Environmentally-friendly technology for rapid identification and quantification of emerging pollutants from wastewater using infrared spectroscopy

Cookie composition analysis by Fourier transform near infrared spectroscopy coupled to chemometric analysis

The consumption ofcookies is ever growing and during the COVID-19 pandemic reached record consumption values and it is imperative to guarantee the quality and safety of the products.Fourier transform near infrared (FT-NIR) spectroscopy, combined with chemometric techniques, provides a promising solution in that regard, due to its speed and simple sample preparation. The objective of this study was to investigate the possibilities of using FT-NIR to predict lipids, carbohydrates, fibers, proteins, salt and energy contents, as well as to identify cookies type and main cereals present in a batch of 120 commercially acquired samples. The prediction models were performed using ordinary least squares (OLS), partial least squares (PLS), and PLS based classification models including discriminant analysis (PLS-DA), k-nearest neighbors (PLS-kNN) and naïve Bayes (PLS-NB). The best prediction models allowed for good accuracies, with correlation coefficients higher than 0.9 for all studied nutritional parameters. PLS-kNN methodology was able to identify all 5 main cereals (wheat, integral wheat, oat, corn and rice) as well as the 14 types of cookies based on the nutritional contents. The developed methods were able to accurately identify the cookies type and composition, confirming the proposed methodology as a fast, reliable, environmentally friendly and non-destructive alternative to standard analytical methods.

Advances in identifying and managing emerging contaminants in aquatic ecosystems: Analytical approaches, toxicity assessment, transformation pathways, environmental fate, and remediation strategies

Emerging contaminants (ECs) are increasingly recognized as threats to human health and ecosystems. This review evaluates advanced analytical methods, particularly mass spectrometry, for detecting ECs and understanding their toxicity, transformation pathways, and environmental distribution. Our findings underscore the reliability of current techniques and the potential of upcoming methods. The adverse effects of ECs on aquatic life necessitate both in vitro and in vivo toxicity assessments. Evaluating the distribution and degradation of ECs reveals that they undergo physical, chemical, and biological transformations. Remediation strategies such as advanced oxidation, adsorption, and membrane bioreactors effectively treat EC-contaminated waters, with combinations of these techniques showing the highest efficacy. To minimize the impact of ECs, a proactive approach involving monitoring, regulations, and public education is vital. Future research should prioritize the refining of detection methods and formulation of robust policies for EC management.

Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration

Wastewater treatment based on the activated sludge process is complex process, which is easily affected by influent quality, aeration time and other factors, leading to unstable effluent. Facing increasingly stringent sewage discharge standards in China, it is necessary to build a prediction model for early warning of effluent quality. In this study, nine machine learning algorithms were adopted to construct models for the prediction of effluent Chemical Oxygen Demand (COD). In order to improve the prediction accuracy of the models, model optimization was conducted by introducing the hysteresis condition [Hydraulic Retention Time (HRT) of 18 h], data processing method (K-FOLD) and process parameters [dissolved oxygen (DO), sludge return ratio (SRR) and mixed liquid suspended solids (MLSS)]. Results showed that both K-Nearest Neighbour (KNN) and Gradient Boosting Decision Tree (GBDT) displayed excellent prediction effects, the best results of MAPE, RMSE and R² were 7.34%/1.29/0.92(COD, KNN). The optimized models were further applied to the prediction of effluent total phosphorus (TP), total nitrogen (TN) and pH. The MAPE/RMSE/R² were 7.43%/0.92/0.93(TN, GBDT), 17.81%/0.19/0.99(TP, KNN), 0.53%/0.16/0.99 (pH, KNN) respectively, indicating high prediction accuracy. The change and comparison of modeling conditions provide a new insight to wastewater prediction models. In addition, this study is close to the actual application scenario of WWTPs operation and management, also laying a foundation for the reverse regulation of energy saving and consumption reduction of wastewater treatment plants (WWTPs).

Simultaneous opto-electrochemical monitoring of carbamazepine and its electro-oxidation by-products in wastewater

The growing human impact on aquatic environments deriving from the extensive use of pharmaceuticals and the release of persistent pollutants necessitates the implementation of new, widespread methods for characterising and quantifying such contaminants and their related degradation products. Carbamazepine, 5 H-dibenzo[b,f]azepine-5-carboxamide, (CBZ) is a widely used anti-epileptic drug characterised by limited removal by conventional wastewater treatments and high persistency in the environment. In this work, CBZ detection and quantification was performed in phosphate buffer, as well as in samples of complex matrix-like landfill leachates and treated wastewater originating from a medical facility, and simultaneously by optical and electrochemical methods using a novel transparent carbon-based nanostructured electrode. Coupling electrochemical (differential pulse voltammetry) with optical (UV-visible spectroscopy) methods, it has been possible to reach the limit of detection (LOD) for CBZ at the levels of 4.7 μM for the electrochemical method, 10.3 μM for the spectroscopic method, and 3.6 μM for the opto-electrochemical method. Raman spectroscopy and ultra-high performance liquid chromatography coupled with tandem mass spectrometry techniques were employed to support and validate the combined technique. The novel developed technique showed high selectivity to carbamazepine and its by-products, even in environmental samples. Thus, this environmentally friendly, fast and accurate detection method is believed to be successfully implementable in investigating other pharmaceutical and chemical contaminates of concern.

Fast microwave-assisted synthesis of magnetic molecularly imprinted polymer for sulfamethoxazole

A fast and simple strategy based on the microwave technique for the preparation of magnetic molecularly imprinted polymers (MMIPs) is proposed for the selective determination of sulfamethoxazole (SMX). The MMIPs were synthesized at 70 °C in 20 min, being much faster than the conventional techniques. A computational approach based on density functional theory was used to design the MMIP and compare the two most used monomers in MIPs, including methacrylic acid (MAA) and acrylamide (AM). Then, two different MMIPs were prepared using AM and MAA as monomers. The resultant materials were characterized with X-ray diffraction, thermogravimetric analysis, scanning/transmission electron microscopy, and Fourier-transform infrared spectroscopy. Besides, the adsorption characterizations suggested that the adsorption of SMX followed the pseudo-second-order model in the kinetic study and the Sips model in the isotherm study. The experimental results corroborated the computational approach. Furthermore, Both MMIPs demonstrated good selectivity. The MMIP-AM and MMIP-MAA were applied as adsorbents in magnetic dispersive solid-phase extraction combined with UV-visible spectroscopy to quantify SMX. The obtained limits of detection and quantification were lower than 0.59 and 1.77 μM, respectively for both MMIPs. The sensitivity of both MMIPs was in the range of 0.021-0.023 (SI). Our findings revealed that there is no significant difference in the analytical parameters between MMIP-AM and MMIP-MAA. However, the application of both MMIPs in a real sample (tap water) showed that the recovery values of SMX obtained with MMIP-AM (68-70%) were lower than that with MMIP-MAA (80-90%) suggesting that MMIP-MAA is more appropriate for SMX determination.