Lead (Pb) is an environmental pollutant that negatively affects rice plants, causing damage to the root system and chloroplast structures, as well as reducing growth. 24-Epibrasnolide (EBR) is a plant growth regulator with a high capacity to modulate antioxidant metabolism. The objective of this research was to investigate whether exogenous EBR application can mitigate oxidative damage in Pb-stressed rice plants, measure anatomical structures and evaluate physiological and biochemical responses connected with redox metabolism. The experiment was randomized with four treatments, including two lead treatments (0 and 200 μM PbCl₂, described as - Pb and + Pb, respectively) and two treatments with brassinosteroid (0 and 100 nM EBR, described as - EBR and + EBR, respectively). The results revealed that plants exposed to Pb suffered significant disturbances, but the EBR alleviated the negative interferences, as confirmed by the improvements in the root structures and antioxidant system. This steroid stimulated the root structures, increasing the epidermis thickness (26%) and aerenchyma area (50%), resulting in higher protection of this tissue against Pb²⁺ ions. Additionally, EBR promoted significant increases in superoxide dismutase (26%), catalase (24%), ascorbate peroxidase (54%) and peroxidase (63%) enzymes, reducing oxidative stress on the photosynthetic machinery in Pb-stressed plants. This research proved that EBR mitigates the toxic effects generated by Pb in rice plants.

Previous studies have shown that accumulation of perfluoroalkyl acids (PFAAs) in the tissues of aquatic species is highly variable. Movement and migration patterns in these species represent an important consideration when evaluating contaminant accumulation in exposed biota, and may have a large influence on the risk profiles for migratory seafood species. In this study, relationships between PFAA concentrations in muscle and liver tissue, and recent fish migration history (inferred from metals profiles in fish otoliths, otherwise known as otolith chemistry) were evaluated in Sea Mullet (Mugil cephalus). A greater number of PFAAs, and higher concentrations, were found in liver compared to muscle tissue. Perfluorooctane sulfonate (PFOS) was present in highest concentrations in both muscle and liver tissues, and there was strong correlation in concentrations between these two tissues. PFOS was found to decrease and increase alongside recent strontium and barium concentrations (respectively) in the otolith, suggesting higher concentrations of PFAAs in fish recently exposed to comparatively lower salinity environments. This study highlights how otolith chemistry can be employed to examine links between contaminant concentrations in fish, and their recent migration history. This approach shows promise for studying contaminant residues in mobile seafood species within the natural environment.

The occurrence of veterinary antibiotics and hydro-chemical parameters in eleven natural springs in a livestock production area is evaluated, jointly with the characterization of their DOM fingerprint by Orbitrap HRMS. Tetracycline and sulfonamide antibiotics were ubiquitous in all sites, and they were detected at low ng L⁻¹ concentrations, except for doxycycline, that was present at μg L⁻¹ in one location. DOM analysis revealed that most molecular formulas were CHO compounds (49 %–68 %), with a remarkable percentage containing nitrogen and sulphur (16 %–23 % and 11 %–24 %, respectively). Major DOM components were phenolic and highly unsaturated compounds (~90 %), typical for soil-derived organic matter, while approximately 11 % were unsaturated aliphatic, suggesting that springs may be susceptible to anthropogenic contamination sources. Comparing the DOM fingerprint among sites, the spring showing the most different profile was the one with surface water interaction and characterized by having lower CHO and higher CHOS formulas and aliphatic compounds. Correlations between antibiotics and DOM showed that tetracyclines positively correlate with unsaturated oxygen-rich substances, while sulfonamides relate with aliphatic and unsaturated oxygen-poor compounds. This indicates that the fate of different antibiotics will be controlled by the type of DOM present in groundwater.

In this study, water and sediment samples from the Jiaozhou Bay and surrounding rivers were collected to analyze the seasonal occurrence and allocation of 12 organophosphate esters (OPEs) and the associated ecological risk. The higher contamination of OPEs in the adjacent rivers indicated the impact of terrestrial input. Tris(1-chloropropan-2-yl) phosphate (TCIPP) was the predominant OPE in the four environmental sample groups investigated. The spatial distribution of OPEs in seawater varied greatly seasonally and was mainly affected by terrestrial input, with OPEs being redistributed under the influence of tidal currents. The partition coefficients (log Kₒc) of the OPEs were calculated, and their strong correlation with the log Kₒw (octanol-water) values suggested that the water-sediment allocation was significantly affected by hydrophobicity. The homologous relationships among the 7 OPEs with detection frequencies greater than 40% were identified by principal component analysis (PCA). The partial least squares regression (PLSR) model explicated that ∑OPEs cycling dynamics and principal controlling factors were dissimilar in the bay versus surrounding rivers. The risk quotient (RQ) faced by typical organisms in seawater and river water indicated that short-term OPEs exposure was safe for green algae, daphnia and fish. The organisms in rivers faced the higher ecological risk of OPEs in spring than in summer and winter. Therefore, the terrestrial transport of OPEs in spring should be controlled.

Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3 × 3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R²), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R² = 0.9415, RMSE = 0.0589, and MAE = 0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion.

A systematic investigation into bioaccessible heavy metals in shellfish Crassostrea ariakensis, Chlamys farreri, and Sinonovacula constricta from coastal cities Shenzhen, Zhoushan, Qingdao, and Dandong was carried out to assess the potential health risk to residents in coastal regions in China. The bioaccessible fractions of heavy metals were (μg‧g⁻¹): Zn (0.63–15.01), Cu (0.10–12.91), Cd (0.01–0.64), As (0.11–0.33), Cr (0.07–0.12), Pb (0.01–0.03). The bioaccessibilities of heavy metals were Cr 61.86%, inorganic As (iAs) 60.44%, Pb 55.74%, Cu 46.83%, Zn 28.16%, and Cd 24.99%. As for child and adult, the bioaccessibility-corrected estimated daily intakes were acceptable and the non-carcinogenic risks posed by heavy metals were not obvious. The carcinogenic risks posed by bioaccessible heavy metals at the fifth percentile were 10-fold higher than the acceptable level (10⁻⁴), with iAs and Cd to be the major contributors, regardless of child or adult. The probabilistic estimation showed the low risk of shellfish consumption, which was verified by higher values of maximum allowable consumption rate and monthly meals at the 95 percentile; while some control of consumption rate and monthly meals was necessary for reducing heavy metal exposure of most shellfish samples, except for the safe consumption of S. constricta for both child and adult in Qingdao and Shenzhen, China.

Based on the bond order, fukui indices and other related descriptors, as well as temperature, a new QSAR model was established to predict the rate constant (kO₃) of VOCs degradation by O₃. 302 logkO₃ values (178–409 K) of 149 VOCs were divided into training set (242 logkO₃) and test set (60 logkO₃), respectively, which were used to construct and verify the QSAR model. The optimal model (R² = 0.83, q² = 0.82, Qₑₓₜ² = 0.72) shows that EHOMO, BOₓ and q(C⁻)ₙ have a greater influence on the value of logkO₃. In addition, fukui indices and logkO₃ are well correlated. The applicability domains of the current models can be used to predict kO₃ of a wide range of VOCs at different temperatures.

Nano metal oxide particles (NMOPs) are widely used in daily life because of their superior performance, and inevitably enter the sewage treatment system. Pollutants in sewage are adsorbed and degraded in wastewater treatment plants (WWTPs) depending on the microbial aggregates of activated sludge system to achieve sewage purification. NMOPs may cause ecotoxicity to the microbial community and metabolism due to their complex chemical behavior, resulting in a potential threat to the safe and steady operation of activated sludge system. It is of great significance to clarify the influencing mechanism of NMOPs on activated sludge system and reduce the risk of WWTPs. Herein, we first introduce the physicochemical behavior of six typical engineering NMOPs including ZnO, TiO₂, CuO, CeO₂, MgO, and MnO₂ in water environment, then highlight the principal mechanisms of NMOPs for activated sludge system. In particular, the performance recovery mechanisms of activated sludge systems in the presence of NMOPs and their future development trends are well documented and discussed extensively. This review can provide a theoretical guidance and technical support for predicting and evaluating the potential threat of NMOPs on activated sludge systems, and promoting the establishment of effective control strategies and performance recovery measures of biological wastewater treatment process under the stress of NMOPs.

Nitrated polycyclic aromatic hydrocarbons (nitro-PAHs) have been widely studied for their mutagenic and carcinogenic effects. This study aims to investigate whether exposure to nitro-PAHs is associated with biomarkers of carbohydrate metabolism, an underlying risk factor for metabolic disorder. Early morning urine and blood samples were longitudinally collected two times with a four-week interval from 43 healthy adults. Five urinary amino-PAHs (1-aminonaphthalene, 2-aminonaphthalene, 9-aminophenanthrene, 2-aminofluorene, and 1-aminopyrene) were measured as biomarkers of nitro-PAH exposures. We measured plasma concentrations of glucose and six amino acids that can regulate insulin secretion, including aspartate (Asp), glutamate (Glu), glutamine (Gln), alanine (Ala), Arginine (Arg), and ornithine (Orn). We found that increasing concentrations of 9-aminophenanthrene were significantly associated with increasing glucose levels and with decreasing Asp, Glu, Ala, and Orn levels. We estimated that 26.4 %–43.8 % of the 9-aminophenanthrene-associated increase in glucose level was mediated by Asp, Glu, and Orn. These results suggest that exposure to certain nitro-PAHs affects glucose homeostasis, partly resulting from the depletion of insulin-stimulating amino acids (Asp, Glu, and Orn).