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Detecting environmental contaminants is crucial for protecting ecosystems and human health. While traditional carbon dot (CD) fluorescent probes are versatile, they may suffer from limitations like fluctuations in signal intensity, leading to detection inaccuracies. In contrast, ratiometric fluorescent probes, designed with internal self-calibration mechanisms, offer enhanced sensitivity and reliability. This review focuses on the design and applications of ratiometric fluorescent probes based on CDs for environmental monitoring. Our discussion covers construction strategies, ratiometric fluorescence principles, and applications in detecting various environmental contaminants, including organic pollutants, heavy metal ions, and other substances. We also explore associated advantages and challenges and provide insights into potential solutions and future research directions.

One way to mitigate the risk of consumption of contaminated water in water distribution networks is optimal placement of the quality sensors. A considerable challenge in this respect is the significance of contamination at a junction. Beside the population affected and the volume of the contaminated water consumed, importance of each junction is a parameter that must be taken into account in placing the sensors. This parameter directly concerns the service provided by each junction as well as the sensitivity and social consequences of junction contamination. The present study defines a new objective function for minimizing the effect of junction contamination with respect to its importance. Using a robust approach, this study applied the NSGA-III algorithm to solve a 5-objective problem. The algorithm was tested on a hypothetical network and a benchmark network and the Pareto response was selected for each scenario based on the slope of the different points. The proposed method suggested 12, 12, and 11 sensors for the three scenarios in the hypothetical network. The results show that sensor placement by this method yielded good performance in comparison with the other solutions presented in a benchmark network. HIGHLIGHTS A NSGA-III algorithm for five-objective sensor placement based on the robust optimization models is proposed.; By employing two typical water distribution networks, the benefits of the proposed algorithm are illustrated and some critical factors are also addressed.; A new objective function describes the impact of contamination of important nodes.; Uncertainty at the point of entry and the entry time of the contamination are considered.;

The spectroscopic and imaging performance of energy-resolved photon counting detectors, based on new sub-millimetre boron oxide encapsulated vertical Bridgman cadmium zinc telluride linear arrays, are presented in this work. The activities are in the framework of the AVATAR X project, planning the development of X-ray scanners for contaminant detection in food industry. The detectors, characterized by high spatial (250 µm) and energy (

Extensive and high residue variations in enrofloxacin (ENR) exist in different aquatic products. A novel quantitative method for measuring ENR using high-performance liquid chromatography–tandem mass spectrometry was developed employing enrofloxacin-d5 (ENR-d5) and enrofloxacin-d3 (ENR-d3) as isotope surrogates. This reduced the deviation of detected values, which results from the overpass of the linear range and/or the large difference in the residue between the isotope standard and ENR, from the actual content. Furthermore, high residue levels of ENR can be directly diluted and re-calibrated by the corresponding curve with the addition of high levels of another internal surrogate without repeated sample preparation, avoiding the overflow of the instrument response. The validation results demonstrated that the method can simultaneously determine ENR residues from MQL (2 µg/kg) to 5000 × MQL (method quantification limit) with recoveries between 97.1 and 106%, and intra-precision of no more than 2.14%. This method realized a wide linear calibration range with dual deuterated isomers, which has not been previously reported in the literature. The developed method was successfully applied to the analysis of ENR in different aquatic products, with ENR residue levels varying from 108 to 4340 μg/kg and an interval of precision in the range of 0.175~6.72%. These results demonstrate that batch samples with a high variation in ENR residues (over the linear range with a single isotope standard) can be detected by the dual isotope surrogates method in a single sample preparation process.

In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.

In recent years, as number of new building getting larger, there has been an increased interest in the cleaning of exterior walls. Accordingly, there is a growing interest in automatic cleaning robots that move around the outer building façade. These robots are also required to apply different cleaning methods to remove various contaminants on the outer wall of the building. However, current surface contaminant detection systems can either detect only a single type of contaminant, or are not compact enough for installation on mobile platforms that move around the outer façade. As cleaning workers are able to distinguish various contaminants with the naked eye, we aim to solve this problem by developing a machine-vision system using convolutional neural networks (CNNs) and image processing methods. As it is a compact system that uses only a camera to take pictures and a processor to process the images, it is suitable for applications involving mobile platforms. Object-type contaminants such as avian feces are handled by the YOLOv3 module using the object-detection algorithm. Area-type contaminants such as rusty stains are processed using the color-detection module using the HSV color space, median filter, and flood fill algorithm. Particle-type contaminants such as dust are handled by the grayscale module, converting images to grayscale images and then comparing the average brightness with a reference that is provided in advance. This proposed machine vision system will detect objects, areas, and particle-type contaminants with a single image and some reference images provided in advance.

The in vitro propagation of Colocasia esculenta is limited by the presence of microbial contaminants, especially bacteria. The objective of this work was to demonstrate that the inclusion of actions for the bacterial contamination management in the in vitro propagation of C. esculenta cv. ‘INIVIT MC-2012’, from field explants, can reduce losses. Two in vitro propagation protocols were used and the incidence of bacterial contaminants in the establishment and multiplication stages was compared. One protocol modified the other with actions for the management of bacterial contamination. In each one, 20 primary rhizomes were taken from which at least two axillary buds were extracted and from each one a line was established. The number of explants contaminated with bacteria per subculture was quantified. Bacterial contamination was observed in the culture medium below or around the explants, indicating the initial explant as the primary source of contamination. With the application of the modified protocol, the percentage of losses due to bacterial contamination in all subcultures evaluated was reduced. In the in vitro establishment it did not exceed 5%. The combination of actions for the management of bacterial contamination among which are the reduction of the size of the explant, the work by lines and the visual detection of contaminants prior to the subculture of the plant material reduces the presence of bacterial contaminants in the in vitro propagation of C. esculenta cv. ‘INIVIT MC-2012’.

The present study investigates the suitability of a machine vision-based method to detect deviations in the Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) biological system fed with different selenium:mercury (Se:Hg) molar ratios. Four groups of fish were fed during 14 days with commercial feed (control) and with the same feed spiked with 0.5, 5 and 10 mg of MeHg per kg, giving Se:Hg molar ratios of 29.5 (control-C1); 6.6, 0.8 and 0.4 (C2, C3 and C4). The basal SE of C1 and C2 (Se:Hg > 1) tended to increase during the experimental period, while that of C3 and C4 (Se:Hg < 1) tended to decrease. In addition, the differences in the SE of the four systems in response to a stochastic event minus that of the respective basal states were less pronounced in the systems fed with Se:Hg molar ratios lower than one (C3 and C4). These results indicate that the SE may be a suitable indicator for the prediction of seafood safety and fish health (i.e., the Se:Hg molar ratio and not the Hg concentration alone) prior to the displaying of pathological symptoms. We hope that this work can serve as a first step for further investigations to confirm and validate the present results prior to their potential implementation in practical settings.

Methylmercury (MeHg) is an environmental contaminant of increasing relevance as a seafood safety hazard that affects the health and welfare of fish. Non-invasive, on-line methodologies to monitor and evaluate the behavior of a fish system in aquaculture may make the identification of altered systems feasible—for example, due to the presence of agents that compromise their welfare and wholesomeness—and find a place in the implementation of Hazard Analysis and Critical Control Points and Fish Welfare Assurance Systems. The Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) system has been shown to differentiate MeHg-treated from non-treated fish, the former displaying a lower SE value than the latter. However, little is known about the initial evolution of the system after removal of the toxicant. To help to cover this gap, the present work aims at providing information about the evolution of the SE of a European seabass system during a recuperation period of 11 days following a two-week treatment with 4 µg·MeHg/L. The results indicate that the SE of the system did not show a recovery trend during the examined period, displaying erratic responses with daily fluctuations and lacking a tendency to reach the initial SE values.