Microplastics are widespread across the global oceans, yet the potential risks of the ubiquitous environmental contaminant to marine organisms has been less known. Accumulation of microplastics and associated contaminants in marine fish, may pose adverse impacts to human health via seafood consumption. This study evaluated microplastic contamination in 24 fish species collected from Beibu Gulf, one of the world’s largest fishing grounds in South China Sea. Microplastics were detected in 12 fish species at an abundance of 0.027–1.000 items individual⁻¹ and found in fish stomach, intestines and gills with the count percentage of 57.7%, 34.6% and 7.7%, respectively. Transparent fibers were observed as the predominant microplastics, which might be ingested accidently by fish or transferred through other animals at lower trophic levels. Majority of microplastics were identified as polyester (44%) and nylon (38%), whereas polypropylene (6%), polyethylene (6%), and acrylics (6%) were also found. Relatively, higher microplastic abundances were found in demersal fish compared to the pelagic species. Overall, the abundance of microplastics was documented as relatively low in the commercial fish collected from the open water of Beibu Gulf, South China Sea.

The strict environmental management has been implemented in Taihu Basin to reduce the surface water contamination; however, the effectiveness of the management actions has not been comprehensively evaluated. In the present study, 364 samples were collected during four campaigns over a span of one year from surface water, municipal wastewater treatment plants (MWWTPs), industrial wastewater treatment plants (IWWTPs), industrial enterprises, and aquaculture in a typical region in the Upper Taihu Basin. Overall concentration, temporal variation and spatial distribution of 16 PAHs in surface water and various pollution sources were evaluated and the potential pollution sources were identified. Results showed that concentrations of individual PAHs in the surface water ranged from less than the limit of quantification (LOQ) to 949 ng L⁻¹, indicating a reduction of PAH contamination level after the implementation of environmental management actions. Influent of MWWTPs and wastewater from industrial enterprises exhibited relatively high ∑PAHs concentrations (mean: 880 ng L⁻¹ and 642 ng L⁻¹, respectively); these samples also exhibited a similar seasonal variation as well as composition of PAH congeners to those found in surface water, and therefore were designated as the main emission sources of PAHs in the studied region. Additional source apportionment using principal component analysis was also conducted to verify the proposed sources and diagnose other pollution sources. The findings provided a thorough understanding of PAH pollution, especially its major emission sources, in a typical region with pollution-intensive industries after the implementation of strict environmental management.

This work aimed at predicting the toxic effects of phenolic compounds in Ba River on the health of female sharpbelly (Hemiculter lucidus) by the de novo transcriptomic analysis of the liver. Sharpbelly, a native fish living in freshwater ecosystem of East Asia, were sampled upstream, near, and downstream of a wastewater discharge to the Ba river. Based on the occurrence of bisphenol A (BPA), nonylphenol (NP), and 4-tert-octylphenol (4-t-OP) in the water and fish sampled from each site, up-, mid-, and down-stream were interpreted as control, high, and low treatment groups, respectively. In the mid-stream group the Fulton’s condition factor (CF) and body weight were remarkably increased by approximate 20%; the gonado-somatic index (GSI) and hepatosomatic index (HSI) in mid-stream fish showed a similar increasing trend but lacking of statistical difference. Exposure to wastewater effluent caused 160 and 162 differentially expressed genes (DEGs) in up-mid and down-mid stream groups, respectively. Two sets of DEGs were primarily enriched in the signaling pathways of drug metabolism, endocrine system, cellular process, and lipid metabolism in the mid-stream sharpbelly, which may alter the fish behavior, disrupt the reproductive function, and lead to hypothyroidism, hepatic steatosis, etc. Taken together, our results linked the disrupted signaling pathways with activities of phenolic compounds to predict the potential effects of wastewater effluent on the health of wild fish.

Particulate matter (PM) pollution is a serious environmental problem in most of the cities in the Yangtze River Delta region. Plants can effectively filter ambient air by adsorbing PM. However, only a few studies have paid attention to the dynamic changes and seasonal differences in particle retention capacities of plants under long-term pollution. In this study, we investigated the dynamic changes in particle retention capabilities of the evergreen, broad-leaved, greening plants—Euonymus japonicus var. aurea-marginatus and Pittosporum tobira—in spring and summer. We employed an open-top chamber to simulate the severity of the tail gas pollution. The results showed that, both the plants reached a saturated state in 18–21 days, under continuous exposure to pollution (daily concentration of PM₂.₅: 214.64 ± 321.33 μg·cm⁻³). This was 6–8 days longer than that in the field experiments. In spring, the maximum retention of total particulate matter per unit leaf area of E. japonicus var. aurea-marginatus and P. tobira was 188.47 ± 3.72 μg cm⁻² (18 days) and 67.63 ± 2.86 μg cm⁻² (21 days), respectively. In summer, E. japonicus var. aurea-marginatus and P. tobira reached the maximum retention of the particle on the 21st day, with a net increase of 94.10 ± 3.77 μg cm⁻² and 27.81 ± 3.57 μg cm⁻², respectively. Irrespective of season, the particle retention capacity of E. japonicus var. aurea-marginatus was higher than that of P. tobira, and it showed a better effect on reducing the concentration of fine particles in the atmosphere. The particle retention of the two plants was higher in spring than that in summer. E. japonicus var. aurea-marginatus displayed a significant difference in particle retention between the seasons, while P. tobira did not show much difference. These results will provide a foundation for future studies on the dynamic changes and mechanism of particle retention in plants and management practices by employing plants for particle retention in severely polluted areas.

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.

The optical properties of light-absorbing carbonaceous aerosols have caused increasing concerns due to their significant impacts on local and regional climates. In this study, particles from biomass burning in home stoves were collected and evaluated for their optical properties. The absorption Ångström exponent (AAE) values ranged from 1.17 to 2.92 and negatively correlated with the modified combustion efficiency, indicatinging more brown carbon in combustion emissions with relatively low combustion efficiencies. The average contribution of brown carbon to the total aerosol absorption at 370 nm was equally as important as that of black carbon (BC), with the average relative contribution fraction of 50% varying from 10% to 84% for different biomasses. The average value of the mass absorption efficiency (MAE) of BC (MAEBC) at 880 nm was positively correlated with the ratio of organic carbon to elemental carbon, indicating the significant coating effects of organic aerosols. The MAE values of BrC at 370 nm were in the range of 1.1–11.3 m²/g, with an average of 5.1 ± 2.2 m²/g. The estimated absorption emission factors at 370 nm and 880 nm were 3.75 ± 3.45 and 0.84 ± 0.78 m²/kg, respectively. Optical property information of particles emitted from real-world biomass burning are imperative in future modeling studies of biomass burning impacts on climate. The limitation of the relatively small sample size for each subgroup fuel calls for more field- and lab-based emission characterization research.

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM₂.₅ (particles with an aerodynamic diameter smaller than 2.5 μm), PM₁₀ (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF_PSO was presented and the predictive performance was analyzed. The results show that the RF_PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM₂.₅, PM₁₀ and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques.

Recent studies have demonstrated the ability of mealworm (Tenebrio molitor) for plastic degradation. This study is focused on changes in microbiome structure depending on diets. Microbial community obtained from oat and cellulose diet formed similar group, two kinds of polyethylene formed another group, while polystyrene diet showed the highest dissimilarity. The highest relative abundance of bacteria colonizing gut was in PE-oxodegradable feeding, nevertheless all applied diets were higher in comparison to oat. Dominant phyla consisted of Proteobacteria, Bacteroides, Firmicutes and Actinobacteria, however after PS feeding frequency in Planctomycetes and Nitrospirae increased. The unique bacteria characteristic for cellulose diet belonged to Selenomonas, while Pantoea were characteristic for both polyethylene diets, Lactococcus and Elizabethkingia were unique for each plastic diet, and potential diazotropic bacteria were characteristic for polystyrene diet (Agrobacterium, Nitrosomonas, Nitrospira).Enzymatic similarity between oatmeal and cellulose diets, was shown. All three plastics diet resulted in different activity in both, digestive tract and bacteria. The enzymes with the highest activity were included phosphatases, esterases, leucine arylamidase, β-galactosidase, β-glucuronidase, α-glucosidase, β-glucosidase, chitinase, α-mannosidase and α-fucosidase. The activity of digestive tract was stronger than cultured gut bacteria. In addition to known polyethylene degradation methods, larvae may degrade polyethylene with esterase, cellulose and oatmeal waste activity is related with the activity of sugar-degrading enzymes, degradation of polystyrene with anaerobic processes and diazotrophs.

Development of new approach methodologies is urgently needed to characterize the likelihood that complex mixtures of chemicals affect water quality. Omics advances in ecotoxicology allow assessment on a broadest coverage of disrupted biological pathway by mixtures. Here the usefulness of transcriptomic analyses for evaluation of combined effects and identification of main effect components are explored. Two artificial mixtures (Mix 1 and Mix 2) were tested by a concentration-dependent reduced zebrafish transcriptome (CRZT) approach and toxicity bioassays using zebrafish embryos. Then, the toxicities and transcriptomic effects of 12 component chemicals on embryos were incorporated into additivity models to characterize the combined effects of chemicals in mixtures and to identify the main bioactive compounds. Mix 1 and Mix 2 displayed similar embryo toxicities (LD₅₀: 6.6 μM and 8.7 μM, respectively), however, Mix 2 elicited broader biological process perturbations and 5-fold higher transcriptome potency (point of departure eliciting a 20% pathway response, PODₚₐₜₕ₂₀) than Mix 1. The predicted mixture toxicities derived from additivity expectations deviated by 2-fold or less from the measured embryo toxicities except for the Jaw defect endpoint; most biological processes deviated by 3-fold or less. Finally, diclofenac (DFC) and propiconazole (PCZ) were identified as the main contributing components (≥80% explanation) to the embryo toxicity and biological process perturbations by Mix 1. While DFC and chlorophene (CLP) explained up to 80% of the embryo toxicities and biological effects of Mix 2 associated with development and Metabolism processes. The CRZT approach provides a powerful tool for assessment of biological pathways perturbed by chemicals in mixtures and for identification of main bioactive compounds.

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (QN₁) and a topological parameter (XᵛPC₄). The optimal model could be mathematically described as following: pLC₅₀ = −7.24–0.30XᵛPC₄ + 0.76logD - 26.15QN₁ - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²ₐdⱼ (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.