Abstract: In this study, a series of BUC-21/titanate nanotube (BT-X) composites were facilely fabricated via ball-milling of 2-dimensional (2D) metal-organic framework (MOF) BUC-21 and titanate nanotubes (TNTs). The BT-X composites were characterized by powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), UV–visible diffuse-reflectance spectroscopy (UV–vis DRS), X-ray photoelectron spectrometer (XPS) and high resolution transmission electron microscopy (HRTEM). Both the photocatalytic reduction from Cr(VI) to Cr(III) and adsorptive removal of formed Cr(III) of BT-X composites were systematically investigated under different conditions including pH values and co-existing inorganic ions. It was found that BUC-21 (100 mg)/TNTs (100 mg) (BT-1) composites demonstrate remarkable ability of photocatalytic Cr(VI) reduction and adsorptive Cr(III) removal, as well as good reusability and stability. It is believed that the introduction of TNTs could capture the formed Cr(III) from the surface of BUC-21, which provided more active sites exposed to enhance the Cr(VI) reduction.
Nowadays, waste water treatment plants (WWTPs) are regarded as the pollution sources of nitrogen and pharmaceutical and personal care products (PPCPs). In the present study, the simultaneous removal of nitrogen and typical PPCPs, ibuprofen and triclosan, was evaluated in a poly-3-hydroxybutyrate-co-3- hydroxyvalerate (PHBV) based solid-phase denitrification (SPD) system. Results after 602 days showed that simultaneous nitrification and denitrification (SND) process occurred with average 83.85 +/- 13.09% NH4 (+) -N and 93.88 +/- 10.19% NO3- -N removals in the SPD system. Interestingly, the system achieved average 79.69 +/- 6.35% and 65.96 +/- 7.62% removals of ibuprofen and triclosan, respectively, under stable influent conditions of 50 mu g L-1. Cometabolic activities of heterotrophic denitrifying bacteria and ammonia oxidizing bacteria (AOB) probably played a role in the biodegradation of the two PPCPs. Illumina MiSeq sequencing results revealed that microbial composition enhanced the simultaneous removal of nitrogen and PPCPs in the SPD system.
The number and mass size distributions of refractory black carbon (rBC) cores in particles emitted from a diesel vehicle were investigated as a function of particle mobility diameter (d(mob)) using a single particle soot photometer (SP2) and a differential mobility analyzer (DMA). The thickness and mass of coatings on the rBC cores were characterized. On the basis of the SP2 and DMA results, the physical properties of particles containing rBC, including effective density (rho(eff)), mass-mobility scaling exponent (D-m), dynamic shape factor (chi), and mass absorption cross section (MAC), were derived as a function of d(mob). At each d(mob), the count median diameter (CMD) of the rBC cores was essentially the same as their mass median diameter (MMD), which increased linearly with d(mob). The mass of the rBC cores was proportional to the cubic of their d(mob). However, coating thickness on rBC cores remained unchanged with d(mob), with an average thickness of 28.72 +/- 4.81 nm. For particles containing rBC, rho(eff) decreased and chi increased with d(mob). The D-m of particles containing rBC was calculated to be 2.09. At 355 and 532 nm wavelengths, the MAC of the diesel particles containing rBC was inversely dependent on d(mob).
Sensors with multifunctions have attracted great attention for their extensive application value, among which humidity sensing and pressure sensing are necessary to electronics undoubtedly because of the complex physical environment we live in. Inspired by the structure of skin, in this article, we design a new method to combine wrinkle structure with porous sponge structure and achieve a novel, flexible, compressible, and bifunctional sensor based on carbon nanotube–polydimethylsiloxane (CNT–PDMS) with functions of humidity sensing and pressure sensing. The performance of the humidity sensing part can be controlled by the ultraviolet and ozone (UVO) treatment time and CNT concentration, while the sensitivity of the pressure sensing part can be controlled by the CNT concentration and grinding time of sugar granules. The bifunctional sensorcan easily sense approaching and touching of a hand, which shows great potential of alarming and protecting some electronics. Moreover, the bifunctional sensor can also be used in detecting human joint motions and breath conditions as a wearable and flexible health monitor.
DNA methylation may play a critical role in aging and age-related diseases. DNA methylation phenotypic age (DNAmPhenoAge) is a new aging biomarker and predictor of chronic disease risk. While smoking is a strong risk factor for chronic diseases and influences methylation, its influence on DNAmPhenoAge is unknown. We investigated associations of self-reported and epigenetic smoking indicators with DNAmPhenoAge acceleration in a longitudinal aging study in eastern Massachusetts. DNA methylation was measured in whole blood samples from multiple visits for 692 male participants in the Veterans Affairs Normative Aging Study during 1999-2013. Acceleration was defined using residuals from linear regression of the DNAmPhenoAge on the chronological age. Cumulative smoking (pack-years) was significantly associated with DNAmPhenoAge acceleration, whereas self-reported smoking status was not. We observed significant validated associations between smoking-related loci and DNAmPhenoAge acceleration for 52 CpG sites, where 18 were hypomethylated and 34 were hypermethylated, mapped to 16 genes. The AHRR gene had the most loci (N = 8) among the 16 genes. We generated a smoking aging index based on these 52 loci, which showed positive significant associations with DNAmPhenoAge acceleration. These epigenetic biomarkers may help to predict age-related risks driven by smoking.
Abstract Semivolatile organic compounds (SVOCs) emitted from building materials, consumer products, and occupant activities alter the composition of air in residences where people spend most of their time. Exposures to specific SVOCs potentially pose risks to human health. However, little is known about the chemical complexity, total burden, and dynamic behavior of SVOCs in residential environments. Furthermore, little is known about the influence of human occupancy on the emissions and fates of SVOCs in residential air. Here, we present the first-ever hourly measurements of airborne SVOCs in a residence during normal occupancy. We employ state-of-the-art semivolatile thermal-desorption aerosol gas chromatography (SV-TAG). Indoor air is shown consistently to contain much higher levels of SVOCs than outdoors, in terms of both abundance and chemical complexity. Time-series data are characterized by temperature-dependent elevated background levels for a broad suite of chemicals, underlining the importance of continuous emissions from static indoor sources. Substantial increases in SVOC concentrations were associated with episodic occupant activities, especially cooking and cleaning. The number of occupants within the residence showed little influence on the total airborne SVOC concentration. Enhanced ventilation was effective in reducing SVOCs in indoor air, but only temporarily; SVOCs recovered to previous levels within hours.
The traditional multiple audio objects codec extracts the parameters of each object in the frequency domain and produces serious confusion because of high coincidence degree in subband among objects. This paper uses sparse domain instead of frequency domain and reconstruct audio object using the binary mask from the down-mixed signal based on the sparsity of each audio object. In order to overcome high coincidence degree of subband among different audio objects, the sparse autoencoder neural network is established. On this basis, a multiple audio objects codec system is built up. To evaluate this proposed system, the objective and subjective evaluation are carried on and the results show that the proposed system has the better performance than SAOC.
Coke production is a significant source of ambient volatile organic compound emissions; thus, stringent control measures must be applied. We fully characterized the trends in volatile organic compound emissions by the coking industry in China between 1949 and 2016 based on a factory-based database and process-specific emission factors. We then projected the reduction potentials in these emissions if different control policies were implemented in 2020 based on three emission scenarios. The results indicate that: (1) the emission factor of volatile organic compounds for coke plants under uncontrolled conditions was 3.065 g/kg coke, and benzene, toluene, and acetone were the most abundant emission species. (2) The annual volatile organic compound emissions from the coking industry increased by an order of magnitude from 3.38 Gg in 1949 to 1376.54 Gg in 2016. The emissions show distinct spatial characteristics, with significantly higher emissions in northern China than in other areas. (3) Compared to the uncontrolled scenario, if basic or more stringent control measures were fully implemented in China in 2020, then volatile organic compound emissions would be reduced by 59% or 82%, respectively. (4) Controlling coke oven flue gases through efficient combustion, sealing and cleaning the openings of coke ovens, and using gas blanketing or carbon absorbers in by-product facilities were the most effective technologies for controlling volatile organic compound emissions from coke production.
Ambient exposure to fine particulate matter (PM2.5) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM2.5 concentrations nationwide, while the lack of surface monitoring data before 2013 have created difficulties in historical PM2.5 exposure estimates. Hindcast approaches using statistical models or chemical transport models (CTMs) were developed to overcome this limitation, while those approaches still suffer from incomplete daily coverage due to missing AOD data or limited accuracy due to uncertainties of CTMs. Here we developed a new machine learning (ML) model with high-dimensional expansion (HD-expansion) of numerous predictors (including AOD and other satellite covariates, meteorological variables and CTM simulations). Through comprehensive characterization of the nonlinear effects of, and interactions among different predictors, the HD-expansion parameterized the association between PM2.5 and AOD as a nonlinear function of space and time covariates (e.g., planetary boundary layer height and relative humidity). In this way, the PM2.5-AOD association can vary spatiotemporally. We trained the model with data from 2013 to 2016 and evaluated its performance using annually-iterated cross-validation, which iteratively held out the in-situ observations for a whole calendar year (as testing data) to examine the predictions from a model trained by the rest of the observations. Our estimates were found to be in good agreement with in-situ observations, with correlation coefficients (R2) of 0.61, 0.68, and 0.75 for daily, monthly and annual averages, respectively. To interpolate the missing predictions due to incomplete AOD data, we incorporated a generalized additive model into the ML model. The two-stage estimates of PM2.5 sacrificed the prediction accuracy on a daily timescale (R2 = 0.55), but achieved complete spatiotemporal coverage and improved the accuracy of monthly (R2 = 0.71) and annual (R2 = 0.77) averages. The model was then used to predict daily PM2.5 concentrations during 2000–2016 across China and estimate long-term trends in PM2.5 for the period. We found that population-weighted concentrations of PM2.5 significantly increased, by 2.10 (95% confidence interval (CI): 1.74, 2.46) μg/m3/year during 2000–2007, and rapidly decreased by 4.51 (3.12, 5.90) μg/m3/year during 2013–2016. In this study, we produced AOD-based estimates of historical PM2.5 with complete spatiotemporal coverage, which were evidenced as accurate, particularly in middle and long term. The products could support large-scale epidemiological studies and risk assessments of ambient PM2.5 in China and can be accessed via the website (http://www.meicmodel.org/dataset-phd.html).
Innovation contributes to the long-term economic growth. From the perspective of externality by innovation, this paper disentangles the spillover effect based on the regions’ abundance of innovation resource and separately identifies the “leader effect” and “peer effect” of innovation spillover and discusses their economic consequences. Empirical results demonstrate a negative spillover effect from innovation leaders on the economic growth and a positive spillover effect from innovation peers. Robustness checks also support main findings. This study has implication both in the endogenous economic growth theory and industry innovation practice.
Interfacial modification as a feasible strategy to improve the properties of perovskite solar cells (PSCs) has been widely studied in recent years. In this work, PbCl2 modification at the interface of SnO2/CH3NH3PbI3 was used for CH3NH3PbI3-based planar PSCs. The introduction of PbCl2 between SnO2 and CH3NH3PbI3 layers boosted the crystallization of perovskite film, which promoted the photovoltaic properties of corresponding PSCs. The open circuit voltage (VOC) and fill factor (FF) of the PbCl2-modified devices (1 mg/mL) were 1.11 V and 0.79, respectively, which were both higher than those of the reference devices without PbCl2 (1.04 V and 0.76). Moreover, a steady-state power output efficiency exceeding 19% was obtained for the PbCl2-modified devices (1 mg/mL), which implied that PbCl2 modification was an effective and low cost strategy for efficient PSCs.