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Hydrocarbon oxidation products (HOPs) formed from crude oil and diesel were formed from laboratory simulated spills at four different periods (1, 4, 7, and 10 days) under environmental conditions that mimicked those in Cook Inlet, Alaska. Two sets of analyses were performed to identify and characterize the HOPs. The first set of analyses performed was non-targeted and included high-resolution mass spectrometry and fluorescence excitation-emission matrix spectroscopy. Liquid chromatography coupled with an Orbitrap mass spectrometer uncovered that HOPs formed from Cook Inlet (CI) crude oil and diesel are relatively reduced, saturated, and unsaturated compounds. The molecular compositions of HOPs from crude oil are more aromatic, whereas those formed from diesel are more aliphatic. Moreover, molecular signatures of naphthenic acids, a class of toxicants, in HOPs are reported. Six unique chemical features of HOPs were revealed by fluorescence excitation-emission matrix spectroscopy, including two unique petroleum signatures. The parallel factor model for fluorescence excitation-emission matrix spectroscopy accurately tracks temporal compositional changes of HOPs. The second set of analyses was targeted and included the quantification of polycyclic aromatic hydrocarbons (PAHs) and oxygenated polycyclic aromatic hydrocarbons (oxyPAHs) in HOPs using tandem mass spectrometry. Two oxyPAHs, phenanthrenequinone, and 1,4-anthraquinone were quantified in HOPs formed from CI crude oil and related to eleven PAHs. The results from this study uncover a comprehensive approach to monitoring compositional changes of hydrocarbon oxidation products in a spill event.

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Advanced economic development and technologies cause worldwide plastic waste to increase many folds, leaving policymakers with the dilemma of managing it. Synthetic solid particles or polymeric matrices of plastics with diverse shapes and sizes are the primary concern of environmental pollution of the marine ecosystem, freshwater, agriculture fields, atmosphere, food, drinking water, and other remote locations. Researchers demonstrated microplastics (MPs) as multifaceted stressors in the ecosystem, carrying toxic chemicals and vectors of transport, and described the implications of these hazardous chemicals on human health. MPs in the environment can adsorb organic, nitrogenous substances and other minerals. This complex system may promote microbial growth and aggregation. Continuous contact of microbes with MPs changes the internal arrangement of ions and atoms, alternating physio-chemical properties and becoming hydrophobic. These properties allow specific bacterial growth on MPs and promote bacterial resistance and transfer of resistance genes. MPs aged by ultra-violet light, temperature, and chemicals increase bacterial adsorption and antibiotic-resistance gene transfer synergistically. MPs are mitigated in the environment by aggregation of microbes, which leads to aging and loss of the crystalline structure of microplastic due to the release of enzymes that cause oxidation, demethylation and desertification, and hydrolysis of MPs. Aerobic conditions are preferred to degrade MPs in different environmental conditions for large-scale degradation of MPs. However, anaerobic degradation requires controlled conditions and specialized equipment. The use of a consortium of bacteria increases biodegradation efficiency. Among the microorganisms, fungi were the most effective at detoxicating xenobiotics in the environment due to their adaptability and ability to tolerate diverse conditions. This critical review analyses microplastic-induced microbial diversity and microbial adaptations to it. Furthermore, it describes MP's role in the cause of diarrhea, antimicrobial resistance, and spread. The potential use of bioremediation methods and pathways for eliminating MPs like phthalates and bisphenol from ecosystems is discussed in detail. Finally, suggestions are put forward for controlling and removing MPs from the environment.

The extraction of multiple attributes from past hours in univariate trends of hourly oxides of nitrogen (NOx) recorded at ground-level sites substantially improves NOx hourly forecasts for at least four hours ahead without assistance from exogenous-variable inputs. The method proposed is evaluated with public datasets of hourly NOx data, compiled from 2017 to 2021, for local sites from multiple cities in central England. The datasets for each urban or roadside site considered include more than 40?000 NOx hourly recordings. The period covered straddles the COVID-19-related lockdowns of 2020, associated with lower vehicle emissions that impacted NOx trends at all the studied sites extending into 2021. Fifteen trend attributes are extracted from the recorded NOx trends relating to the previous twelve hours of recorded data. The attributes considered are easily calculated and include seasonal components, recent-past-hour NOx values, averages of several past hours, and differences and rates of change between selected past hours. A multi-linear regression (MLR) and three machine-learning (ML) models are trained and cross-validated for various yearly intervals within the 2017 to 2021 period. The trained models are then applied to predict up to four hours ahead for 2020 and 2021 as separate testing subsets. The models substantially outperform autoregressive and moving average (MA) methods in their hours-ahead forecasts. Feature importance analysis extracted from the MLR and ML models reveals the flexibility with which the models can give more weight to certain trend attributes depending upon the t + x hour being predicted.

The escalating quantity of wastewater from multiple sources has raised concerns about both water reuse and environmental preservation. Therefore, there is a pressing need for intelligent tools that can aid in comprehending the intricate process of removing dyes and salts from wastewater beyond membrane technology. This study introduces novel standalone hybrid models that integrate an improved nonlinear ensemble approach to model the fractionation of dye and salt rejection (RJDS) (%) based on established experimental data. Using linear sensitivity analysis, two model combinations were identified based on different input variables: M1 ( R = 52%, T = 50%, and P = 61%) and M2 ( R = 52%, T = 50%, P = 61%, F = 71%, and RJ = 83%). These combinations were incorporated into hybrid neuro-fuzzy (NF) and least square support vector machine (LSSVM) models. The standalone and improved ensemble models were evaluated using several performance criteria, such as MSE, MAE, MAPE, RMSE, and PBAIS. The predictive outcomes demonstrated that NF-M2 outperformed all other models, with an MAE of 0.0002 and an RMSE of 0.0003. Similarly, the ensemble results indicated a significant improvement over the individual models. The study's findings demonstrate the reliability of intelligent tools for modelling RJDS (%) and serving as decision-making performance analysis tools. The proposed approach offers a novel, efficient and reliable technique for understanding and predicting dye and salt rejection in wastewater.

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The United Nations (UN) expectations for 2030 account for a renewable, affordable, and eco-friendly energy future. The 2030 agenda includes 17 different Sustainable Development Goals (SDGs) for countries worldwide. In this work, the 7th SDG: Affordable and Clean Energy, is brought into focus. For this goal, five main challenges are discussed: (i) limiting the use of fossil fuels; (ii) migrating towards diversified and renewable energy matrices; (iii) decentralizing energy generation and distribution; (iv) maximizing energy and energy storage efficiency; and (v) minimizing energy generation costs of chemical processes. These challenges are thoroughly scrutinized and surveyed in the context of recent developments and technologies including energy planning and supervision tools employed in the Global South. The discussion of these challenges in this work shows that the realization of SDG 7, whether partially or in full, within the Global South and global contexts, is possible only if existing technologies are fully implemented with the necessary international and national policies. Among the key solutions identified in addressing the five main challenges of SDG 7 are a global climate agreement; increased use of non-fossil fuel energy sources; Global North assistance and investment; reformed global energy policies; smart grid technologies and real time optimization and automation technologies.

Microalgae are a source of scientific curiosity and inspiration for their utilization as ‘inoculants’ in agriculture and the commercial production of high-value products. Their diversity and abundance in the soil environment highlight the fact that these integral members of the soil microbial community modify the physical and chemical conditions of soils and interact with other microorganisms and even with higher plants with varying degrees of association. However, to date, the agronomic benefits of the nitrogen fixation trait of cyanobacteria have not been fully realized. Thus, the ecological functions of these organisms in the biological soil crusts should be thoroughly evaluated and widely applied given that climate change events can increase desertification. Currently, the crop yield increments and pest control due to these biostimulants and the reclamation of saline and sodic soils by these bioameliorants are considered economically marginal. Similarly, the carbon capture and storage by eukaryotic microalgae and cyanobacteria in soils are poorly understood. Limitations in their commercial production for agricultural use include inadequate technological innovations and the enormous expectation for yield increments, together with the contemporary monetization of their environmental benefits. Thus, this critical review presents the desirable reappraisal of their agronomic benefits and the invigoration of research and culture collections to utilize these organisms or their metabolites, considering the evolutionary consequences and environmental advantages and finally their commercial production for widespread application in agriculture.

In this study, we measured the levels of occupational radon using short-term (electret passive environmental radon monitor system [E-PERM?]), long-term (Radtrak2? alpha track), and real-time (RAD7) monitoring detectors and characterized radon exposure levels in workplaces directly handling radon raw materials and byproducts and underground public-use facilities likely to be exposed to radon in the form of a naturally occurring radioactive material (NORM). The geometric means (GMs) of occupational radon exposures measured at 10 manufacturing workplaces and 11 underground public-use facilities were 86.4 Bq m ?3 ( n = 299) overall, 60.7 Bq m ?3 ( n = 91) for short-term measurements, 132.4 Bq m ?3 ( n = 176) for long-term measurements, and 30.0 Bq m ?3 ( n = 32) for real-time measurements. More importantly, the GM of radon levels measured at the underground facilities [118.9 Bq m ?3 ( n = 127)] was significantly higher than that found at the workplaces [68.3 Bq m ?3 ( n = 172)] ( p 0.001). We found that workers at underground public-use facilities could be unintentionally exposed to higher radon levels resulting from NORMs. Therefore, we suggest that the Korean Occupational Safety and Health Act strengthens the regulations related to occupational exposure management for radiation and radon and establishes a more comprehensive control system to regularly monitor, manage, and reduce the levels of occupational radon exposure, particularly NORMs. In doing so, we can protect workers’ health and safety from potential radon exposure at various workplaces and underground public-use facilities. Further studies should be conducted to quantitatively evaluate occupational radon exposures for a larger number of other underground facilities and workplaces, build radon-specific job or task-based exposure matrices, and follow-up health effects for workers who could possibly be exposed to radon or NORMs.

When modeling many physicochemical, biochemical, and ecological processes, numerical data on Henry's law constants are much desired. In addition, these data are used in pharmaceuticals for the development of gaseous drugs, as well as in modeling drug–receptor interactions. Henry's law constant is an indicator of the affinity of compounds for the vapor phase and water. The local symmetry of simplified molecular input-line entry systems (SMILES) comprises compositions of identical symbols that can be represented as three ‘xyx’, four ‘xyyx’, or five symbols ‘xyzyx’. Taking account of these attributes of SMILES can improve the predictive potential of models for Henry's law constants. We updated our CORAL software using the optimal (flexible) descriptor. The updated descriptor improved the predictive potential when applied to the model for Henry's law constants. This new approach also permits fast definition of a set of pollutants that have a minimal impact on climate change and are safe from an environmental point of view.