyond the limits of reliable half-life quantification, and the number of available data points per substance may vary considerably, requiring a statistically robust approach for the characterization of data. Here, we apply Bayesian inference to address these challenges and characterize the distributions of reported soil half-lives. Our model estimates the mean, standard deviation, and corresponding uncertainties from a set of reported half-lives experimentally obtained for a single substance. We apply our inference model to 893 pesticides and pesticide transformation products with experimental soil half-lives of varying data quantity and quality, and we infer the half-life distribution for each compound. By estimating average half-lives, their experimental variability, and the uncertainty of the estimations, we provide a reliable data source for building predictive models, which are urgently needed by regulatory authorities to manage existing chemicals and by industry to design benign, nonpersistent chemicals. Our approach can be readily adapted for other environmental hazard indicators.

Organophosphorus pesticides in southeastern China marginal seas: Land-based export and ocean currents redistribution

Organophosphorus pesticides (OPPs) have raised an increasing public concern due to their harmful impacts. To explore the occurrence and distribution of OPPs in southeastern China marginal seas (SCMS), a sampling campaign was carried out from East China Sea (ECS) to South China Sea (SCS). A total of 33 OPPs are quantified with the ΣOPPs concentrations ranging from 4.73 to 14.15 ng/L. Higher ΣOPPs concentrations in the surface seawater from the estuaries of Yangtze River, Minjiang River, and Pearl River than those at other sampling sites indicates that riverine emissions are the principal sources of OPPs in SCMS. Different compositions of OPPs in ECS and SCS highlight the different priority of use categories for OPPs in China coastal region. In addition, the vertical diffusion and upwelling ocean currents play critical roles in the redistribution of OPPs in SCMS. For the first time, the ΣOPPs mass inventories in surface seawater of ECS and SCS are estimated at 8.51 and 11.26 t, respectively. Although the current ecological risk of OPPs is at low level in surface seawater of SCMS, the long-term use and bio-accumulative potential point to the necessity for the normalized monitoring of OPPs in China.

Scientific concepts and methods for moving persistence assessments into the 21st century

The evaluation of a chemical substance's persistence is key to understanding its environmental fate, exposure concentration, and, ultimately, environmental risk. Traditional biodegradation test methods were developed many years ago for soluble, nonvolatile, single-constituent test substances, which do not represent the wide range of manufactured chemical substances. In addition, the Organisation for Economic Co-operation and Development (OECD) screening and simulation test methods do not fully reflect the environmental conditions into which substances are released and, therefore, estimates of chemical degradation half-lives can be very uncertain and may misrepresent real environmental processes. In this paper, we address the challenges and limitations facing current test methods and the scientific advances that are helping to both understand and provide solutions to them. Some of these advancements include the following: (1) robust methods that provide a deeper understanding of microbial composition, diversity, and abundance to ensure consistency and/or interpret variability between tests; (2) benchmarking tools and reference substances that aid in persistence evaluations through comparison against substances with well-quantified degradation profiles; (3) analytical methods that allow quantification for parent and metabolites at environmentally relevant concentrations, and inform on test substance bioavailability, biochemical pathways, rates of primary versus overall degradation, and rates of metabolite formation and decay; (4) modeling tools that predict the likelihood of microbial biotransformation, as well as biochemical pathways; and (5) modeling approaches that allow for derivation of more generally applicable biotransformation rate constants, by accounting for physical and/or chemical processes and test system design when evaluating test data. We also identify that, while such advancements could improve the certainty and accuracy of persistence assessments, the mechanisms and processes by which they are translated into regulatory practice and development of new OECD test guidelines need improving and accelerating. Where uncertainty remains, holistic weight of evidence approaches may be required to accurately assess the persistence of chemicals. Integr Environ Assess Manag 2022;18:1454-1487. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Wastewater microorganisms impact the micropollutant biotransformation potential of natural stream biofilms

Biotransformation is the most important process removing manmade chemicals from the environment, yet mechanisms governing this essential ecosystem function are underexplored. To understand these mechanisms, we conducted experiments in flow-through systems, by colonizing stream biofilms under different conditions of mixing river water with treated (and ultrafiltered) wastewater. We performed biotransformation experiments with those biofilms, using a set of 75 micropollutants, and could disentangle potential mechanisms determining the biotransformation potential of stream biofilms. We showed that the increased biotransformation potential downstream of wastewater treatment plants that we observed for specific micropollutants contained in household wastewaters (downstream effect) is caused by microorganisms released with the treated effluent, rather than by the in-stream exposure to those micropollutants. Complementary data from 16S rRNA amplicon-sequencing revealed 146 amplicon sequence variants (ASVs) that followed the observed biotransformation patterns. Our results align with findings for community tolerance, and provide clear experimental evidence that microorganisms released with treated wastewater integrate into downstream biofilms and impact crucial ecosystem functions.

Predicting Primary Biodegradation of Petroleum Hydrocarbons in Aquatic Systems: Integrating System and Molecular Structure Parameters using a Novel Machine-Learning Framework

Quantitative structure-property relationship (QSPR) models for predicting primary biodegradation of petroleum hydrocarbons have been previously developed. These models use experimental data generated under widely varied conditions, the effects of which are not captured adequately within model formalisms. As a result, they exhibit variable predictive performance and are unable to incorporate the role of study design and test conditions on the assessment of environmental persistence. To address these limitations, a novel machine-learning System-Integrated Model (HC-BioSIM) is presented, which integrates chemical structure and test system variability, leading to improved prediction of primary disappearance time (DT50) values for petroleum hydrocarbons in fresh and marine water. An expanded, highly curated database of 728 experimental DT50 values (181 unique hydrocarbon structures compiled from 13 primary sources) was used to develop and validate a supervised model tree machine-learning model. Using relatively few parameters (6 system and 25 structural parameters), the model demonstrated significant improvement in predictive performance (root mean square error = 0.26, R²  = 0.67) over existing QSPR models. The model also demonstrated improved accuracy of persistence (P) categorization (i.e., "Not P/P/vP"), with an accuracy of 96.8%, and false-positive and -negative categorization rates of 0.4% and 2.7%, respectively. This significant improvement in DT50 prediction, and subsequent persistence categorization, validates the need for models that integrate experimental design and environmental system parameters into biodegradation and persistence assessment. Environ Toxicol Chem 2022;41:1359-1369. © 2022 ExxonMobil Biomedical Sciences, Inc. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Bioaugmentation of pilot-scale slow sand filters can achieve compliant levels for the micropollutant metaldehyde in a real water matrix

Metaldehyde is a polar, mobile, low molecular weight pesticide that is challenging to remove from drinking water with current adsorption-based micropollutant treatment technologies. Alternative strategies to remove this and compounds with similar properties are necessary to ensure an adequate supply of safe and regulation-compliant drinking water. Biological removal of metaldehyde below the 0.1 µg•L^-1 regulatory concentration was attained in pilot-scale slow sand filters (SSFs) subject to bioaugmentation with metaldehyde-degrading bacteria. To achieve this, a library of degraders was first screened in bench-scale assays for removal at micropollutant concentrations in progressively more challenging conditions, including a mixed microbial community with multiple carbon sources. The best performing strains, A. calcoaceticus E1 and Sphingobium CMET-H, showed removal rates of 0.0012 µg•h^-1•10⁷ cells^-1 and 0.019 µg•h^-1•10⁷ cells^-1 at this scale. These candidates were then used as inocula for bioaugmentation of pilot-scale SSFs. Here, removal of metaldehyde by A. calcoaceticus E1, was insufficient to achieve compliant water regardless testing increasing cell concentrations. Quantification of metaldehyde-degrading genes indicated that aggregation and inadequate distribution of the inoculum in the filters were the likely causes of this outcome. Conversely, bioaugmentation with Sphingobium CMET-H enabled sufficient metaldehyde removal to achieve compliance, with undetectable levels in treated water for at least 14 d (volumetric removal: 0.57 µg•L^-1•h^-1). Bioaugmentation did not affect the background SSF microbial community, and filter function was maintained throughout the trial. Here it has been shown for the first time that bioaugmentation is an efficient strategy to remove the adsorption-resistant pesticide metaldehyde from a real water matrix in upscaled systems. Swift contaminant removal after inoculum addition and persistent activity are two remarkable attributes of this approach that would allow it to effectively manage peaks in metaldehyde concentrations (due to precipitation or increased application) in incoming raw water by matching them with high enough degrading populations. This study provides an example of how stepwise screening of a diverse collection of degraders can lead to successful bioaugmentation and can be used as a template for other problematic adsorption-resistant compounds in drinking water purification.

PFAS fate and destruction mechanisms during thermal treatment: a comprehensive review

Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals and have been detected throughout the environment. Thermal treatment is the most common remediation approach for PFAS-contaminated solid wastes. Although various thermal treatment techniques have demonstrated the potential to destruct PFAS, the fate of PFAS, removal efficacy, potential emissions, and the formation of incomplete combustion products during thermal treatment are little known. This study provides a critical review on the behavior of PFAS based on different types of thermal treatment technologies with various PFAS-impacted environmental medias that include water, soil, sewage sludge, pure PFAS materials, and other PFAS-containing wastes. Different extents of PFAS thermal destruction are observed across various thermal treatment techniques and operating conditions. PFAS removal and destruction efficiencies rely heavily on PFAS structures, the complex combustion chemistry, the presence or absence of oxygen, temperature, and other operational conditions. This review also covers proposed PFAS thermal destruction mechanisms. Different thermal destruction mechanisms for perfluorooctanoic acid (PFOA) and perfluorooctanesulfonate (PFOS), and other PFAS are reviewed and compared. The majority of studies about PFAS thermal destruction mechanisms were focused on a specific list of PFAS and based mostly on the pyrolysis treatment. The basic pathway for PFAS destruction during pyrolysis is hydrodefluorination, which could be largely influenced by the alkaline condition. Future field-scale research that involves the characterization of PFAS destruction products and incomplete combustion products is needed to address public concerns and better emission control.

Highly hazardous pesticides and related pollutants: Toxicological, regulatory, and analytical aspects

The pervasive manifestation and toxicological influence of hazardous pesticides pose adverse consequences on various environmental matrices and humans, directly via bioaccumulation or indirectly through the food chain. Due to pesticide residues' continuous presence above permissible levels in multiple forms, much attention has been given to re-evaluating to regulate their usage practices without harming or affecting the environment. However, there are regulations in place banning the use of multiple hazardous pesticides in the environment. Thus, efforts must be made to achieve robust detection and complete mitigation of pesticides, possibly through a combination of new and conventional methods. The complex nature of pesticides helps them to react differently across different environmental matrices. Therefore, highly hazardous pesticides are a risk to human well-being and the environment through enzymatic inhibition and the induction of oxidative stress. Consequently, developing fast, sensitive sensing strategies is essential to detect and quantify multiple pesticides and remove the pesticides present in the specific matrix without creating harmful derivatives. Additionally, the technology should be available worldwide to eliminate pesticide residuals from the environment. There are regulations, in practice, that limit the selling, storage, use of pesticides, and their concentration in the environment, although such regulations must be revised. However, the existing literature lacks regulatory, analytical detection, and mitigation considerations for pesticide remediation. Furthermore, the enforcement of such regulations and strict monitoring of pesticides in developing countries are needed. This review spotlights various analytical detection, regulatory, and mitigation considerations for efficiently removing hazardous pesticides.

Potential of preventive bioremediation to reduce environmental contamination by pesticides in an agricultural context: A case study with the herbicide 2,4-D

One of the major problems with pesticides is linked to the non-negligible proportion of the sprayed active ingredient that does not reach its intended target and contaminates environmental compartments. Here, we have implemented and provided new insights to the preventive bioremediation process based on the simultaneous application of the pesticide with pesticide-degrading microorganisms to reduce the risk of leaching into the environment. This study pioneers such a practice, in an actual farming context. The 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) and one of its bacterial mineralizing-strains (Cupriavidus necator JMP134) were used as models. The 2,4-D biodegradation was studied in soil microcosms planted with sensitive (mustard) and insensitive (wheat) plants. Simultaneous application of a 2,4-D commercial formulation (DAM®) at agricultural recommended doses with 10⁵ cells.g^-1 dw of soil of the JMP134 strain considerably accelerated mineralization of the herbicide since its persistence was reduced threefold for soil supplemented with the mineralizing bacterium without reducing the herbicide efficiency. Furthermore, the inoculation of the Cupriavidus necator strain did not significantly affect the α- and β-diversity of the bacterial community. By tackling the contamination immediately at source, the preventive bioremediation process proves to be an effective and promising way to reduce environmental contamination by agricultural pesticides.

Data mining for pesticide decontamination using heterogeneous photocatalytic processes

Pesticides are chemical compounds used to kill pests and weeds. Due to their nature, pesticides are potentially toxic to many organisms, including humans. Among the various methods used to decontaminate pesticides from the environment, the heterogeneous photocatalytic process is one of the most effective approaches. This study focuses on artificial intelligence (AI) techniques used to generate optimum predictive models for pesticide decontamination processes using heterogeneous photocatalytic processes. In the present study, 537 valid cases from 45 articles from January 2000 to April 2020 were filtered based on their content collected and analyzed. Based on cross-industry standard process (CRISP) methodology, a set of four classifiers were applied: Decision Trees (DT), Bayesian Network (BN), Support Vector Machines (SVM), and Feed Forward Multilayer Perceptron Neural Networks (MLP). To compare the accuracy of the selected algorithms, accuracy, and sensitivity criteria were applied. After the final analysis, the DT classification algorithm with seven factors of prediction, the accuracy of 91.06%, and sensitivity of 80.32% was selected as the optimal predictor model.

Controls on species distribution and biogeochemical cycling in nitrate-contaminated groundwater and surface water, southeastern Australia

A detailed study of groundwater and surface water nitrate over four seasons across an area of varied landuse provided insights into the mechanisms that underlie accumulation and transport of nitrate. High nitrate concentrations found in a significant percentage of surface water and shallow groundwater samples are due to anthropogenic contamination. Statistics (PCA, ANOVA, parsimonious model and general linear regression) were used to explore the relationship between NO₃^- and land use, and confirmed that areas of high NO₃^- concentration are associated with dairy pasture and horticulture. Seasonally, NO₃^- levels are greater during winter, the wettest part of the year. Values of δ¹⁵N showed that most nitrate is sourced from livestock waste, with a smaller contribution from synthetic fertilizer. Direct wash-off of animal waste from dairy farms results in higher NO₃^- concentrations in surface water than in groundwater. Denitrification is an important NO₃^- attenuation mechanism which reduces NO₃^- to NH₄, as demonstrated by the PCA analysis, which showed positive correlation of NO₃^- concentrations with dissolved oxygen and negative correlations with NH₄⁺, Fe²⁺and Mn²⁺; the latter two species may act as the electron donors necessary for reduction of NO₃^-. The often high NO₃^- concentrations in shallow groundwater are decreased by denitrification, which can occur at relatively shallow depths (<3 m). The relatively small NO₃^- concentrations in deeper groundwater are due partly to denitrification, but more to originally lower NO₃^- concentrations, as the age of deeper groundwater shows that it was recharged before agriculture was established in the study area. Overall, the study demonstrates the usefulness of hydrogeochemical characterisation and multivariate statistics in the evaluation of impacts of agricultural land-use on regional N cycling. In particular, the results show that efforts to mitigate NO₃^- pollution from farms should concentrate more on wash-off of animal waste than the contribution of nitrogenous synthetic fertilizer.

Comprehensive assessment for the residual characteristics and degradation kinetics of pesticides in Panax notoginseng and planting soil

Incorrect and excess usage of pesticides during crop cultivation poses a serious threat to human health and ecosystems. In this study, we tested for the presence of 201 pesticide residues in 90 batches of Panax notoginseng (P. notoginseng) and 10 batches of planting soil. Pesticide residue characteristics and the relationship between pesticides present in P. notoginseng and the soil were discussed. Twenty-nine pesticides were detected in P. notoginseng samples and 15 pesticides were found in the soil samples. In P. notoginseng samples, the 68.9% of the identified pesticides were fungicides, and six fungicides (procymidone, iprodione, pyrimethanil, propiconazole, dimethomorph and tebuconazole) were found in >90% of the samples. Nine insecticides were found, with one insecticide, chlorpyrifos, detected in 93.3% of the P. notoginseng samples. The residual concentrations of 17 pesticides were found at levels exceeded the "non-Chinese" maximum residue levels (MRLs) for Ginseng and 17 pesticides were found at levels exceeding the MRLs set by China for "pollution-free" P. notoginseng. We observed no significant differences in pesticide residues were found on P. notoginseng from different cultivation areas. We also analyzed the degradation kinetics of pesticides in the soil, as well as their bioconcentration factors (BCFs), and found that the fungicides iprodione and myclobutanil displayed strong uptake from the soil to the root of P. notoginseng. Together, our data suggest that fungicides should be considered as key monitoring substances in P. notoginseng and planting soil.

Rapid and efficient removal of acetochlor from environmental water using Cr-MIL-101 sorbent modified with 3, 5-Bis(trifluoromethyl)phenyl isocyanate

The excessive use of acetochlor (ACT), a commonly used herbicide with latent endocrine disrupting functions, causes surface water pollution. The efficient removal of ACT from contaminated water supplies is of paramount importance. In the current work, 3,5-Bis(trifluoromethyl)phenyl isocyanate (BTP) was successfully anchored onto Cr-MIL-101 walls via covalent incorporation to afford Cr-MIL-101-BTP as a novel adsorbent for the high-efficiency removal of ACT in aqueous solutions. The kinetic adsorption process, adsorption isotherms, adsorbent regeneration, and key parameters, such as adsorbent dosage, pH value, and ionic strength, for the adsorption of ACT were studied. Results showed that a pseudo-second-order rate equation effectively describes the adsorption kinetics. The Langmuir model exhibited a better fit to adsorption isotherm than the Freundlich model. Given the π-π stacking and hydrogen bond interaction, the adsorption capacity in Cr-MIL-101-BTP approached a maximum of 312.5 mg/g for ACT, which was considerably higher than the adsorption capacities of many other reported adsorbents. The excellent adsorption characteristics of Cr-MIL-101-BTP toward ACT were preserved in a wide pH range and high concentration of background electrolytes. In addition, the result showed that partition coefficient (PC) of Cr-MIL-101-BTP was 356.14 mg/g/μM at 5 mg/L of ACT concentration, which was found as the outperformer in all tested subjects. The ACT adsorption capacity of Cr-MIL-101-BTP at the breakthrough point was greatly influenced by initial concentration, and could be described by the Thomas model. Regeneration experiments indicated that the Cr-MIL-101-BTP was recycled at least six times without significant loss of adsorption capacity. Moreover, Cr-MIL-101-BTP did not show cytotoxic activity against the tested HepG2 cell lines and did not pose serious risks to Daphnia carinata survival (48 h LC₅₀ = 446.6 μg/mL). These results prefigured the promising potential of Cr-MIL-101-BTP as a novel adsorbent for the efficient removal of ACT from aqueous solutions.

Fall Creek Monitoring Station: Using Environmental Covariates To Predict Micropollutant Dynamics and Peak Events in Surface Water Systems

This research aimed to further our understanding of how environmental processes control micropollutant dynamics in surface water systems as a means to predict peak concentration events and inform intermittent sampling strategies. We characterized micropollutant concentrations in daily composite samples from the Fall Creek Monitoring Station over 18 months. These data were compiled alongside environmental covariates, including daily measurements of weather, hydrology, and water quality parameters, to generate a novel data set with high temporal resolution. We evaluated the temporal trends of several representative micropollutants, along with cumulative metrics of overall micropollutant contamination, by means of multivariable analyses to determine which combination of covariates best predicts micropollutant dynamics and peak events. Peak events of agriculture-derived micropollutants were best predicted by positive associations with turbidity and UV₂₅₄ absorbance and negative associations with baseflow index. Peak events of wastewater-derived micropollutants were best predicted by positive associations with alkalinity and negative associations with streamflow rate. We demonstrate that these predictors can be used to inform intermittent sampling strategies aimed at capturing peak events, and we generalize the approach so that it could be applied in other watersheds. Finally, we demonstrate how our approach can be used to contextualize micropollutant data derived from infrequent grab samples.

Tolerance and remedial potential of trees submitted to atrazine and sulfentrazone in the rhizosphere

Residual herbicides are important for agricultural production but they may be lost underground. Trees capable of removing or degrading these products are interesting to the agro system. The objective was to evaluate the tolerance and remedial potential of trees in soil contaminated by atrazine and sulfentrazone. The experiment was arranged in a 7 × 3 factorial scheme. Factor one was composed of Inga edulis Mart., Myrsine gardneriana A.DC., Schizolobium parahyba (Vell.) Blake, Toona ciliata M. Roem., Trichilia hirta L. and Triplaris americana L. Factor two consisted of monthly solutions of atrazine (1000 g ha^-1), sulfentrazone (150 g ha^-1) and water only (control), applied through subgrade irrigation. The following parameters were evaluated: visual intoxication, plant growth, and biomass accumulation. Cucumber biomass was used as an indicator of herbicide residues in soil. Symptoms of intoxication were found only in S. parahyba and T. americana. Growth and biomass of the species were not affected by herbicides, except for T. americana. The herbicides provided higher biomass for T. hirta. Saplings of I. edulis, M. gardneriana, S. parahyba, T. ciliate, and T. hirta tolerate atrazine and sulfentrazone. Triplaris americana is sensitive to sulfentrazone. Inga edulis decreased sulfentrazone residues in the soil.

A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals

The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R² = 0.89, Q²_loo = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation.