Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Regular Tetrahedron Model for the Assessment of High-Resolution Mass Spectrometry Data of Four-Way Fractionated Dissolved Organic Matter.

A regular tetrahedron model was established to pierce the fractionation of dissolved organic matter (DOM) among quaternary components by using high-resolution mass spectrometry. The model can stereoscopically visualize molecular formulas of DOM to show the preference to each component according to the position in a regular tetrahedron. A classification method was subsequently developed to divide molecular formulas into 15 categories related to fractionation ratios, the relative change of which was demonstrated to be convergent with the uncertainty of mass peak area. The practicality of the regular tetrahedron model was verified by seven kinds of sludge from waste leachate treatment and sewage wastewater treatment plants by using stratification of extracellular polymeric substances coupled with Orbitrap MS as an example, presenting the DOM chemodiversity in stratified sludge flocs. Sensitivity analysis proved that classification results were relatively stable with the perturbation of four model parameters. Multinomial logistic regression analysis could further help identify the effect of molecular properties on the fractionation of DOM based on the classification results of the regular tetrahedron model. This model offers a methodology for the assessment of specificity of sequential extraction on DOM from solid or semisolid components and simplifies the complex mathematical expression of fractionation coefficients for quaternary components.

Functional Redundant Microbiome Enhanced Anaerobic Digestion Efficiency under Ammonium Inhibition Conditions.

Revealing the role of functional redundancy is of great importance considering its key role in maintaining the stability of microbial ecosystems in response to various disturbances. However, experimental evidence on this point is still lacking due to the difficulty in "manipulating" and depicting the degree of redundancy. In this study, manipulative experiments of functional redundancy were conducted by adopting the mixed inoculation strategy to evaluate its role in engineered anaerobic digestion systems under ammonium inhibition conditions. The results indicated that the functional redundancy gradient was successfully constructed and confirmed by evidence from pathway levels. All mixed inoculation groups exhibited higher methane production regardless of the ammonium level, indicating that functional redundancy is crucial in maintaining the system's efficiency. Further analysis of the metagenome-assembled genomes within different functional guilds revealed that the extent of redundancy decreased along the direction of the anaerobic digestion flow, and the role of functional redundancy appeared to be related to the stress level. The study also found that microbial diversity of key functional populations might play a more important role than their abundance on the system's performance under stress. The findings provide direct evidence and highlight the critical role of functional redundancy in enhancing the efficiency and stability of anaerobic digestion.

Nontarget Insights into the Fate of Cl-/Br-Containing DOM in Leachate during Membrane Treatment.

Halogenated organic compounds in wastewater are persistent and bioaccumulative contaminants of great concern, but few are known at the molecular level. Herein, we focus on nontarget screening of halogenated dissolved organic matter (DOM) in highly concentrated organic matrices of waste leachates and their concentrates. Solid-phase extraction (SPE) was optimized before capturing halogenated signatures via HaloSeeker 2.0 software on mining full-scan high-resolution mass spectrometry (HRMS) fingerprints. This study identified 438 Cl-/Br-containing DOM formulas in 21 leachates and membrane concentrates. Among them, 334 formulas were achieved via SPE with mixed-sorbent cartridges (mixed-SPE), surpassing the 164 formulas achieved through Bond Elut PPL cartridges (PPL-SPE). Herein, only four samples identified via PPL-SPE exhibited a resolution of >50% for extracted Cl-/Br-containing DOM by either SPE. The halogenated DOM constituted 6.87% of the total DOM mass features. Nevertheless, more abundant adsorbable organic halogens deciphered waste leachates and highly concentrated waste streams as reservoirs for halogenated contaminants. Remarkably, 75.7-98.1% of Cl-/Br-containing DOM in primary membrane concentrates remained stable through the secondary membrane treatment, indicating the persistence of these unknown contaminants even post-treatment.

High-Bonding-Strength Polyimide Films Achieved via Thermal Management and Surface Activation.

In this study, thermal and argon (Ar) plasma/wetting treatments were combined to enhance the bonding strength of polyimide (PI) films. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) was used to analyze the changes in the PI imidization degrees. The contact angles of the PI films were also measured. The results show that the contact angles of the fully cured PI films markedly decreased from 78.54° to 26.05° after the Ar plasma treatments. X-ray photoelectron spectroscopy (XPS) analysis was also conducted on the PI surfaces. We found that the intensities of the C-OH and C-N-H bonds increased from 0% to 13% and 29% to 57%, respectively, after Ar plasma activation. Such increases in the C-OH and C-N-H intensities could be attributed to the generation of dangling bonds and the breakage of the imide ring or polymer long chains. Shear tests were also conducted to characterize the bonding strength of the PI films, which, after being treated with the appropriate parameters of temperature, plasma power, and wetting droplets, was found to be excellent at greater than 35.3 MPa.

A Novel Analog Interpolation Method for Heterodyne Laser Interferometer.

Laser interferometer technology is used in the precision positioning stage as an encoder. For better resolution, laser interferometers usually work with interpolation devices. According to the interpolation factor, these devices can convert an orthogonal sinusoidal signal into several square-wave signals via digital processing. The bandwidth of the processing will be the limitation of the moving speed of the positioning stage. Therefore, the user needs to make a trade-off between the interpolation factor and the moving speed. In this investigation, a novel analog interpolation method for a heterodyne laser interferometer has been proposed. This method is based on the principle of the lock-in amplifier (LIA). By using the proposed interpolation method, the bandwidth of the laser encoder system can be independent of the interpolation factor. This will be a significant benefit for the ultra-high resolution encoder system and the laser interferometers. The concept, design, and experiment are revealed in this manuscript. The experimental results show that the proposed interpolation method can reach nanometer resolution with a heterodyne laser interferometer, and the bandwidth of the signal is independent of the resolution.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste.

Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

Segmentation-guided Denoising Network for Low-dose CT Imaging.

BACKGROUND: To reduce radiation exposure and improve diagnosis in low-dose computed tomography, several deep learning (DL)-based image denoising methods have been proposed to suppress noise and artifacts over the past few years. However, most of them seek an objective data distribution approximating the gold standard and neglect structural semantic preservation. Moreover, the numerical response in CT images presents substantial regional anatomical differences among tissues in terms of X-ray absorbency. METHODS: In this paper, we introduce structural semantic information for low-dose CT imaging. First, the regional segmentation prior to low-dose CT can guide the denoising process. Second the structural semantical results can be considered as evaluation metrics on the estimated normal-dose CT images. Then, a semantic feature transform is engaged to combine the semantic and image features on a semantic fusion module. In addition, the structural semantic loss function is introduced to measure the segmentation difference. RESULTS: Experiments are conducted on clinical abdomen data obtained from a clinical hospital, and the semantic labels consist of subcutaneous fat, muscle and visceral fat associated with body physical evaluation. Compared with other DL-based methods, the proposed method achieves better performance on quantitative metrics and better semantic evaluation. CONCLUSIONS: The quantitative experimental results demonstrate the promising performance of the proposed methods in noise reduction and structural semantic preservation. While, the proposed method may suffer from several limitations on abnormalities, unknown noise and different manufacturers. In the future, the proposed method will be further explored, and wider applications in PET/CT and PET/MR will be sought.

[Temporal and Spatial Variation in Odor Pollution and Membrane Barrier Effect in Municipal Solid Waste Landfill].

In order to explore the source characteristics as well as the temporal and spatial variations in odor pollution in municipal waste landfills, gas samples were collected from a landfill in an eastern coastal area of China throughout winter and summer. The total concentration of malodorous substances reached 60000 µg·m-3. There were more types of odor pollutants detected in summer than in winter, the average concentration was 30-300 times higher than that in winter, and the concentration of sulfur compounds increased by 4.7-136.7 times in summer. Oxygenated compounds had the highest concentration, and the total concentration of sulfur compounds accounted for less than 10% of malodorous substances. However, sulfur compounds contributed more than 90% to the theoretical odor concentration. Sulfur compounds such as methyl mercaptan and propane mercaptan were the key odorants in the landfill. After the landfill unit was covered, the concentration of odorous substances and the theoretical odor concentration on the surface of the landfill showed an increasing trend with time, indicating that the covering had a certain odor barrier effect; however, the landfill unit still had a large odor release potential. The similarity analysis showed that the odorous gas accumulated in the unit with temporary cover and without an exhaust system could be released to the environment through the overlapping gap of the membrane and the location of membrane rupture, resulting in more serious odor pollution around the landfill at night than that during the day.

Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods.

Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.

Sarcopenia Was a Poor Prognostic Predictor for Patients With Advanced Lung Cancer Treated With Immune Checkpoint Inhibitors.

Background: It remains not well known whether skeletal muscle mass (SMM) loss has any impact on the effectiveness of immune checkpoint inhibitors (ICIs) in patients with advanced lung cancer. We aimed to evaluate the association between SMM and clinical outcome of patients with advanced lung cancer receiving ICIs as first line or second line. Materials and Methods: From March 1st, 2019 to March 31st, 2021 at our hospital, 34 patients with advanced lung cancer treated with first-line or second-line ICIs were enrolled retrospectively. The estimation of skeletal muscle index (SMI) for sarcopenia was assessed at the level of the third lumbar vertebra (L3) on computed tomography (CT) images obtained within 4 weeks before initiation of ICIs treatment. The impact of sarcopenia (low SMI) on progression free survival (PFS) was analyzed using Kaplan-Meier method and log-rank tests. The effect of various variables on PFS was evaluated using Cox proportional hazards regression model with univariate and multivariate analysis. The impact on treatment response including objective response rate (ORR) and disease control rate (DCR) and immunotherapy related adverse events (irAEs) between patients with and without sarcopenia was compared by the chi-squared test. The comparison of SMI value between patients with objective response (OR), disease control (DC) and those without OR and DC was used student t-test or Mann-Whitney U test. Results: Both in univariate and multivariate analysis, sarcopenia and treatment lines were the predictive factors for PFS (p < 0.05). Patients with sarcopenia had significantly shorter PFS than that of non-sarcopenic ones [6.57 vs. 16.2 months, hazard ratios (HR) = 2.947 and 3.542, and 95% confidence interval (CI): 1.123-13.183 and 1.11-11.308, p = 0.022 and 0.033]. No significant difference in ORR and irAEs was found. Patients with sarcopenia had lower DCR than those without sarcopenia. The mean SMI value of DCR group and non-DCR group was 32.94 ± 5.49 and 44.77 ± 9.06 cm2/m2, respectively (p = 0.008). Conclusion: Sarcopenia before immunotherapy might be a significant predictor for poor prognosis including shorter PFS and lower DCR in patients with advanced lung cancer treated with ICIs as first line or second line.

Upcycling waste polyvinyl chloride: One-pot synthesis of valuable carbon materials and pipeline-quality syngas via pyrolysis in a closed reactor.

Polyvinyl chloride (PVC) is one of the most commonly used plastics. The treatment and recycling of PVC waste is still challenging, due to its non-biodegradability, low thermal stability, high Cl content and low product value. In this study, a one-pot method was developed to upcycle PVC into valuable carbon materials, pipeline-quality pyrolysis gas and chlorides. The well-designed process included dechlorination by Cl-fixative (ZnO or KOH), carbonization of dechlorinated polyenes, and modification of carbon materials in sequence. ZnO and KOH converted 84.48% and 94.15% of total Cl into corresponding chlorides, respectively. CH4 and H2 accounted for 81.87-99.34 vol% of pyrolysis gas with higher heat values of 30.11-32.84 MJ m-3, which can be used as substitute natural gas. As high as 83.13% of the C element was converted into carbon materials. The morphology, structure and property of carbon materials can be modified by different Cl-fixatives. Millimeter-scale carbon spheres with mono-dispersity and porous carbon with a high specific surface area of 1922 m2 g-1 were obtained when ZnO and KOH were added, respectively. Moreover, the reaction mechanisms of PVC with Cl-fixatives were also deciphered through thermogravimetric analysis and thermodynamic simulation.

Repercussions of clinical waste co-incineration in municipal solid waste incinerator during COVID-19 pandemic.

During coronavirus disease 2019 pandemic, the exponential increase in clinical waste (CW) generation has caused immense burden to CW treatment facilities. Co-incineration of CW in municipal solid waste incinerator (MSWI) is an emergency treatment method. A material flow model was developed to estimate the change in feedstock characteristics and resulting acid gas emission under different CW co-incineration ratios. The ash contents and lower heating values of the feedstocks, as well as HCl concentrations in flue gas showed an upward trend. Subsequently, 72 incineration residue samples were collected from a MSWI performing co-incineration (CW ratio <10 wt%) in Wuhan city, China, followed by 20 incineration residues samples from waste that were not co-incineration. The results showed that the contents of major elements and non-volatile heavy metals in the air pollution control residues increased during co-incineration but were within the reported ranges, whereas those in the bottom ashes revealed no significant changes. The impact of CW co-incineration at a ratio <10 wt% on the distribution of elements in the incineration residues was not significant. However, increase in alkali metals and HCl in flue gas may cause potential boiler corrosion. These results provide valuable insights into pollution control in MSWI during pandemic.

[Experimental Influence of Food Waste Fermentation Broth on the Soil Quality in a Loess Hilly Area].

Loess is widely distributed in northwestern China. Due to the arid climate and rainstorm erosion, lack of nutrients and microorganisms, as well as severe salinization limits the ecosystem carrying capacity of loess soil, which has become one of the major causes of regional land desertification. The fermentation broth derived from food waste usually contains substantial organic acids and nutrients such as nitrogen and phosphorus, and it has the advantages of being easily produced industrially and applied as fertilizer. Hence, this broth has the potential to become a soil amendment for loess soils. This work studied the Lanzhou loess, which is a typical soil of the Loess Plateau of China, fertilized with fermentation broth for the evaluation of physicochemical properties and microbial analyses. After the application of the broth amendment, the total nitrogen, available phosphorus and potassium, and organic matter content increased by 363%, 577%, 308%, and 204%, respectively. After planting grass, including Halogeton arachnoideus Moq. and Medicago sativa L., the comprehensive soil fertility level was further improved and the total salt content of the soil was decreased by 2.3 g·kg-1 and 1.2 g·kg-1, respectively. Meanwhile, the fermentation broth promoted the growth of microorganisms, including bacteria and archaea, which increased by 22 times, and fungi by 8.3 times. Therefore, food waste fermentation broth is conducive to further forming plant-microorganisms symbiosis, improving the ecological environment quality of loess soils.

[Pollutant Removal Efficiency of Different Units Along a Mature Landfill Leachate Treatment Process in a Membrane Biological Reactor-Nanofiltration Combined Facility].

Properties of landfill leachate are complex. Therefore, leachate should be treated by combined processes with both biological and advanced methods. Due to the shortage of engineering-scale assessment data about the pollutant treatment contribution of individual process units, existing optimization methods still lack theoretical support. Here, a membrane biological reactor (MBR)+nanofiltration (NF) system with a capacity of 800 m3·d-1 was examined. Conventional physiochemical parameters and fluorescent parameters were examined to analyze the contribution of each process unit to treating mature landfill leachate. Furthermore, the transformation of dissolved organic matter (DOM) was evaluated using excitation emission matrix fluorescence spectroscopy-parallel factor (EEMs-PARAFAC). Results showed that the biological treatment removed soluble nitrogen (dissolved nitrogen, DN) by 74.7%, 54.6% occurred in the first-stage denitrification unit. The external ultrafiltration unit reduced dissolved chemical oxygen demand (COD) and dissolved organic carbon (DOC) by 92.2% and 93.3%, respectively. The nanofiltration unit effectively removed heavy metals and salts. Based on the tracking of DOM using fluorescent parameters, the first-stage denitrification unit was found to remove 75.4% of protein-like substances. The ultrafiltration unit mainly retained DOM with high hydrophilicity, while humus with high aromaticity was mainly retained by nanofiltration. The higher the degree of humification, the better the interception effect that was obtained. This indicates that biological treatment using the MBR process can be simplified, and ultrafiltration should prove reliable at preventing clogging during the treatment of mature landfill leachate.

Upcycling of PET waste into methane-rich gas and hierarchical porous carbon for high-performance supercapacitor by autogenic pressure pyrolysis and activation.

The explosive growth of polyethylene terephthalate (PET) wastes has brought serious pollution to the environment. Here, PET waste was upcycled into methane-rich pyrolysis gas and carbon material for energy storage through autogenic pressure pyrolysis and post-activation. The pyrolysis gas contained 34.58 ± 0.23 vol% CH4. After CO2 removal, the high caloric value of the pyrolysis gas could reach 29.2 MJ m-3, which could be used as a substitute natural gas. Pyrolytic carbon was further activated by KOH and ZnCl2. KOH-activated carbon (AC-K) obtained a hierarchical porous structure, a high specific surface area of 2683 m2 g-1 and abundant surface functional groups. Working as supercapacitor electrodes, AC-K exhibited an outstanding specific capacitance of 325 F g-1 at a current density of 0.5 A g-1. After 5000 charge-discharge cycles, AC-K still retained 91.86% of the initial specific capacitance. This study provides a sustainable way to control plastic-derived pollution and alleviate the energy crisis.

Inhibition of chlorobenzenes formation by calcium oxide during solid waste incineration.

Solid waste incineration is a major emission source of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). The injection of N- and S-containing compounds is an effective way to suppress the formation of PCDD/Fs, but this approach is still shortcoming because additional pollutants such as NH3 and SOx are emitted. To avoid the secondary pollutions, a de novo synthesis inhibition mechanism in the presence of CaO was postulated to transform CuCl2 to CuO and deplete Cl2 and HCl. Chlorobenzenes (CBzs), which are indicators and precursors of PCDD/Fs, were adopted to prove the inhibitory effect of CaO at 400 °C, using both simulated synthetic ash and extracted air pollution control residues. As the molar ratio of CaO to CuCl2 exceeded 3, the residual carbon increased, and the inhibition efficiency of CBzs exceeded 93 %. This performance is superior to the corresponding performance of NH4H2PO4, which has been proved to be a potential inhibitor. Furthermore, with CaO, chlorides remained in the solid phase and had inactive catalytic performance; and they were the major products rather than HCl, Cl2 and Cu2OCl2. The addition of CaO during waste incineration therefore can facilitate the abatement of PCDD/Fs contamination and reduce the emissions of acid gas simultaneously.

Identification of Cell Status via Simultaneous Multitarget Imaging Using Programmable Scanning Electrochemical Microscopy.

A programmable multitarget-response electrochemical imaging technique was presented using scanning electrochemical microscopy (SECM) combined with a self-designed waveform. The potential waveform applied to the tip decreased the charging current caused by the potential switch, enhancing the signal-to-noise ratio. This programmable SECM (P-SECM) method was used to scan a metal strip for verifying its feasibility in feedback mode. Since it could achieve simultaneous multitarget imaging during one single imaging process, PC12 cells status was imaged and identified through three different molecules (FcMeOH, Ru(NH3)63+, and oxygen). The FcMeOH image eliminated the error from cell height, and the Ru(NH3)63+ image verified the change of membrane permeability. Moreover, the oxygen image demonstrated the bioactivity of the cell via its intensity of respiration. Combining information from these three molecules, the cell status could be determined accurately and also the error caused by time consumption with multiple scans in traditional SECM was eliminated.