Types Of Emerging Contaminants Research Articles

Degradation of emerging contaminants in synthetic hydrolyzed urine by UV/peracetic acid: Free radical chemistry, and toxicity analysis

The ecological impact of emerging contaminants (ECs) in aquatic environments has raised concerns, particularly with regards to urine as a significant source of such contaminants in wastewater. The current investigation used the UV/Peracetic Acid (UV/PAA) processes, an innovative advanced oxidation technology, to effectively separate two emerging pollutants from urine at its source, namely, ciprofloxacin (CIP) and bisphenol A(BPA). The research findings demonstrate that the presence of the majority of characteristic ions has minimal impact on the degradation of ECs. However, in synthetic hydrolyzed urine, only NH4+ inhibits the degradation of two types of ECs, with a more pronounced effect observed on CIP degradation compared to BPA.The impact of halogen ions, specifically Cl− and I−, on the degradation of CIP in synthetic hydrolyzed urine was a complex phenomenon. When these two halogen ions are present individually, the generation of reactive halogen species (RHS) within the system enhances the degradation of CIP. However, when both types of ions coexist, the formation of diatomic radical species partially inhibits degradation. In terms of BPA degradation, while the production of reactive chlorine species (RCS) to some extent hinders the reaction rate, the generation of reactive iodine species (RIS) promotes the overall process. CIP undergoes fragmentation of the piperazine and quinoline rings, decarboxylation, defluorination reactions, as well as substitution reactions, leading to the formation of products with simplified structures. The degradation of BPA occurs gradually through hydroxyl and halogen substitution as well as isopropyl cleavage. The preliminary toxicity analysis confirmed that the presence of halogen ions in urine resulted in the formation of halogenated products in two types of ECs, albeit with an overall reduction in toxicity. The UV/PAA processes was considered to be an effective and relatively safe approach for the separation of ECs in urine.

The Grey–Taguchi Method, a Statistical Tool to Optimize the Photo-Fenton Process: A Review

Currently there is a growing concern about the presence of emerging contaminants (EC) in water bodies and their potential ecotoxicological effects. Pharmaceuticals, a type of EC, are widely distributed in the environment and their main entry is through wastewater from treatment plants, since these systems are not designed to remove EC. In this sense, the photo-Fenton process, an advanced oxidation process, has proven to be highly efficient in degrading new potentially harmful contaminants. However, this process is affected by multiple factors, which makes it necessary to implement a methodological strategy to optimize the photo-Fenton process. Within these, the Taguchi method provides robust and low-cost solutions with the least number of experiments, providing knowledge of the contribution of each of the factors studied to the response variable. In addition to the above, the Taguchi method can be coupled to a Grey relational analysis (Grey–Taguchi method), which will allow the optimization of more than one response variable at the same time. This paper discusses the parameters that affect the photo-Fenton process and the application of designs of experiments to optimize the process.

Alternative methods of monitoring emerging contaminants in water: a review.

Anthropogenic activities have steadily increased the release of emerging contaminants (ECs) in aquatic bodies, and these ECs may have adverse effects on humans even at their trace (μg L-1) levels. Their occurrence in wastewater systems is more common, and the current wastewater treatment facilities are inefficient in eliminating many of such persistent ECs. "Gold standard" techniques such as chromatography, mass spectrometry, and other high-resolution mass spectrometers are used for the quantification of ECs of various kinds, but they all have significant limitations. This paper reviews the alternative methods for EC detection, which include voltammetry, potentiometry, amperometry, electrochemical impedance spectroscopy (EIS) based electrochemical methods, colorimetry, surface-enhanced Raman spectroscopy (SERS), fluorescence probes, and fluorescence spectroscopy-based optical techniques. These alternative techniques have several advantages over conventional techniques, including low sample volume, excludes solid phase extraction procedure, high sensitivity, selectivity, portability, reproducibility, rapidity, low cost, and the ability to monitor ECs in real time. This review summarises each of the alternative methods for detecting ECs in water samples and their respective limits of detection (LODs). The sensitivity of each technique varied depending on the type of EC measured, type of electrochemical probe and electrode, substrates, type of nanoparticle (NP), the physicochemical parameters of water samples tested, and more. Nevertheless, this paper also focuses on some of the current challenges encountered by these alternative methods in monitoring ECs.

Artificial Intelligence Meets Marine Ecotoxicology: Applying Deep Learning to Bio-Optical Data from Marine Diatoms Exposed to Legacy and Emerging Contaminants.

Simple SummaryOur work is motivated by the increasing production of chemicals with environmentally harmful effects to our aquatic ecosystems. We show that it is possible to detect and distinguish the presence of several different emerging contaminants, using the photochemical responses of a microalgae species, which is among the most abundant phytoplankton group in the oceans. We use several machine learning and deep learning models that operate on chlorophyll fluorescence induction curves, which are composed of fluorescence values taken at different time steps from the microalgae exposure trials, achieving up to 97.65% accuracy when predicting the type of contaminant, and up to 100% in several cases when predicting the exposure concentration. Our results show the combination of these models with the fluorescence induction curves creates a powerful tool for ecotoxicity assessment, capable of classifying model organisms for their contaminant exposure, both in terms of type and concentration, opening new doors for toxicophenomics developments.Over recent decades, the world has experienced the adverse consequences of uncontrolled development of multiple human activities. In recent years, the total production of chemicals has been composed of environmentally harmful compounds, the majority of which have significant environmental impacts. These emerging contaminants (ECs) include a wide range of man-made chemicals (such as pesticides, cosmetics, personal and household care products, pharmaceuticals), which are of worldwide use. Among these, several ECs raised concerns regarding their ecotoxicological effects and how to assess them efficiently. This is of particular interest if marine diatoms are considered as potential target species, due to their widespread distribution, being the most abundant phytoplankton group in the oceans, and also being responsible for key ecological roles. Bio-optical ecotoxicity methods appear as reliable, fast, and high-throughput screening (HTS) techniques, providing large datasets with biological relevance on the mode of action of these ECs in phototrophic organisms, such as diatoms. However, from the large datasets produced, only a small amount of data are normally extracted for physiological evaluation, leaving out a large amount of information on the ECs exposure. In the present paper, we use all the available information and evaluate the application of several machine learning and deep learning algorithms to predict the exposure of model organisms to different ECs under different doses, using a model marine diatom (Phaeodactylum tricornutum) as a test organism. The results show that 2D convolutional neural networks are the best method to predict the type of EC to which the cultures were exposed, achieving a median accuracy of 97.65%, while Rocket is the best at predicting which concentration the cultures were subjected to, achieving a median accuracy of 100%.

Progress in the biological and chemical treatment technologies for emerging contaminant removal from wastewater: A critical review

This review focuses on the removal of emerging contaminants (ECs) by biological, chemical and hybrid technologies in effluents from wastewater treatment plants (WWTPs). Results showed that endocrine disruption chemicals (EDCs) were better removed by membrane bioreactor (MBR), activated sludge and aeration processes among different biological processes. Surfactants, EDCs and personal care products (PCPs) can be well removed by activated sludge process. Pesticides and pharmaceuticals showed good removal efficiencies by biological activated carbon. Microalgae treatment processes can remove almost all types of ECs to some extent. Other biological processes were found less effective in ECs removal from wastewater. Chemical oxidation processes such as ozonation/H2O2, UV photolysis/H2O2 and photo-Fenton processes can successfully remove up to 100% of pesticides, beta blockers and pharmaceuticals, while EDCs can be better removed by ozonation and UV photocatalysis. Fenton process was found less effective in the removal of any types of ECs. A hybrid system based on ozonation followed by biological activated carbon was found highly efficient in the removal of pesticides, beta blockers and pharmaceuticals. A hybrid ozonation-ultrasound system can remove up to 100% of many pharmaceuticals. Future research directions to enhance the removal of ECs have been elaborated.

Emerging contaminants in the environment: Risk-based analysis for better management

Emerging contaminants (ECs) are chemicals of a synthetic origin or deriving from a natural source that has recently been discovered and for which environmental or public health risks are yet to be established. This is due to limited available information on their interaction and toxicological impacts on receptors. Several types of ECs exist such as antibiotics, pesticides, pharmaceuticals, personal care products, effluents, certain naturally occurring contaminants and more recently nanomaterials. ECs may derive from a known source, for example released directly to the aquatic environment from direct discharges such as those from wastewater treatment plants. Although in most instances the direct source cannot be identified, ECs have been detected in virtually every country's natural environment and as a consequence they represent a global problem. There is very limited information on the fate and transport of ECs in the environment and their toxicological impact. This lack of information can be attributed to limited financial resources and the lack of analytical techniques for detecting their effects on ecosystems and human health on their own or as mixture. We do not know how ECs interact with each other or various contaminants. This paper presents an overview of existing knowledge on ECs, their fate and transport and a risk-based analysis for ECs management and complementary strategies.