Molecular Insights into the Dual Promotion-Inhibition Effects of NaCl at Various Concentrations on the CO2 Hydrate Growth: A Molecular Simulation Study.

Hydrate-based CO2 storage in the ocean is considered a potential method for mitigating the greenhouse effect. Numerous studies demonstrated that NaCl exhibited the dual effects of promotion and inhibition in the nucleation and growth processes of CO2 hydrate, whose mechanisms remain unclear. In this study, the effects of NaCl at various concentrations on the CO2 hydrate growth and crystal are investigated. The independent gradient model based on Hirshfeld partition, electrostatic potential, and binding energy is conducted to study the interaction between ions and water molecules. The motion trajectories of ions are observed at the molecular level to reflect the impact of ion motion on hydrate growth. The results show that the influence of NaCl on hydrate growth depends on a delicate balance of dual promotion-inhibition effects. NaCl can combine more water molecules and provide a transport channel of CO2 to promote hydrate growth at low concentrations. Meanwhile, the promoting effects shift toward inhibition with increasing NaCl concentrations. In a word, this paper proposes a novel mechanism for the dual promotion-inhibition effects of NaCl on hydrate growth, which is significant for further research on hydrate-based CO2 storage in the ocean.

Visible-Light-Driven Inactivation of Bacteria and H2 Generation Catalyzed by Oxygen-Vacancy-Rich One-Dimensional/Two-Dimensional W18O49/g-C3N4 Z-Scheme Heterostructures.

Z-scheme heterostructure-based photocatalysts consist of a reduction photocatalyst and an oxidation photocatalyst, enabling them to possess a high capacity for both reduction and oxidation. However, the coupling reaction between photocatalytic H2 generation through water reduction and sterilization using Z-scheme systems has been rarely reported. Herein, 1D W18O49 nanowires embedded over 2D g-C3N4 nanosheets are well-constructed as an integrated Z-scheme heterojunction. Experimental results and density functional theory calculations not only demonstrate the achievement of efficient interfacial charge separation and transport, leading to prolonged lifetime of photogenerated charge carriers, but also directly confirm the mechanism of Z-scheme charge transfer. As expected, the optimized W18O49/g-C3N4 nanostructure exhibits superior photocatalytic sterilization activity against Staphylococcus aureus as well as excellent H2 generation performance under visible-light irradiation (λ ≥ 420 nm). Due to its nontoxic nature, W18O49/g-C3N4 holds great potential in eradicating bacterial infections in living organisms.

Magnetic-responsive upconversion luminescence resonance energy transfer (LRET) biosensor for ultrasensitive detection of SARS-CoV-2 spike protein.

Upconversion nanoparticles (UCNPs) are ideal donors for luminescence resonance energy transfer (LRET)-based biosensors due to their excellent upconversion luminescence properties. However, the relatively large size of antibodies and proteins limits the application of UCNPs-based LRET biosensors in protein detection because the large steric hindrance of proteins leads to low energy transfer efficiency between UCNPs and receptors. Herein, we developed a magnetic responsive UCNPs-based LRET biosensor to control the coupling distance between antibody-functionalized UCNPs (Ab-UCNPs) as donors and antibody-PEG linker-magnetic gold nanoparticles (Ab-PEG-MGNs) as acceptors for ultrasensitive and highly selective detection of SARS-CoV-2 spike proteins. Our results showed that this platform reversibly shortened the coupling distance between UCNPs and MGNs and enhanced the LRET signal with a 10-fold increase in the limit of detection (LOD) from 20.6 pg/mL without magnetic modulation to 2.1 pg/mL with magnetic modulation within 1 h. The finite-difference time-domain (FDTD) simulation with cyclic distance change confirmed the distance-dependent LRET efficiency under magnetic modulation, which supported the experimental results. Moreover, the applications of this magnetic-responsive UCNP-based LRET biosensor could be extended to other large-size biomolecule detection.

Nanomanipulation of Ligand Nanogeometry Modulates Integrin/Clathrin-Mediated Adhesion and Endocytosis of Stem Cells.

Nanosubstrate engineering can be a biomechanical approach for modulating stem cell differentiation in tissue engineering. However, the study of the effect of clathrin-mediated processes on manipulating this behavior is unexplored. Herein, we develop integrin-binding nanosubstrates with confined nanogeometries that regulate clathrin-mediated adhesion- or endocytosis-active signaling pathways for modulating stem fates. Isotropically presenting ligands on the nanoscale enhances the expression of clathrin in cells, thereby facilitating uptake of dexamethasone-loaded nanoparticles (NPs) to boost osteogenesis of stem cells. In contrast, anisotropic ligand nanogeometry suppresses this clathrin-mediated NP entry by strengthening the association between clathrin and adhesion spots to reinforce mechanotransduced signaling, which can be abrogated by the pharmacological inhibition of clathrin. Meanwhile, inhibiting focal adhesion formation hinders cell spreading and enables a higher endocytosis efficiency. Our findings reveal the crucial roles of clathrin in both endocytosis and mechanotransduction of stem cells and provide the parameter of ligand nanogeometry for the rational design of biomaterials for tissue engineering.

Fast adaptation of multi-task meta-learning for optical performance monitoring.

An algorithm is proposed for few-shot-learning (FSL) jointing modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. The constellation diagrams of six widely-used modulation formats over a wide range of OSNR (10-40 dB) are obtained by a dual-polarization (DP) coherent detection system at 32 GBaud. We introduce auxiliary task to model-agnostic meta-learning (MAML) which makes the gradient of meta tasks decline faster in the direction of optimal target. Ablation experiments including multi-task model-agnostic meta-learning (MT-MAML), single-task model-agnostic meta-learning (ST-MAML) and adaptive multi-task learning (AMTL) are executed to train a data set with only 20 examples for each class. First, we discuss the impact from the number of shots and gradient descent steps for support set on the meta-learning based schemes to determine the best hyper parameters and conclude that the proposed method better captures the similarity between new and previous knowledge at 4 shot and 1 step. Withdrawn fine-tuning, the model achieves the lowest error ∼0.37 dB initially. Then, we simulate two other schemes (AMTL and ST-MAML), and the numerical results shows that mean square error (MSE) are ∼0.6 dB, ∼0.3 dB and ∼0.18 dB, respectively, proposed method has faster adaption to main task. For low order modulation formats, the proposed method almost reduces the error to 0. Meanwhile, we reveal the degree of deviation between the prediction and target and find that the deviation is mainly concentrated in the high OSNR range of 25-40 dB. Specifically, we investigate the variation curve of adaptive weights during pretraining and conclude that after 30 epoch, the model's attention was almost entirely focused on estimating OSNR. In addition, we study the generalization ability of the model by varying the transmission distance. Importantly, excellent generalization is also experimentally verified. In this paper, the method proposed will greatly reduce the cost for repetitively collecting data and the training resources required for fine-tuning models when OPM devices need to be deployed at massive nodes in dynamic optical networks.

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.

Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab's effective depth (SED), span-depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab's effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method.

Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modeling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.

The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete.

A dual "turn-on" biosensor based on AIE effect and FRET for in situ detection of miR-125b biomarker in early Alzheimer's disease.

MicroRNA-125b (miR-125b) is highly associated with synaptic dysfunction and tau hyperphosphorylation in the early pathogenesis of Alzheimer's disease (AD), making it a promising biomarker for early AD diagnosis. Hence, there is an urgent need for a reliable sensing platform to assist in situ miR-125b detection. In this work, we report a dual "turn-on" fluorescence biosensor based on the nanocomposite of aggregation-induced emission fluorogen (AIEgen)-labeled oligonucleotide (TPET-DNA) probes immobilized on the surface of cationic dextran modified molybdenum disulfide (TPET-DNA@Dex-MoS2). In the presence of the target, TEPT-DNA can hybridize with miR-125b to form a DNA/RNA duplex, causing TPET-DNA to detach from the surface of Dex-MoS2 that simultaneously activates the dual fluorescence enhancement processes: (1) recovery of TPET-DNA signal and (2) strong fluorescent emission from AIEgen triggered by restriction of the intramolecular rotation. The sensing performance of TPET-DNA@Dex-MoS2 was demonstrated by detecting miR-125b in vitro with good sensitivity at the picomolar level and rapid response (≤1 h) without amplification procedures. Furthermore, our nanoprobes exhibited excellent imaging capabilities to aid real-time monitoring of the endogenous miR-125b in PC12 cells and brain tissues of mice AD model induced by local administration of okadaic acid (OA). The fluorescence signals of the nanoprobes indicated miR-125b was spatially associated with phosphorylated tau protein (p-tau) in vitro and in vivo. Therefore, TPET-DNA@Dex-MoS2 could be a promising tool for in situ and real-time monitoring of the AD-related microRNAs and also provide mechanistic insight into the early prognosis of AD.

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material.

The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R2: 0.9800 and 0.9108, VAF: 98.0005% and 91.0880%, RMSE: 0.7057 and 1.9128, MAE: 0.4461 and 0.7364; R2: 0.9857 and 0.9065, VAF: 98.5909% and 91.3652%, RMSE: 0.5781 and 1.8814, MAE: 0.4233 and 0.9913). In addition, the sensitive analysis results indicated that the rubber and cement are the most important parameters for predicting the strength and energy absorption properties, respectively. Accordingly, the improved POA-RF model not only is proven as an effective method to predict the strength and energy absorption properties of aseismic materials, but also this hybrid model provides a new idea for assessing other aseismic performances in the field of tunnel engineering.

Defective Bi2S3 Anchored on CuS/C as an Ultrafast and Long-Life Anode for Sodium-Ion Storage.

Due to the high electrical conductivity and abundant redox active sites, bimetal sulfides are highly competitive anode materials for sodium storage with long-life and high-rate. Herein, a heterostructured metal sulfide (Bi2S3-CuS) with a carbon-based support is prepared by calcination and ion exchange methods. The synergistic effects of the heterostructure and defective structure provide facile diffusion channels, fast Na+ migration, and plentiful active sites for Na+, which reflect in the impressive electrochemical performance with a high reversible capacity of 592.2 mA h g-1 after 1000 cycles at 8 A g-1. Furthermore, the Na-ion full batteries exhibit an ultra-long cycling performance with a value of 216 mA h g-1 after 4000 cycles at 10 A g-1. Interestingly, the defective structure of Bi2S3 remains after cycling. Kinetic analyses and density functional theoretical calculations clarified that the heterointerfacial structure, especially on the interface containing sulfur defects in Bi2S3 of Bi2S3-CuS, could induce feasible ion adsorption and promote ion transfer, which lays the foundation for achieving ultrafast sodiation kinetics.

Solid-Phase Synthesis of Titanium Dioxide Micro-Nanostructures.

Titanium dioxide (TiO2) micro-nanostructures are widely utilized in photochemical applications due to their unique band gaps and are of huge demand in scientific research and industrial manufacture. Herein, this work reports a controllable, facile, economical, and green solid-phase synthesis strategy to prepare TiO2 with governable morphologies containing 1D nanorods, 3D microbulks, and irregular thick plates. Specifically, Ti powders are transformed into TiO2 micro-nanostructures through dispersing them into a solid NaOH/KOH mixture with a low eutectic point, followed by grinding, heating, ion exchange, and calcination. As no solvents are utilized in the alkali treatment process, the usage of solvents is decreased and high vapor pressure is avoided. Moreover, the band gaps of TiO2 micro-nanostructures can be regulated from 3.02 to 3.34 eV through altering the synthetic parameters. Notably, the as-prepared TiO2 micro-nanostructures exhibit high photocatalytic activities in the degradation of rhodamine B and methylene blue under simulated solar light illumination. It is believed that the solid-phase synthesis strategy will be of huge demand for the synthesis of TiO2 micro-nanostructures.

A CRISPR-Cas12a integrated SERS nanoplatform with chimeric DNA/RNA hairpin guide for ultrasensitive nucleic acid detection.

Background: CRISPR-Cas12a has been integrated with nanomaterial-based optical techniques, such as surface-enhanced Raman scattering (SERS), to formulate a powerful amplification-free nucleic acid detection system. However, nanomaterials impose steric hindrance to limit the accessibility of CRISPR-Cas12a to the narrow gaps (SERS hot spots) among nanoparticles (NPs) for producing a significant change in signals after nucleic acid detection. Methods: To overcome this restriction, we specifically design chimeric DNA/RNA hairpins (displacers) that can be destabilized by activated CRISPR-Cas12a in the presence of target DNA, liberating excessive RNA that can disintegrate a core-satellite nanocluster via toehold-mediated strand displacement for orchestrating a promising "on-off" nucleic acid biosensor. The core-satellite nanocluster comprises a large gold nanoparticle (AuNP) core surrounded by small AuNPs with Raman tags via DNA hybridization as an ultrabright Raman reporter, and its disassembly leads to a drastic decrease of SERS intensity as signal readouts. We further introduce a magnetic core to the large AuNPs that can facilitate their separation from the disassembled nanostructures to suppress the background for improving detection sensitivity. Results: As a proof-of-concept study, our findings showed that the application of displacers was more effective in decreasing the SERS intensity of the system and attained a better limit of detection (LOD, 10 aM) than that by directly using activated CRISPR-Cas12a, with high selectivity and stability for nucleic acid detection. Introducing magnetic-responsive functionality to our system further improves the LOD to 1 aM. Conclusion: Our work not only offers a platform to sensitively and selectively probe nucleic acids without pre-amplification but also provides new insights into the design of the CRISPR-Cas12a/SERS integrated system to resolve the steric hindrance of nanomaterials for constructing biosensors.

Constructing Direct Z-Scheme Heterostructure by Enwrapping ZnIn2 S4 on CdS Hollow Cube for Efficient Photocatalytic H2 Generation.

Rational design hybrid nanostructure photocatalysts with efficient charge separation and transfer, and good solar light harvesting ability have critical significance for achieving high solar-to-chemical conversion efficiency. Here a highly active and stable composite photocatalyst is reported by integrating ultrathin ZnIn2 S4 nanosheets on surface of hollow CdS cube to form the cube-in-cube structure. Experimental results combined with density functional theory calculations confirm that the Z-scheme ZnIn2 S4 /CdS heterojunction is formed, which highly boosts the charge separation and transfer under the local-electric-field at semiconductor/semiconductor interface, and thus prolongs their lifetimes. Moreover, such a structure affords the highly enhanced light-harvesting property. The optimized ZnIn2 S4 /CdS nanohybrids exhibit superior H2 generation rate under visible-light irradiation (λ ≥ 420 nm) with excellent photochemical stability during 20 h continuous operation.

An AIEgen/graphene oxide nanocomposite (AIEgen@GO)-based two-stage "turn-on" nucleic acid biosensor for rapid detection of SARS-CoV-2 viral sequence.

The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has posed significant challenges in early viral diagnosis. Hence, it is urgently desirable to develop a rapid, inexpensive, and sensitive method to aid point-of-care SARS-CoV-2 detection. In this work, we report a highly sequence-specific biosensor based on nanocomposites with aggregation-induced emission luminogens (AIEgen)-labeled oligonucleotide probes on graphene oxide nanosheets (AIEgen@GO) for one step-detection of SARS-CoV-2-specific nucleic acid sequences (Orf1ab or N genes). A dual "turn-on" mechanism based on AIEgen@GO was established for viral nucleic acids detection. Here, the first-stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid, and the second-stage enhancement of AIE-based fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen. Furthermore, the feasibility of our platform for diagnostic application was demonstrated by detecting SARS-CoV-2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification. Our platform shows great promise in assisting the initial rapid detection of the SARS-CoV-2 nucleic acid sequence before utilizing quantitative reverse transcription-polymerase chain reaction for second confirmation.

Architecting Braided Porous Carbon Fibers Based on High-Density Catalytic Crystal Planes to Achieve Highly Reversible Sodium-Ion Storage.

Carbonaceous materials are considered strong candidates as anode materials for sodium-ion batteries (SIBs), which are expected to play an indispensable role in the carbon-neutral era. Herein, novel braided porous carbon fibres (BPCFs) are prepared using the chemical vapour deposition (CVD) method. The BPCFs possess interwoven porous structures and abundant vacancies. The growth mechanism of the BPCFs can be attributed to the polycrystalline transformation of the nanoporous copper catalyst in the early stage of CVD process. Density functional theory calculations suggest that the Na+ adsorption energies of the mono-vacancy edges of the BPCFs (-1.22 and -1.09 eV) are lower than that of an ideal graphene layer (-0.68 eV), clarifying in detail the adsorption-dominated sodium storage mechanism. Hence, the BPCFs as an anode material present an outstanding discharge capacity of 401 mAh g-1 at 0.1 A g-1 after 500 cycles. Remarkably, this BPCFs anode, under high-mass-loading of 5 mg cm-2, shows excellent long-term cycling ability with a reversible capacity of 201 mAh g-1 at 10 A g-1 over 1000 cycles. This study provided a novel strategy for the development of high-performance carbonaceous materials for SIBs.

Magnetic-Responsive Surface-Enhanced Raman Scattering Platform with Tunable Hot Spot for Ultrasensitive Virus Nucleic Acid Detection.

Surface-enhanced Raman scattering (SERS)-based biosensors are promising tools for virus nucleic acid detection. However, it remains challenging for SERS-based biosensors using a sandwiching strategy to detect long-chain nucleic acids such as nucleocapsid (N) gene of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) because the extension of the coupling distance (CD) between the two tethered metallic nanostructures weakens electric field and SERS signals. Herein, we report a magnetic-responsive substrate consisting of heteoronanostructures that controls the CD for ultrasensitive and highly selective detection of the N gene of SARS-CoV-2. Significantly, our findings show that this platform reversibly shortens the CD and enhances SERS signals with a 10-fold increase in the detection limit from 1 fM to 100 aM, compared to those without magnetic modulation. The optical simulation that emulates the CD shortening process confirms the CD-dependent electric field strength and further supports the experimental results. Our study provides new insights into designing a stimuli-responsive SERS-based platform with tunable hot spots for long-chain nucleic acid detection.

Surface plasmon enhanced THz emission with nanoporous gold supported CdTe.

Terahertz emission by ultrafast excitation of semiconductor/metal interfaces was found strongly enhanced by plasmon resonance. Here, a three-dimensional nanoporous gold (NPG) was used to form semiconductor/metal compound with cadmium telluride (CdTe). We investigated the specific impact of surface plasmon from randomly nanoporous structure in the ultrafast optoelectronic response for THz generation, and observed a THz amplitude enhancement around an order of magnitude from CdTe on NPG compared to that from CdTe on silicon. Moreover, the plasmon enhancement for THz emission from NPG is stronger than that from gold film, indicating that randomly nanoporous structure is also effective for plasmonic enhancement in THz band.

Recent advances in the removal of persistent organic pollutants (POPs) using multifunctional materials:a review.

Persistent organic pollutants (POPs) have gained heightened attentions in recent years owing to their persistent property and hazard influence on wild life and human beings. Removal of POPs using varieties of multifunctional materials have shown a promising prospect compared with conventional treatments. Herein, three main categories, including thermal degradation, electrochemical remediation, as well as photocatalytic degradation with the use of diverse catalytic materials, especially the recently developed prominent ones were comprehensively reviewed. Kinetic analysis and underlying mechanism for various POPs degradation processes were addressed in detail. The review also systematically documented how catalytic performance was dramatically affected by the nature of the material itself, the structure of target pollutants, reaction conditions and treatment techniques. Moreover, the future challenges and prospects of POPs degradation by means of multiple multifunctional materials were outlined accordingly. Knowing this is of immense significance to enhance our understanding of POPs remediation procedures and promote the development of novel multifunctional materials.

Environmental impact and health risk assessment of volatile organic compound emissions during different seasons in Beijing.

Volatile organic compounds (VOCs) are major contributors to air pollution. Based on the emission characteristics of 99 VOCs that daily measured at 10 am in winter from 15 December 2015 to 17 January 2016 and in summer from 21 July to 25 August 2016 in Beijing, the environmental impact and health risk of VOC were assessed. In the winter polluted days, the secondary organic aerosol formation potential (SOAP) of VOC (199.70 ± 15.05 µg/m3) was significantly higher than that on other days. And aromatics were the primary contributor (98.03%) to the SOAP during the observation period. Additionally, the result of the ozone formation potential (OFP) showed that ethylene contributed the most to OFP in winter (26.00% and 27.64% on the normal and polluted days). In summer, however, acetaldehyde was the primary contributor to OFP (22.00% and 21.61% on the normal and polluted days). Simultaneously, study showed that hazard ratios and lifetime cancer risk values of acrolein, chloroform, benzene, 1,2-dichloroethane, acetaldehyde and 1,3-butadiene exceeded the thresholds established by USEPA, thereby presenting a health risk to the residents. Besides, the ratio of toluene-to-benzene indicated that vehicle exhausts were the main source of VOC pollution in Beijing. The ratio of m-/p-xylene-to-ethylbenzene demonstrated that there were more prominent atmospheric photochemical reactions in summer than that in winter. Finally, according to the potential source contribution function (PSCF) results, compared with local pollution sources, the spread of pollution from long-distance VOCs had a greater impact on Beijing.