Real-world emission characteristics of carbonyl compounds from on-road vehicles in Beijing and Zhengzhou, China.

Carbonyl compounds have a profound role in atmospheric chemistry, which can cause the formation of ozone and secondary organic aerosols. On-road vehicle emissions are an important source of carbonyl compounds, but systematic knowledge of real-world emission characteristics is still scarce. In this study, a total of 181 on-road vehicles of 16 types in Beijing and Zhengzhou, China, were tested using portable emission measurement system under real-world driving conditions. The total carbonyl compound emission factors of gasoline vehicles, diesel vehicles, motorcycles, and agricultural transport vehicles were 24.9 ± 11.4 mg/km, 42.5 ± 21.5 mg/km, 20.4 ± 6.8 mg/km, and 78.3 ± 34.3 mg/km, respectively. Vehicles fueled with E10 ethanol gasoline had significantly higher carbonyl compound emission factors compared to E0 gasoline vehicles. It was observed that the continuous tightening of emission standards has effectively reduced the emissions of carbonyl compounds from on-road vehicles. The carbonyl compound emission factors on highways were 1.3-1.9 times lower than those on general roads. The total carbonyl compound emissions from on-road vehicles in Beijing and Zhengzhou in 2019 were estimated to be 3.5 kt and 3.1 kt, with corresponding ozone formation potentials of 24.4 kt and 21.4 kt, respectively. Formaldehyde, acetaldehyde, propionaldehyde and acetone were the most significant contributors to total carbonyl compound emissions, and among them, formaldehyde, acetaldehyde and propionaldehyde were the main contributors to total ozone formation potential. Our results provide updated and supplementary information on on-road vehicle emission factors for carbonyl compounds and can facilitate the improvement of emission inventories and help in the development of control strategies to improve air quality.

Dynamic low-count PET image reconstruction using spatio-temporal primal dual network.

Objective.Dynamic positron emission tomography (PET) imaging, which can provide information on dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited counts received in individual frame, especially in ultra short frames. Recently, the unrolled model-based deep learning methods have shown inspiring results for low-count PET image reconstruction with good interpretability. Nevertheless, the existing model-based deep learning methods mainly focus on the spatial correlations while ignore the temporal domain.Approach.In this paper, inspired by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network (STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability.Main results.The experiments of both simulation data and real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization, spatio-temporal kernel method, LPD and FBPnet.Significance.Experimental results show STPDnet better reconstruction performance in the low count situation, which makes the proposed method particularly suitable in whole-body dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer from high level of noise.

Ecological risks and innovative-investment projects.

Market competition is becoming fiercer all around the world and countries pay considerable attention to their innovative-investment environment. Rapid global economic development and infinite resource extraction have damaged the environment and the harmful environmental effect has become increasingly significant. Thus, technological innovation occupies an important place in the discussion of developmental issues. Previous studies on innovative projects were focused largely on how technological innovations allow us to prevent financial risks and enter the market. However, it is necessary to pay attention to environmental risks arising from successful technological innovations. Thus, this study focused on the nexus between ecological risks and innovative investment. Specifically, the study considers the environmental risks of innovation. The findings reveal that investment in innovations and environmental protection measures can be carried out simultaneously for both ecological and economic targets. To control and prevent environmental risks of technological innovations, there should be a shift from industrial technological innovations to environmental technological innovations to achieve the unity of economic benefits and environmental interests. Such an approach preserves social and public interests and ensures sustainable development.

Unsupervised PET logan parametric image estimation using conditional deep image prior.

Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient's computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: 62.25%±29.93%; striatum of brain PET datasets : 129.51%±32.13%, thalamus of brain PET datasets: 128.24%±31.18%) than Gaussian filtered results (PET/CT datasets: 23.33%±18.63%; striatum of brain PET datasets: 74.71%±8.71%, thalamus of brain PET datasets: 73.02%±9.34%) and nonlocal mean (NLM) denoised results (PET/CT datasets: 37.55%±26.56%; striatum of brain PET datasets: 100.89%±16.13%, thalamus of brain PET datasets: 103.59%±16.37%).

Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network.

PURPOSE: Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an advanced noninvasive imaging technology that can measure cerebral blood flow (CBF) quantitatively without a contrast agent injection or radiation exposure. However, because of the weak labeling, conventional ASL images usually suffer from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. Therefore, a method that can simultaneously improve the spatial resolution and SNR is needed. METHODS: In this work, we proposed an unsupervised superresolution (SR) method to improve ASL image resolution based on a pyramid of generative adversarial networks (GAN). Through layer-by-layer training, the generators can learn features from the coarsest to the finest. The last layer's generator that contains fine details and textures was used to generate the final SR ASL images. In our proposed framework, the corresponding T1-weighted MR image was supplied as a second-channel input of the generators to provide high-resolution prior information. In addition, a low-pass-filter loss term was included to suppress the noise of the original ASL images. To evaluate the performance of the proposed framework, a simulation study and two real-patient experiments based on the in vivo datasets obtained from three healthy subjects on a 3T MR scanner were conducted, regarding the low-resolution (LR) to normal-resolution (NR) and the NR-to-SR tasks. The proposed method was compared to the nearest neighbor interpolation, trilinear interpolation, third-order B-splines interpolation methods, and deep image prior (DIP) with the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as the quantification metrics. The averaged ASL images acquired with 44 min acquisition time were used as the ground truth for real-patient LR-to-NR study. The ablation studies of low-pass-filter loss term and T1-weighted MR image were performed based on simulation data. RESULTS: For the simulation study, results show that the proposed method achieved significantly higher PSNR ( p $p$ -value < $<$ 0.05) and SSIM ( p $p$ -value < $<$ 0.05) than the nearest neighbor interpolation, trilinear interpolation, third-order B-splines interpolation, and DIP methods. For the real-patient LR-to-NR experiment, results show that the proposed method can generate high-quality SR ASL images with clearer structure boundaries and low noise levels and has the highest mean PSNR and SSIM. For real-patient NR-to-SR tasks, the structure of the results using the proposed method is sharper and clearer, which are the most similar to the structure of the reference 44 min acquisition image than other methods. The proposed method also shows the ability to remove artifacts in the NR image while superresolution. The ablation study verified that the low-pass-filter loss term and T1-weighted MR image are necessary for the proposed method. CONCLUSIONS: The proposed unsupervised multiscale GAN framework can simultaneously improve spatial resolution and reduce image noise. Experiment results from simulation data and three healthy subjects show that the proposed method achieves better performance than the nearest neighbor interpolation, the trilinear interpolation, the third-order B-splines interpolation, and DIP methods.

The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence.

PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-time limit of system components leads to the loss of the count rate; the scattered and random events received by the detector introduce additional noise; the characteristics of the detector limit the spatial resolution; and the low signal-to-noise ratio caused by the scan-time limit (eg, dynamic scans) and dose concern. The early PET reconstruction methods are analytical approaches based on an idealized mathematical model.

Populational and individual information based PET image denoising using conditional unsupervised learning.

Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily applied to existing PET/computed tomography (CT) and PET/magnetic resonance (MR) datasets. This method consists of two steps: populational training and individual fine-tuning. As for populational training, a network was first pre-trained by a group of patients' noisy PET images and the corresponding anatomical prior images from CT or MR. As for individual fine-tuning, a new network with initial parameters inherited from the pre-trained network was fine-tuned by the test patient's noisy PET image and the corresponding anatomical prior image. Only the last few layers were fine-tuned to take advantage of the populational information and the pre-training efforts. Both networks shared the same structure and took the CT or MR images as the network input so that the network output was conditioned on the patient's anatomic prior information. The noisy PET images were used as the training and fine-tuning labels. The proposed method was evaluated on a68Ga-PPRGD2 PET/CT dataset and a18F-FDG PET/MR dataset. For the PET/CT dataset, with the original noisy PET image as the baseline, the proposed method has a significantly higher contrast-to noise ratio (CNR) improvement (71.85% ± 27.05%) than Gaussian (12.66% ± 6.19%,P= 0.002), nonlocal mean method (22.60% ± 13.11%,P= 0.002) and conditional deep image prior method (52.94% ± 21.79%,P= 0.0039). For the PET/MR dataset, compared to Gaussian (18.73% ± 9.98%,P< 0.0001), NLM (26.01% ± 19.40%,P< 0.0001) and CDIP (47.48% ± 25.36%,P< 0.0001), the CNR improvement ratio of the proposed method (58.07% ± 28.45%) is the highest. In addition, the denoised images using both datasets also showed that the proposed method can accurately restore tumor structures while also smoothing out the noise.

Seasonal discrepancies in peroxyacetyl nitrate (PAN) and its correlation with ozone and PM2.5: Effects of regional transport from circumjacent industrial cities.

Peroxyacetyl nitrate (PAN) is the most important reservoir of nitrogen oxides, with effects on atmospheric oxidation capacity and regional nitrogen distribution. The first yearlong observational study of PAN was conducted from September 2018 to August 2019 at a suburban site and an urban site in Zhengzhou, Henan Province, central China. Compared with studies over the past two decades, summer PAN pollution at the suburban site and winter PAN pollution at both sites were more significant, with annual average concentrations of 1.96 ± 1.44 and 2.01 ± 1.59 ppbv, respectively. Seasonal PAN discrepancies between the urban and suburban areas were analyzed in detail. Active PAN formation, regional transport, photochemical precursors, and PAN lifetime played key roles during seasons with elevated PAN (winter and spring). According to the results of cluster analysis and potential source contribution function analysis, during the cold months, short-distance air mass transport from the east, south, and southeast of Henan Province and southern Hebei Province increased PAN pollution in urban Zhengzhou. PAN source areas were located in circumjacent industrial cities surrounding Zhengzhou except in the northeastern direction. Based on the relationships between pollutant concentrations, wind speed, and wind direction, a strong positive correlation between PAN and PM2.5 (and O3) existed in winter due to their joint transport. A slow-moving, low-height air mass passed through surrounding industrial cities before reaching the study area, carrying both pollutants and leading to strong consistency between PAN and O3 levels. The long-term PAN characteristics described in this study will help clarify the causes of regional air pollution in inland city agglomerations. Moreover, the PAN correlations and joint transport of PAN and PM2.5 (or O3) support the use of PAN as an indicator of air pollution introduced from surrounding industrial areas.

Gas-particle partitioning of carbonyls and its influencing factors in the urban atmosphere of Zhengzhou, China.

Despite their profound roles in atmospheric chemistry and health concerns, the gas-particle partitioning of carbonyl compounds and its influencing factors in the ambient atmosphere are poorly elucidated. In this work, a reliable method using a denuder/filter-pack system coated with the derivative reagent, O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) was developed for the simultaneous collection of gaseous and particulate carbonyls. Sampling campaigns were performed at an urban site in Zhengzhou, China. The average field-derived partitioning coefficients (Kpf) of the six most abundant carbonyls (formaldehyde, acetaldehyde, acetone, propionaldehyde, glyoxal, and methylglyoxal) were in the range of 10-5-10-4 m3·µg-1, and their effective Henry's law coefficients (eff. KH) ranged from 107 to 109 M·atm-1. Comparisons revealed that their Kpf and eff. KH were 104-106 times and 102-107 times higher than theoretically predicted, respectively. Given that the aerosol liquid water is a concentrated salt solution, these six carbonyls very clearly salted in to three atmospherically relevant aqueous salts, following the order of sulfate > ammonium > nitrate. However, even taking salting-in effects into account, the Pankow's absorptive partitioning theory and effective Henry's law both failed to explain the unexpected highly particulate carbonyls. In regard to the influencing factors, the negative correlations between Kpf and temperature indicate that lower temperature is conducive to carbonyls partitioning. As for the strong relative humidity (RH) dependence of KPf, high partitioning coefficients were observed under low and high RH conditions. Partitioning is considered to be dominated by the carbonyl-oligomer formation when RH increases from <10% to 50%, and driven by the abundant aerosol liquid water content when RH exceeds 50%. The presence of particulate inorganic components and the transition of particle phase state may also impact the partitioning process, especially in the urban atmosphere.

Fluorochemicals biodegradation as a potential source of trifluoroacetic acid (TFA) to the environment.

The anthropogenic release of trifluoroacetic acid (TFA) into the environmental media is not limited to photochemical oxidation of CFC alternatives and industrial emissions. Biological degradation of some fluorochemicals is expected to be a potential TFA source. For the first time, we assess if the potential precursors [6:2 fluorotelomer alcohol (6:2 FTOH), 4:2 fluorotelomer alcohol (4:2 FTOH), acrinathrin, trifluralin, and 2-(trifluoromethyl)acrylic acid (TFMAA)] can be biologically degraded to TFA. Results show that 6:2 FTOH was terminally transformed to 5:3 polyfluorinated acid (5:3 FTCA; 12.5 mol%), perfluorohexanoic acid (PFHxA; 2.0 mol%), perfluoropentanoic acid (PFPeA; 1.6 mol%), perfluorobutyric acid (PFBA; 1.7 mol%), and TFA (2.3 mol%) by day 32 in the landfill soil microbial culture system. 4:2 FTOH could remove multiple -CF2 groups by microorganisms and produce PFPeA (2.6 mol%), PFBA (17.4 mol%), TFA (7.8 mol%). We also quantified the degradation products of TFMAA as PFBA (1.3 mol%) and TFA (6.3 mol%). Furthermore, we basically analyzed the biodegradation contribution of short-chain FTOH as raw material residuals in commercial products to the TFA burden in the environmental media. We estimate global emission of 3.9-47.3 tonnes of TFA in the period from 1961 to 2019, and project 3.8-46.4 tonnes to be emitted from 2020 to 2040 via the pathway of 4:2 and 6:2 FTOH biodegradation (0.6-7.1 and 0.6-7.0 tonnes in China, respectively). Direct evidence of the experiments indicates that biodegradation of fluorochemicals is an overlooked source of TFA and there are still some unspecified mechanisms of TFA production pathways.

Methylsiloxanes in plasma from potentially exposed populations and an assessment of the associated inhalation exposure risk.

Methylsiloxanes (MSs) are ubiquitous in indoor air and pose an important health risk. Thus, assessments of indoor inhalation exposure by measuring MSs levels in plasma are needed. In this study, we measured plasma MSs concentrations and evaluated daily indoor inhalation exposure in potentially exposed populations, including residents of industrial areas, university campus, and residential areas, all located in southwestern China. The concentrations of MSs in indoor air (gas-phase and PM2.5) collected from factory housing and from girls' dormitories on university campus were approximately one to three orders of magnitude higher than in parallel samples from other areas. The consequences of MSs exposure were investigated by measuring MSs levels in the plasma samples of the exposed populations. Relatively high levels of cyclic MSs (CMSs: D4-D6) were found in the plasma of the co-resident family members of factory workers and in female college students living in campus dormitories. The highest levels of CMSs (D4-D6) and linear MSs (L5-L16), 2.3 × 102 and 2.0 × 102 ng/mL, respectively, were detected in the very young (0-3 years old) co-resident children of factory workers. The average daily dose via inhalation (ADDinh) in different groups showed that the ADDinh values of all MSs (D4-D6, L5-L16) were one to two orders of magnitude higher in the co-resident family members of factory workers and in female college students than in other groups, indicating that both populations should be considered as potentially highly exposed to MSs. A further assessment showed that inhalation exposure is the main source of CMSs (D4-D6) in plasma for people exposed to high indoor air levels of these compounds. Although the health risk assessment showed that the health risk from inhalation exposure to D4 and D5 was acceptable for all of the studied groups based on the current chronic reference dose (cRfD), the maximum ADDinh,CMSs value in 0- to 3-year-old children was only 7.9-fold below the cRfD. Because the toxicity of other MSs is unknown, the potential health risk of MSs to very young children via inhalation exposure should be further analysed.

Distribution characteristics of trifluoroacetic acid in the environments surrounding fluorochemical production plants in Jinan, China.

Trifluoroacetic acid (TFA) is a ubiquitous and extremely stable contaminant in the ambient environment and may be discharged during fluorochemical production processes. However, the impacts of fluorochemical production on surrounding areas have seldom been evaluated. We focused on Jinan, the capital of Shandong Province, China, and measured TFA levels in water, soil, and air samples. Our results showed that the average TFA concentrations in flowing water bodies were lower than those in landscape water bodies. The average TFA concentrations in soils were significantly higher than the background concentration. As for atmospheric TFA levels, the mean concentrations in the gas phase were higher than those in the particle phase, and average daytime levels were slightly higher than nighttime levels. In addition, the quotient method was used to assess the ecological risk of TFA in water in Jinan. The ratio of pollutant environmental concentration to predicted no-effect concentration (PEC/PNEC) for TFA was greater than 1, indicating that TFA does potentially damage the aquatic ecosystem of Jinan. Our findings suggest that TFA pollution around fluoride production plants is a serious problem and that actions are required to avoid exacerbating the local ecological and environmental risks of TFA.

PET image denoising using unsupervised deep learning.

PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsupervised deep learning, where no training pairs are needed. METHODS: In this method, the prior high-quality image from the patient was employed as the network input and the noisy PET image itself was treated as the training label. Constrained by the network structure and the prior image input, the network was trained to learn the intrinsic structure information from the noisy image and output a restored PET image. To validate the performance of the proposed method, a computer simulation study based on the BrainWeb phantom was first performed. A 68Ga-PRGD2 PET/CT dataset containing 10 patients and a 18F-FDG PET/MR dataset containing 30 patients were later on used for clinical data evaluation. The Gaussian, non-local mean (NLM) using CT/MR image as priors, BM4D, and Deep Decoder methods were included as reference methods. The contrast-to-noise ratio (CNR) improvements were used to rank different methods based on Wilcoxon signed-rank test. RESULTS: For the simulation study, contrast recovery coefficient (CRC) vs. standard deviation (STD) curves showed that the proposed method achieved the best performance regarding the bias-variance tradeoff. For the clinical PET/CT dataset, the proposed method achieved the highest CNR improvement ratio (53.35% ± 21.78%), compared with the Gaussian (12.64% ± 6.15%, P = 0.002), NLM guided by CT (24.35% ± 16.30%, P = 0.002), BM4D (38.31% ± 20.26%, P = 0.002), and Deep Decoder (41.67% ± 22.28%, P = 0.002) methods. For the clinical PET/MR dataset, the CNR improvement ratio of the proposed method achieved 46.80% ± 25.23%, higher than the Gaussian (18.16% ± 10.02%, P < 0.0001), NLM guided by MR (25.36% ± 19.48%, P < 0.0001), BM4D (37.02% ± 21.38%, P < 0.0001), and Deep Decoder (30.03% ± 20.64%, P < 0.0001) methods. Restored images for all the datasets demonstrate that the proposed method can effectively smooth out the noise while recovering image details. CONCLUSION: The proposed unsupervised deep learning framework provides excellent image restoration effects, outperforming the Gaussian, NLM methods, BM4D, and Deep Decoder methods.

Rapid in situ microwave synthesis of Fe3O4@MIL-100(Fe) for aqueous diclofenac sodium removal through integrated adsorption and photodegradation.

Metal-Organic Frameworks (MOFs) are efficient adsorbent and catalyst, however, the prepare of MOFs can be extremely time consuming. The rapid in situ microwave synthesis process offers the possibility of MOFs to a large-scale application. In this study, Fe3O4@MIL-100(Fe) was rapidly prepared via microwave in 30 min using Fe3O4 as metal precursor and applied as the adsorbent and photocatalyst to remove diclofenac sodium (DCF) from water. Fe3O4@MIL-100(Fe) exhibited an excellent adsorption effect to DCF with the maximum adsorption capacities of 400 mg/L. The presence of H2O2 could promote the removal of DCF during photocatalytic process. Approximately 99.4% of the DCF was removed in Fe3O4@MIL-100(Fe)/vis/H2O2 system via adsorption removal and consequent photocatalytic degradation. The high efficiency was attributed to the large BET surface area (1244.62 m2/g) and abundant iron metal sites (Fe(III) and Fe(II)) of Fe3O4@MIL-100(Fe). The adsorptive, photocatalytic property of Fe3O4@MIL-100(Fe) and the Fenton-like reaction were the main mechanisms for DCF removal. TOC analyzer was served to assess the mineralization of solutions treated by Fe3O4@MIL-100(Fe)/vis/H2O2 in 12 h. High elimination of TOC (87.8%) was observed during the DCF mineralization process. In addition, the major products were illuminated using HPLC-Q-TOF-MS and DCF degradation pathways were also proposed.

Assessment of volatile methylsiloxanes in environmental matrices and human plasma.

Volatile methylsiloxanes (VMSs) are widely used in various personal-care products and industrial additives and products. This study focused on VMSs exposure in the general population, workers, and the families of workers living in residential and industrial areas of southwestern China. VMSs concentrations in indoor environmental matrices from six industrial facilities were 3.4 × 102 to 9.0 × 102 µg m-3 in gas-phase samples, 4.7 × 102 to 1.5 × 104 µg g-1 in PM2.5 samples, and 2.3 × 102 to 7.2 × 103 µg g-1 in dust samples, which were two to four orders of magnitude higher than the concentrations measured in residential areas. Exposure to VMSs was investigated by analysis of plasma samples from workers in residential and industrial areas for the presence of cyclic (D4-D6) and linear (L3-L16) VMSs. VMSs concentrations in plasma samples ranged from 84 to 2.3 × 102 ng ml-1 in workers, one to two orders of magnitude higher than those in the general population (2.2 ng ml-1). Daily VMSs indoor exposure via inhalation and ingestion in individuals from residential and industrial areas were estimated and assessed under working-time and leisure-time conditions. This study showed that exposure to VMSs in industrial areas is approximately two to four or one to two orders of magnitude higher than that in residential areas during the working- or leisure-time scenario, respectively. Furthermore, the families of workers (the non-occupational group) experienced higher levels of exposure to VMSs in their homes compared with the general population. The ratios of exposure to linear VMSs via PM2.5 inhalation to that via the gas phase ranged from 7.8% to 43.1% in industrial areas. This study suggests that intake of linear VMSs via PM2.5 inhalation should be considered when estimating human exposure to VMSs in areas with high levels of PM2.5 air pollution.

The contribution of fluoropolymer thermolysis to trifluoroacetic acid (TFA) in environmental media.

The source of trifluoroacetic acid (TFA) has long been a controversial issue. Fluoropolymer thermolysis is expected to be a potential anthropogenic source except for CFC alternatives. However, its TFA yield and contributions have rarely been reported more recently. In this study, we investigated the thermal properties of three kinds of fluoropolymers, including poly (vinylidene fluoride-co-hexafluropropylene) (PVDF-HFP), poly (vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) and poly (tetrafluoroethylene) (PTFE). A laboratory simulation experiment was then performed to analyze the TFA levels in the thermolysis products and hence to examine the TFA yields of these fluoropolymers. Thermolysis of these fluoropolymers occurred in the temperature ranges from ∼400 °C to ∼650 °C, with the peak weight loss rate at around 550-600 °C. TFA could be produced through fluoropolymer thermolysis when being heated to 500 °C and above. Average TFA yields of PTFE, PVDF-HFP and PVDF-CTFE were 1.2%, 0.9% and 0.3%, respectively. Furthermore, the contribution of fluoropolymer thermolysis and CFC alternatives to rainwater TFA in Beijing, China was evaluated by using a Two-Box model. The degradation of fluoropolymers and HCFCs/HFCs could explain 37.9-43.4 ng L-1 rainwater TFA in Beijing in 2014. The thermolysis of fluoropolymers contributed 0.6-6.1 ng L-1 of rainwater TFA, accounting for 1.6-14.0% of the TFA burden from all the precursors which were considered here.

Simultaneous estimation and segmentation from projection data in dynamic PET.

PURPOSE: Dynamic positron emission tomography (PET) is known for its ability to extract spatiotemporal information of a radio tracer in living tissue. Information of different functional regions based on an accurate reconstruction of the activity images and kinetic parametric images has been widely studied and can be useful in research and clinical setting for diagnosis and other quantitative tasks. In this paper, our purpose is to present a novel framework for estimating the kinetic parametric images directly from the raw measurement data together with a simultaneous segmentation accomplished through kinetic parameters clustering. METHOD: An iterative framework is proposed to estimate the kinetic parameter image, activity map and do the segmentation simultaneously from the complete dynamic PET projection data. The clustering process is applied to the kinetic parameter variable rather than to the traditional activity distribution so as to achieve accurate discrimination between different functional areas. Prior information such as total variation regularization is incorporated to reduce the noise in the PET images and a sparseness constraint is integrated to guarantee the solution for kinetic parameters due to the over complete dictionary. Alternating direction method of multipliers (ADMM) method is used to solve the optimization problem. The proposed algorithm was validated with experiments on Monte Carlo-simulated phantoms and real patient data. Symbol error rate (SER) was defined to evaluate the performance of clustering. Bias and variance of the reconstruction activity images were calculated based on ground truth. Relative mean square error (MSE) was used to evaluate parametric results quantitatively. RESULT: In brain phantom experiment, when counting rate is 1 × 106 , the bias (variance) of our method is 0.1270 (0.0281), which is lower than maximum likelihood expectation maximization (MLEM) 0.1637 (0.0410) and direct estimation without segmentation (DE) 0.1511 (0.0326). In the Zubal phantom experiment, our method has the lowest bias (variance) 0.1559 (0.0354) with 1 × 105 counting rate, compared with DE 0.1820 (0.0435) and MLEM 0.3043 (0.0644). As for classification, the SER of our method is 18.87% which is the lowest among MLEM + k-means, DE + k-means, and kinetic spectral clustering (KSC). Brain data with MR reference and real patient results also show that the proposed method can get images with clear structure by visual inspection. CONCLUSION: In this paper, we presented a joint reconstruction framework for simultaneously estimating the activity distribution, parametric images, and parameter-based segmentation of the ROIs into different functional areas. Total variation regularization is performed on the activity distribution domain to suppress noise and preserve the edges between ROIs. An over complete dictionary for time activity curve basis is constructed. SER, bias, variance, and MSE were calculated to show the effectiveness of the proposed method.

Fabrication and characterization of metal organic frameworks/ polyvinyl alcohol cryogel and their application in extraction of non-steroidal anti-inflammatory drugs in water samples.

A series of novel MOFs/PVA composite cryogel (MIL-101(Cr)/PVA, MIL-100(Fe)/PVA, ZIF-8(Zn)/PVA, MOF-199(Cu)/PVA and MIL-53(Al)/PVA) were fabricated by using a facile and green freeze-thaw approach for the first time. MIL-101(Cr)/PVA cryogel was selected as a VA-SPE sorbent for extraction of four NSAIDs in environmental water samples. The procedures of condition investigation (synthesis and extraction optimization) and characterization were also performed. And a satisfactory result of methodology validation was obtained by making use of HPLC-MS/MS. Under the optimum conditions, good sensitivity levels were achieved with the limits of detection between 0.007 and 0.037 µg L-1, a linearity of 0.10-10 µg L-1 for phenylbutazone, indomethacin, nimesulide and 0.020-2.0 µg L-1 for benorilate (r2 ≥ 0.9934). The relative recoveries of the target analytes were in the range from 78.44% to 105.7% with relative standard deviation (RSD) from 1.33% to 9.85%. In the extraction process, MIL-101(Cr)/PVA cryogel as a whole sheet outperformed the pristine dispersive MIL-101(Cr) in separation from solvent, and the application of cryogel also simplified the operation procedure. Additionally, the combination of PVA with MOFs might strengthen the interaction ability between the sorbent and analytes. This novel pretreatment method had a variety of merits, such as easy operation, high enrichment efficiency and low matrix effect. It looks forward to further optimization or functionalization and application of these MOFs/PVA cryogel in various disciplines.

Deep reconstruction model for dynamic PET images.

Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the activity distribution-based on individual frames, may lead to inaccurate results due to the checkerboard effect and limitation of photon counts. In this paper, we propose a stacked sparse auto-encoder based reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a deep learning representation, where the encoding layers extract the prototype features, such as edges, so that, in the decoding layers, the reconstructed results are obtained through a combination of those features. The qualitative and quantitative results of the procedure, including the data based on a Monte Carlo simulation and real patient data demonstrates the effectiveness of our method.