Bacteria and their secreted extracellular polymeric substances (EPS) are widely distributed in ecosystems and have high capacity for heavy metal immobilization. The knowledge about the molecular-level interactions with heavy metal ions is essential for predicting the behavior of heavy metals in natural and engineering systems. This comprehensive study using potentiometric titration, Fourier-transform infrared (FTIR) spectroscopy, isothermal titration calorimetry (ITC) and X-ray absorption fine structure (XAFS) was able to reveal the functional diversity and adsorption mechanisms for Pb onto bacteira and the EPS in greater detail than ever before. We identified mono-carboxylic, multi-carboxylic, phosphodiester, phosphonic and sulfhydryl sites and found the partitioning of Pb to these functional groups varied between gram-negative and gram-positive bacterial strains, the soluble and cell-bound EPS and Pb concentrations. The sulfhydryl and phosphodiester groups preferentially complexed with Pb in P. putida cells, while multifunctional carboxylic groups promoted Pb adsorption in B. subtilis cells and the protein fractions in EPS. Though the functional site diversity, the adsorption of Pb to organic ligands occurred spontaneously through a universal entropy increase and inner-sphere complexation mechanism. The functional group scale knowledge have implications for the modeling of heavy metal behavior in the environment and application of these biological resources.

Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO₂ and SO₂. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R² and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R² and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R² can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored.

Herein, activated carbon supported modified with bimetallic-platin ruthenium nano sorbent (PtRu@AC) was synthesized by a thermal decomposition process and used in the removal of methylene blue (MB) from aqueous solutions. The synthesized nano sorbents were characterized by X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), and X-ray Photoelectron Spectroscopy (XPS) spectroscopic techniques. The data obtained from characterization studies showed that PtRu@AC nano sorbent was highly crystalline and in a form of PtRu alloy with a monodispersed composition. The results indicated that the maximum adsorption capacity (qemax) for the removal of MB with PtRu@AC under optimum conditions was detected to be 1.788 mmol/g (569.4 mg/g). The experimental kinetic results of the study revealed that the adsorption of methylene blue was found to be more compatible with the false second-order model compared to some tested models. Calculations for thermodynamic functions including enthalpy change (ΔHo), entropy change (ΔSo), and Gibbs free energy change (ΔGo) values were performed to get an idea about the adsorption mechanism. As a result, the synthesized PtRu@AC nano adsorbent was detected as a highly effective adsorbent material in the removal of MB from aquatic mediums.

The massive generation of medical waste (MW) results in a series of environmental, social, and ecological problems. Pyrolysis is one such approach that has attracted more attention because of the production of value-added products with lesser environmental risk. In this study, the activated biochar (ABC600) was obtained from MW pyrolysis and activated with KOH. The adsorption mechanism of activated biochar on cationic (methylene blue) and anionic (reactive yellow) dyes were studied. The physicochemical characterization of biochar showed that increasing pyrolysis temperature and KOH activation resulted in increased surface area, a rough surface with a clear porous structure, and sufficient functional groups. MB and RYD-145 adsorption on ABC600 was more consistent with Langmuir isotherm (R² ≥ 0.996) and pseudo-second-order kinetics (R² ≥ 0.998), indicating chemisorption with monolayer characteristics. The Langmuir model fitting demonstrated that MB and RYD-145 had maximum uptake capacities of 922.2 and 343.4 mg⋅g⁻¹. The thermodynamics study of both dyes showed a positive change in enthalpy (ΔH°) and entropy (ΔS°), revealing the endothermic adsorption behavior and randomness in dye molecule arrangement on activated-biochar/solution surface. The activated biochar has excellent adsorption potential for cationic and anionic dyes; hence, it can be considered an economical and efficient adsorbent.

The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challenging. With a focus on the on-site optical imaging of HNS, this study explores the potential of single spectral imaging for HNS identification using the Faster R-CNN architecture. Images at 365 nm (narrow UV band), blue channel images (visible broadband of ∼400–600 nm), and RGB images of typical HNS (benzene, xylene, and palm oil) in different scenarios were studied with and without Faster R-CNN. Faster R-CNN was applied to locate and classify the HNS spills. The segmentation using Faster R-CNN-based methods and the original masking methods, including Otsu, Max entropy, and the local fuzzy thresholding method (LFTM), were investigated to explore the optimal wavelength and corresponding image processing method for the optical imaging of HNS. We also compared the classification and segmentation results of this study with our previously published studies on multispectral and whole spectral images. The results demonstrated that single spectral UV imaging at 365 nm combined with Faster R-CNN has great potential for the automatic identification of transparent HNS floating on the surface of the water. RGB images and images using Faster R-CNN in the blue channel are capable of HNS segmentation.

Fluoroquinolone antibiotics (FQs) are considered to be emerging environmental contaminants that have been detected extensively in aquatic environment. It is of quite importance to explore FQs interacting with dissolved organic matter (DOM). The interactions of FQs with DOM were examined by nuclear magnetic resonance (NMR) spectroscopy, fluorescence quenching, UV–vis, Fourier transform infrared (FT-IR) spectroscopic techniques. The bindings of FQs to DOM had one single binding site and their quenching mechanisms were static, which were evaluated by the Stern-Volmer and Site-binding equations. Addition of DOM could result in micro-environmental changes of fluorophores groups in FQs. The location adjacent oxygen right of Ofloxacin (OFL) and the aromatic ring (the adjacency replaced by two nitrogen-containing groups) of Ciprofloxacin (CIP), Enrofloxacin (ENR), Norfloxacin (NOR) might be highly affected by DOM molecule. The negative enthalpy change (ΔH⁰), negative entropy change (ΔS⁰) and the positive Gibbs' energy change (ΔG⁰) figured out that the binding processes were exothermic but not thermodynamic favorable, the formation of HA-FQs complexes would be powered chiefly by the ΔS⁰. H-bonding, electrostatic effect, van der Waals force were the acting force in the binding reactions and the π-π stacking effect was the major binding force under alkaline conditions. Moreover, the protonated, deprotonated, or partially protonated state of FQs were found to have different binding capacity to DOM, and the binding reactions for FQs-HA system were suppressed as the ionic strength increased. Meanwhile, alterations of FQs conformation in the presence of DOM were evaluated by FT-IR and UV–vis spectra.

Metal-resistant bacteria can survive exposure to high metal concentrations without any negative impact on their growth. Biosorption is considered to be one of the more effective detoxification mechanisms acting in most bacteria. However, molecular-scale characterization of metal biosorption by wild metal-resistant bacteria has been limited. In this study, the Pb(II) biosorption behavior of Serratia Se1998 isolated from Pb-contaminated soil was investigated through macroscopic and microscopic techniques. A four discrete site non-electrostatic model fit the potentiometric titration data best, suggesting a distribution of phosphodiester, carboxyl, phosphoryl, and amino or hydroxyl groups on the cell surface. The presence of these functional groups was verified by the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, which also indicated that carboxyl and phosphoryl sites participated in Pb(II) binding simultaneously. The negative enthalpy (−9.11 kJ mol−1) and large positive entropy (81.52 J mol−1 K−1) of Pb(II) binding with the bacteria suggested the formation of inner-sphere complexes by an exothermic process. X-ray absorption fine structure (XAFS) analysis further indicated monodentate inner-sphere binding of Pb(II) through formation of C−O−Pb and P−O−Pb bonds. We inferred that C−O−Pb bonds formed on the flagellar surfaces, establishing a self-protective barrier against exterior metal stressors. This study has important implications for an improved understanding of metal-resistance mechanisms in wild bacteria and provides guidance for the construction of genetically engineered bacteria for remediation purposes.

The characteristics and mechanisms of competitive adsorption of trace metals on bacteria-associated clay mineral composites have never been studied, despite their being among the most common organic–mineral complexes in geological systems. Herein, competitive adsorption of Pb and Cd on Pseudomonas putida–montmorillonite composite was investigated through adsorption–desorption experiment, isothermal titration calorimetry (ITC), and synchrotron micro X-ray fluorescence (μ-XRF). From the experiment, stronger competition was observed on clay mineral than on bacteria–clay composite because more non-specific sites accounted for heavy metal adsorption on clay mineral surface at the studied pH 5. Both competing heavy metals tended to react with bacterial fractions in the composite, which was verified by the higher correlation of Cd (and Pb) with Zn (R2 = 0.41) elemental distribution than with Si (R2 = 0.10). ITC results showed that competitive adsorption exhibited a lower entropy change (ΔS) at the metal-sorbent interfaces compared with single-metal adsorption, revealing that Cd and Pb are bound to the same types of adsorption sites on the sorbent. The competitive effect on bacteria–clay composite was found to be helpful for a better understanding on the fixation, remobilization and subsequent migration of heavy metals in multi-metal contaminated environments.

Ecotoxicological bioassays, e.g. based on Danio rerio teratogenicity (DarT) or the acute luminescence inhibition with Vibrio fischeri, could potentially lead to significant benefits for detecting on site contaminations on qualitative or semi-quantitative bases. The aim was to use the observed effects of two ecotoxicological assays for estimating the extent of a Benzene groundwater contamination plume. We used a Maximum Entropy (MaxEnt) method to rebuild a bivariate probability table that links the observed toxicity from the bioassays with Benzene concentrations. Compared with direct mapping of the contamination plume as obtained from groundwater samples, the MaxEnt concentration map exhibits on average slightly higher concentrations though the global pattern is close to it. This suggest MaxEnt is a valuable method to build a relationship between quantitative data, e.g. contaminant concentrations, and more qualitative or indirect measurements, in a spatial mapping framework, which is especially useful when clear quantitative relation is not at hand.

Evaluating the effects of anthropogenic pressure on the marine environment is one of the focal objectives in identifying strategies for its use, conservation and restoration. In this paper, we assessed the effects of chemical pollutants, grain size and plastic litter on functional traits, biodiversity and biotic indices. The study was conducted on the benthic communities of three harbours in the central Mediterranean Sea: Malta, Augusta and Syracuse, subjected to different levels of anthropogenic stress (high, medium and low, respectively). Six traits were considered, subdivided into 22 categories: reproductive frequency, environmental position, mobility, life habit, feeding habit and bioturbation. Functional diversity indices analysed were: Functional Divergence, Quadratic Entropy, Functional Evenness and Functional Richness. To assess the trait responses to environmental gradients, we applied RLQ analysis, which considers simultaneously the relationship between three components: environmental data (R), species abundances (L) and species traits (Q). From our analyses, significant relationships (P-value = 0.0018 for permutation of samples, and P-value = 0.00027 for permutation of species) between functional traits and environmental data were highlighted. The trait categories significantly influenced by environmental variables were those representing feeding habits and mobility. In particular, the first category was influenced by chemical pollutants (organotin compounds and polycyclic aromatic hydrocarbons) and grain size (silt and sand), while the latter category was influenced only by chemical pollutants.Pearson correlations performed for functional vs biotic and diversity indices confirmed the validity of the chosen conceptual framework for harbour environments. Finally, linear models assessing the influence of stressors on functional parameters underlined the link between environmental data vs benthic and functional indices. Our results highlight the fact that functional trait analysis provides a useful and fast method for detecting in greater depth the effects of multiple stressors on functional diversity in marine ecosystems.