Application of hyperspectral and deep learning in farmland soil microplastic detection.

The ecological environment is gravely threatened by the buildup of microplastics (MPs) in soil. Currently, there are no established techniques for detecting MPs in soil. Some of the standard chemical detection methods now in use are time-consuming and cumbersome. This research suggested a method for identifying soil microplastic polymers (MPPs) based on convolutional neural networks (CNN) and hyperspectral imaging (HSI) technologies to address this issue. The categorization model for MPPs on the soil surface was first established by simulating the natural soil environment in the lab. While decision tree (DT) and support vector machine (SVM) models' classification accuracy was 87.9 % and 85.6 %, respectively, that of CNN was 92.6 %. The HIS and CNN model combination produced the best classification results out of all of these models. Secondly, farmland in Guangzhou's Tianhe, Panyu, and Zengcheng districts was sampled for surface soil samples measuring 0-20 cm in order to confirm the model's accuracy in the actual environment. Before data analysis, the physicochemical properties of soil samples were determined by a standardization scheme. MPs in soil samples were extracted by traditional chemical detection method and their chemical properties were obtained as the results of the control group. Then, CNN was applied to hyperspectral data from soil samples collected for MPs detection. Finally, it was demonstrated that the physical and chemical properties of the soil have an impact on the accuracy of the model through the investigation of the physical and chemical characteristics of soil samples from three distinct areas. On the other hand, the results indicated that the suggested technique offers quick and non-destructive results for MPPs detection when comparing the detection results of hyperspectral and conventional chemical methods.

Parental exposure to polystyrene nanoplastics and di(2-ethylhexyl) phthalate induces transgenerational growth and reproductive impairments through bioaccumulation in Daphnia magna.

The ubiquitous presence of polystyrene nanoplastics (PSNPs) and di(2-ethylhexyl) phthalate (DEHP) in the aquatic environment may cause unpredictable negative effects on aquatic organisms and even continue to the offspring. This study assessed the transgenerational impacts of parental exposure to PSNPs and DEHP over four generations (F0-F3) of Daphnia magna. A total of 480 D. magna larvae (F0, 24 h old) were divided into four groups with six replicates (each of them contains 20 D. magna) and exposed with dechlorinated tap water (control), 1 mg/L PSNPs, 1 µg/L DEHP, and 1 mg/L PSNPs + 1 µg/L DEHP (PSNPs-DEHP) until spawning begins. Subsequent to exposure, all the surviving F1 offspring were transferred to new water and continued to be cultured until the end of F3 generation births in all groups. The results showed that the PSNPs accumulated in F0 generation and were inherited into F1 and F2 generations, and disappeared in F3 generation in PSNPs and PSNPs-DEHP groups. However, the accumulation of DEHP lasted from F0 generation to F3 generation, despite a significant decline in F2 and F3 generations in DEHP and PSNPs-DEHP groups. The accumulation of PSNPs and DEHP caused overproduction of reactive oxygen species in F0-F2 generations and fat deposition in F0-F3 generations. Additionally, single and in combination parental exposure to PSNPs and DEHP induced regulation of growth-related genes (cyp18a1, cut, sod and cht3) and reproduction-related genes (hr3, ftz-f1, vtg and ecr) in F0-F3 generations. Survival rates were decreased in F0-F1 generations and recovered in F2 generation in all treatment groups. Furthermore, the spawning time was prolonged and the average number of offspring was increased in F1-F2 generaions as a defense mechanism against population mortality. This study fosters a greater comprehension of the transgenerational and reproductive effects and associated molecular mechanisms in D. magna.

A novel framework-based meta-analysis for in-depth characterization of microplastic pollution and associated ecological risks in Chinese Bays.

Among aquatic ecosystems, bays are ubiquitously contaminated with microplastics (MPs, size <5 mm), but a comprehensive understanding of their pollution characterization in Chinese Bays is largely elusive. The current study aims to systematically highlight factors intricating MP contamination as well as their geographic distribution, interactions, risk evaluation, and abundance prediction in bays. MPs' abundance was varied in different bays, at concentrations ranging between 0.26 ± 0.14-89, 500 ± 20, 600 items/m3 in water, 15 ± 6-6433.5 items/kg dry weight in sediment and 0.21 ± 0.10-103.5 items/individual in biota. Redundancy analysis, Permannova, and GeoDetector model revealed that the sampling and extraction/identification methods, and geographical locations were the major drivers affecting MP distribution and characteristics. The Mantel test highlighted that the MP characteristics changed with geographic distance, higher in water than that in sediment and biota. ANOSIM results showed that the different environmental media exhibit significant differences in MP characteristics (e.g., color, shape, and polymer). The ARIMA model predicted that Sanggou Bay and Hangzhou Bay have a higher potential for significantly increasing MP contamination in the future. The highest hazard index (HI) values for water, sediment, and biota were respectively reported at Jiaozhou Bay (18,844.16), Bohai Bay (11,485.37), and Dongshan Bay (48,485.11). The highest values for the ecological risk index (RI) in water, sediment, and biota were detected at Beibu Gulf (6,129,559.02), Haikou Bay (2229.14), and Dongshan Bay (561,563.05), respectively. Overall, this framework can be used at different scales and in different environments, which makes it useful for understanding and controlling MP pollution in the ecosystem.

Di-(2-ethylhexyl) phthalate exacerbated the toxicity of polystyrene nanoplastics through histological damage and intestinal microbiota dysbiosis in freshwater Micropterus salmoides.

Organic pollutants such as di-(2-ethylhexyl) phthalate (DEHP) interact with nanoplastics (NPs) and change their bioavailability and toxicity to aquatic organisms. This study aims to assess the ecotoxicological impacts of NPs in the presence and absence of DEHP on juvenile largemouth bass (LMB) Micropterus salmoides. Therefore, LMB was fed with diets containing various concentrations (0, 2, 10, and 40 mg/g) of polystyrene nanoplastics (PSNPs) by the weight of diets. After a 21-day of PSNPs dietary exposure, LMB was treated with DEHP at 450 µg/L through waterborne exposure for three days. Our results showed that PSNPs were accumulated in the intestinal tissues, which significantly decreased the feeding and growth rates in LMB. The histopathological analysis showed the intestine and liver of LMB were subjected to various degrees of structural damage caused by PSNPs, and DEHP-PSNP co-exposure enhanced those histopathological damages in both tissues. Additionally, the co-exposure induced oxidative stress in terms of increased activities of glutathione S-transferase, catalase, and superoxide dismutase enzymes in the liver, intestine, spleen, and serum. Furthermore, the co-exposure significantly changed the intestinal microbial composition, i.e., the decrease in the abundance of probiotics (Bacteroidetes and Proteobacteria) and the increase in pathogenic bacteria (Firmicutes) posed a great threat to fish metabolism and health. Therefore, this study highlights that the presence of DEHP enhances the toxicity of NPs on LMB in freshwater and suggests the regulated use of plastic and its additives for improving the health status of aquaculture fish for food safety in humans.

Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil.

Microplastics (MPs) are emerging environmental pollutants and their accumulation in the soil can adversely affect the soil biota. This study aims to employ hyperspectral imaging technology for the rapid screening and classification of MPs in farmland soil. In this study, a total of 600 hyperspectral data are collected from 180 sets of farmland soil samples with a hyperspectral imager in the wavelength range of 369- 988 nm. To begin, the hyperspectral data are preprocessed by the Savitzky-Golay (S-G) smoothing filter and mean normalization. Second, principal component analysis (PCA) is used to minimize the dimensions of the hyperspectral data and hence the amount of data, making the subsequent model easier to construct. The cumulative contribution rate of the first three principal components is reached 98.37%, including the main information of the original spectral data. Finally, three models including decision tree (DT), support vector machine (SVM), and convolutional neural network (CNN) are established, all of which can achieve well classification effects on three MP polymers including polyethylene (PE), polypropylene (PP), and polyvinyl chloride (PVC) in farmland soil. By comparing the recognition accuracy of the three models, the classification accuracy of DT and SVM is 87.9% and 85.6%, respectively. The CNN model based on the S-G smoothing filter obtains the best prediction effect, the classification accuracy reaches 92.6%, exhibiting obvious advantages in classification effect. Altogether, these results show that the proposed hyperspectral imaging technique identifies the soil MPs rapidly and nondestructively, and provides an effective automated method for the detection of polymers, requiring only rapid and simple sample preparation.