Optical dipole-induced anisotropic growth of semiconductors: A facile strategy toward chiral and complex nanostructures.

Chiral nanostructures based on semiconductors exhibit pronounced properties of chiral luminescence and optoelectronic responses, which are fundamental for chiroptoelectronic devices. However, the state-of-the-art techniques of generating semiconductors with chiral configurations are poorly developed, most of which are complicated or of low yield, rendering low compatibility to the platform of optoelectronic devices. Here we show polarization-directed oriented growth of platinum oxide/sulfide nanoparticles based on optical dipole interactions and near-field-enhanced photochemical deposition. By rotating the polarization during the irradiation or employing vector beam, both three dimensional and planar chiral nanostructures can be obtained, which is extendable to cadmium sulfide. These chiral superstructures exhibit broadband optical activity with a g-factor of ~0.2 and a luminescence g-factor of ~0.5 in the visible, making them promising candidate for chiroptoelectronic devices.

P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network.

Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.

Enhanced mitophagy driven by ADAR1-GLI1 editing supports the self-renewal of cancer stem cells in HCC.

BACKGROUND AND AIMS: Deregulation of adenosine-to-inosine editing by adenosine deaminase acting on RNA 1 (ADAR1) leads to tumor-specific transcriptome diversity with prognostic values for HCC. However, ADAR1 editase-dependent mechanisms governing liver cancer stem cell (LCSC) generation and maintenance have remained elusive. APPROACH AND RESULTS: RNA-seq profiling identified ADAR1-responsive recoding editing events in HCC and showed editing frequency of GLI1 , rather than transcript abundance was clinically relevant. Functional differences in LCSC self-renewal and tumor aggressiveness between wild-type (GLI1 wt ) and edited GLI1 (GLI1 edit ) were elucidated. We showed that overediting of GLI1 induced an arginine-to-glycine (R701G) substitution, augmenting tumor-initiating potential and exhibiting a more aggressive phenotype. GLI1 R701G harbored weak affinity to SUFU, which in turn, promoted its cytoplasmic-to-nuclear translocation to support LCSC self-renewal by increased pluripotency gene expression. Moreover, editing predisposed to stabilize GLI1 by abrogating ß-TrCP-GLI1 interaction. Integrative analysis of single-cell transcriptome further revealed hyperactivated mitophagy in ADAR1-enriched LCSCs. GLI1 editing promoted a metabolic switch to oxidative phosphorylation to control stress and stem-like state through PINK1-Parkin-mediated mitophagy in HCC, thereby conferring exclusive metastatic and sorafenib-resistant capacities. CONCLUSIONS: Our findings demonstrate a novel role of ADAR1 as an active regulator for LCSCs properties through editing GLI1 in the highly heterogeneous HCC.

SPHK1/S1P/S1PR pathway promotes the progression of peritoneal fibrosis by mesothelial-mesenchymal transition.

Long-term exposure to non-physiologically compatible dialysate inevitably leads to peritoneal fibrosis (PF) in patients undergoing peritoneal dialysis (PD), and there is no effective prevention or treatment for PF. Sphingosine-1-phosphate (S1P) is a bioactive sphingolipid produced after catalysis by sphingosine kinase (SPHK) 1/2 and activates signals through the S1P receptor (S1PR) via autocrine or paracrine. However, the role of SPHK1/S1P/S1PR signaling has never been elucidated in PF. In our research, we investigated S1P levels in peritoneal effluents and demonstrated the role of SPHK1/S1P/S1PR pathway in peritoneal fibrosis. It was found that S1P levels in peritoneal effluents were positively correlated with D/P Cr (r = 0.724, p < .001) and negatively correlated with 4 h ultrafiltration volume (r = -0.457, p < .001). S1PR1 and S1PR3 on peritoneal cells were increased after high glucose exposure in vivo and in vitro. Fingolimod was applied to suppress S1P/S1PR pathway. Fingolimod restored mouse peritoneal function by reducing interstitial hyperplasia, maintaining ultrafiltration volume, reducing peritoneal transport solute rate, and mitigating the protein expression changes of fibronectin, vimentin, α-SMA, and E-cadherin induced by PD and S1P. Fingolimod preserved the morphology of the human peritoneal mesothelial cells, MeT-5A, and moderated the mesothelial-mesenchymal transition (MMT) process. We further delineated that SPHK1 was elevated in peritoneal cells after high glucose exposure and suppression of SPHK1 in MeT-5A cells reduced S1P release. Overexpression of SPHK1 in MeT-5A cells increased S1P levels in the supernatant and fostered the MMT process. PF-543 treatment, targeting SPHK1, alleviated deterioration of mouse peritoneal function. In conclusion, S1P levels in peritoneal effluent were correlated with the deterioration of peritoneal function. SPHK1/S1P/S1PR pathway played an important role in PF.

Construction of an Electron Capture and Transfer Center for Highly Efficient and Selective Solar-Light-Driven CO2 Conversion.

Exploring high-efficiency photocatalysts for selective CO2 reduction is still challenging because of the limited charge separation and surface reactions. In this study, a noble-metal-free metallic VSe2 nanosheet was incorporated on g-C3N4 to serve as an electron capture and transfer center, activating surface active sites for highly efficient and selective CO2 photoreduction. Quasi in situ X-ray photoelectron spectroscopy (XPS), soft X-ray absorption spectroscopy (sXAS), and femtosecond transient absorption spectroscopy (fs-TAS) unveiled that VSe2 could capture electrons, which are further transferred to the surface for activating active sites. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory (DFT) calculations revealed a kinetically feasible process for the formation of a key intermediate and confirmed the favorable production of CO on the VSe2/PCN (protonated C3N4) photocatalyst. As an outcome, the optimized VSe2/PCN composite achieved 97% selectivity for solar-light-driven CO2 conversion to CO with a high rate of 16.3 µmol·g-1·h-1, without any sacrificial reagent or photosensitizer. This work offers new insights into the photocatalyst design toward highly efficient and selective CO2 conversion.

Boosting Electrocatalytic Ammonia Synthesis via Synergistic Effect of Iron-Based Single Atoms and Clusters.

Electrocatalytic reduction of nitrate to ammonia (NO3RR) is gaining attention for low carbon emissions and environmental protection. However, low ammonia production rate and poor selectivity have remained major challenges in this multi-proton coupling process. Herein, we report a facile strategy toward a novel Fe-based hybrid structure composed of Fe single atoms and Fe3C atomic clusters that demonstrates outstanding performance for synergistic electrocatalytic NO3RR. By operando synchrotron Fourier transform infrared spectroscopy and theoretical computation, we clarify that Fe single atoms serve as the active site for NO3RR, while Fe3C clusters facilitate H2O dissociation to provide protons (*H) for continued hydrogenation reactions. As a result, the Fe-based electrocatalyst exhibits ammonia Faradaic efficiency of nearly 100%, with a corresponding production rate of 24768 µg h-1 cm-2 at -0.4 V vs RHE, exceeding most reported metal-based catalysts. This research provides valuable guidance toward multi-step reactions.

Enhancing Light Out-coupling in Perovskite Light-Emitting Diodes through Plasmonic Nanostructures.

Perovskite light-emitting diodes (PeLEDs) have emerged as promising candidates for lighting and display technologies owing to their high photoluminescence quantum efficiency and high carrier mobility. However, the performance of planar PeLEDs is limited by the out-coupling efficiency, predominantly governed by photonic losses at device interfaces. Most notably, the plasmonic loss at the metal electrode interfaces can account for up to 60% of the total loss. Here, we investigate the use of plasmonic nanostructures to improve the light out-coupling in PeLEDs. By integrating these nanostructures with PeLEDs, we have demonstrated an effectively reduced plasmonic loss and enhanced light out-coupling. As a result, the nanostructured PeLEDs exhibit an average 1.5-fold increase in external quantum efficiency and an ∼20-fold improvement in device lifetime. This finding offers a generic approach for enhancing light out-coupling, promising great potential to go beyond existing performance limitations.

Global profiling of protein lactylation in Caenorhabditis elegans.

Lactylation, as a novel posttranslational modification, is essential for studying the functions and regulation of proteins in physiological and pathological processes, as well as for gaining in-depth knowledge on the occurrence and development of many diseases, including tumors. However, few studies have examined the protein lactylation of one whole organism. Thus, we studied the lactylation of global proteins in Caenorhabditis elegans to obtain an in vivo lactylome. Using an MS-based platform, we identified 1836 Class I (localization probabilities > 0.75) lactylated sites in 487 proteins. Bioinformatics analysis showed that lactylated proteins were mainly located in the cytoplasm and involved in the tricarboxylic acid cycle (TCA cycle) and other metabolic pathways. Then, we evaluated the conservation of lactylation in different organisms. In total, 41 C. elegans proteins were lactylated and homologous to lactylated proteins in humans and rats. Moreover, lactylation on H4K80 was conserved in three species. An additional 238 lactylated proteins were identified in C. elegans for the first time. This study establishes the first lactylome database in C. elegans and provides a basis for studying the role of lactylation.

Proteomic and Phosphoproteomic Profiling of Matrix Stiffness-Induced Stemness-Dormancy State Transition in Breast Cancer Cells.

The dormancy of cancer stem cells is a major factor leading to drug resistance and a high rate of late recurrence and mortality in estrogen receptor-positive (ER+) breast cancer. Previously, we demonstrated that a stiffer matrix induces tumor cell dormancy and drug resistance, whereas a softened matrix promotes tumor cells to exhibit a stem cell state with high proliferation and migration. In this study, we present a comprehensive analysis of the proteome and phosphoproteome in response to gradient changes in matrix stiffness, elucidating the mechanisms behind cell dormancy-induced drug resistance. Overall, we found that antiapoptotic and membrane transport processes may be involved in the mechanical force-induced dormancy resistance of ER+ breast cancer cells. Our research provides new insights from a holistic proteomic and phosphoproteomic perspective, underscoring the significant role of mechanical forces stemming from the stiffness of the surrounding extracellular matrix as a critical regulatory factor in the tumor microenvironment.

Charge Transfer Plasmons Enabled by Supramolecular Plug: From Optoelectronic Switching to Enhanced Chiral Sensing.

Miniaturization and integration of plasmonic nanodevices are fundamentally limited by quantum tunneling, which leads to quantum plasmonics with reduced local E-field intensity. Despite significant efforts devoted to modeling and deterring the detrimental effect of quantum plasmonics, the modulation and application of electron transport through the subnanometer gaps seems rarely exploited due to the limited tunability of conventional quantum materials. Here, we establish a supramolecular plasmonic system made of pillar[5]arene complexes and plasmonic resonators (nanoparticle-on-mirror, NPoM). The supramolecular assemblies significantly enhance the gap conductance of NPoM, which results in a blue-shift of the coupled plasmons. Plasmonic hot-electron transport with laser excitation further modulates the gap plasmons, which are fully reversible and beneficial for enhanced chiroptic sensing. Such a conductive supramolecular plasmonic system not only suggests an optoelectronic switching strategy for charge transfer plasmons but also provides a superior sensing platform for single molecules.

Visualization of the Distance-Dependent Synergistic Interaction in Heterogeneous Dual-Site Catalysis.

Understanding the characteristics of interfacial hydroxyl (OH) at the solid/liquid electrochemical interface is crucial for deciphering synergistic catalysis. However, it remains challenging to elucidate the influences of spatial distance between interfacial OH and neighboring reactants on reaction kinetics at the atomic level. Herein, we visualize the distance-dependent synergistic interaction in heterogeneous dual-site catalysis by using ex-situ infrared nanospectroscopy and in situ infrared spectroscopy techniques. These spectroscopic techniques achieve direct identification of the spatial distribution of synergistic species and reveal that OH facilitates the reactant deprotonation process depending on site distances in dual-site catalysts. Via modulating Ir-Co pair distances, we find that the dynamic equilibrium between generation and consumption of OH accounts for high-efficiency synergism at the optimized distance of 7.9 Å. At farther or shorter distances, spatial inaccessibility and resistance of OH with intermediates lead to OH accumulation, thereby diminishing the synergistic effect. Hence, a volcano-shaped curve has been established between the spatial distance and mass activity using formic acid oxidation as the probe reaction. This notion could also be extended to oxophilic metals, like Ir-Ru pairs, where volcano curves and dynamic equilibrium further evidence the universal significance of spatial distances.

A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data.

With the development of high-throughput technologies, the accumulation of large amounts of multidimensional genomic data provides an excellent opportunity to study the multilevel biological regulatory relationships in cancer. Based on the hypothesis of competitive endogenous ribonucleic acid (RNA) (ceRNA) network, lncRNAs can eliminate the inhibition of microRNAs (miRNAs) on their target genes by binding to intracellular miRNA sites so as to improve the expression level of these target genes. However, previous studies on cancer expression mechanism are mostly based on individual or two-dimensional data, and lack of integration and analysis of various RNA-seq data, making it difficult to verify the complex biological relationships involved. To explore RNA expression patterns and potential molecular mechanisms of cancer, a network-regularized sparse orthogonal-regularized joint non-negative matrix factorization (NSOJNMF) algorithm is proposed, which combines the interaction relations among RNA-seq data in the way of network regularization and effectively prevents multicollinearity through sparse constraints and orthogonal regularization constraints to generate good modular sparse solutions. NSOJNMF algorithm is performed on the datasets of liver cancer and colon cancer, then ceRNA co-modules of them are recognized. The enrichment analysis of these modules shows that >90% of them are closely related to the occurrence and development of cancer. In addition, the ceRNA networks constructed by the ceRNA co-modules not only accurately mine the known correlations of the three RNA molecules but also further discover their potential biological associations, which may contribute to the exploration of the competitive relationships among multiple RNAs and the molecular mechanisms affecting tumor development.

Base-Stabilized Gallium Sulfides and Selenides Supported by a Bis(oxazolinyl)(phenyl)methanide Ligand.

Gallylene supported by a bis(oxazolinyl)(phenyl)methanide (Boxm) ligand was synthesized and structurally characterized. The reaction of this gallylene with triphenylphosphine sulfide/selenide yielded dimeric gallium sulfide and selenide. These compounds could be converted to monomeric terminal sulfide and selenide by coordination of an external Lewis base such as an N-heterocyclic carbene (NHC or IMe4) and 4-dimethylaminopyridiene (DMAP). These doubly-base-stabilized gallium sulfide/selenide reacted with phenyl isocyanate to give the corresponding cycloadducts by releasing the Lewis base, indicating the formation of a single-base-stabilized gallium sulfide/selenide intermediate.

Recurrence risk stratification of hepatocellular carcinomas based on immune gene expression and features extracted from pathological images.

BACKGROUND: Immune-based therapy is a promising type of treatment for hepatocellular carcinoma (HCC) but has only been partially successful due to the high heterogeneity in HCC tumor. The differences in the degree of tumor cell progression and in the activity of tumor immune microenvironment could lead to varied clinical outcome. Accurate subgrouping for recurrence risk is an approach to address the issue of such heterogeneity. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome. METHODS: We included a total of 231 patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project. For each patient, we extracted 18 statistical metrics corresponding to a global region of interest and 135 features regarding nucleus shape from WSI. A risk score was developed using these image features with high-dimensional survival modeling. We also introduced into the model the expression profile of 66 representative marker genes relevant to currently available immunotherapies. We stratified all patients into higher and lower-risk subgroup based on the final risk score selected from multiple models generated, and further investigated underlying molecular mechanisms associated with the risk stratification. RESULTS: One WSI feature and three immune marker genes were selected into the final recurrence-free survival (RFS) prediction model following the best integrated modeling framework. The resultant score showed a significantly improved prediction performance on the test dataset (mean time-dependent AUCs = 0.707) as compared to those of other types (e.g: mean time-dependent AUCs of AJCC tumor stage = 0.525) of input data integration. To assess that the risk score could provide a higher-resolution risk stratification, a lower-risk subgroup (or a higher-risk subgroup) was arbitrarily assigned according to score falling below (or above) the median score. The lower risk subgroup had significantly longer median RFS time than that of the higher-risk patients (median RFS = 903 vs. 265 days, log-rank test p-value< 0.0001). Additionally, the higher-risk subgroup, in contrast to the lower-risk patients were characterized with a significant downregulation of immune checkpoint genes, suppressive signal in tumor immune response pathways, and depletion of CD8 T cells. These observations for the higher-risk subgroup suggest that new targets for adoptive or checkpoint-based combined systemic therapies may be useful. CONCLUSION: We developed a novel prognostic model to predict RFS for HCC patients, using one feature that can be automatically extracted from routine histopathological images, as well as the expression profiles of three immune marker genes. The methodology used in this paper demonstrates the feasibility of developing prognostic models that provide both useful risk stratification along with valuable biological insights into the underlying characteristics of the subgroups identified.

Double pyramid stacked CoO nano-crystals induced by graphene at low temperatures as highly efficient Fenton-like catalysts.

Transition metal oxides are widely used as Fenton-like catalysts in the treatment of organic pollutants, but their synthesis usually requires a high temperature. Herein, an all-solid-state synthesis method controlled by graphene was used to prepare a double pyramid stacked CoO nano-crystal at a low temperature. The preparation temperature decreased by 200 °C (over 30% reduction) due to the introduction of graphene, largely reducing the reaction energy barrier. Interestingly, the corresponding degradation rate constants (kobs) of this graphene-supported pyramid CoO nano-crystals for organic molecules after their adsorption were over 2.5 and 35 times higher than that before adsorption and that of free CoO, respectively. This high catalytic efficiency is attributed to the adsorption of pollutants at the surface by supporting graphene layers, while free radicals activated by CoO can directly and rapidly contact and degrade them. These findings provide a new strategy to prepare low carbon-consuming transition metal oxides for highly efficient Fenton-like catalysts.

Metabolites and metabolic pathway analysis of sulfadimidine in carp (Cyprinus carpio) based on UHPLC-Q-orbitrap HRMS.

Sulfadimidine (SM2) is an N-substituted derivative of p-aminobenzenesulfonyl structure. This study aimed to analyze the metabolism of SM2 in carp (Cyprinus carpio). The carps were fed with SM2 at a dose of 200 mg/(kg · bw) and then killed. The blood, muscle, liver, kidney, gill, other guts, and carp aquaculture water samples were collected. The UHPLC-Q-Exactive Plus Orbitrap-MS was adopted for determining the metabolites of SM2 in the aforementioned samples. Twelve metabolites, which were divided into metabolites in vivo and metabolites in vitro, were identified using Compound Discoverer software. The metabolic pathways in vivo of SM2 in carp included acetylation, hydroxylation, glucoside conjugation, glycine conjugation, carboxylation, glucuronide conjugation, reduction, and methylation. The metabolic pathways in vitro included oxidation and acetylation. This study clarified the metabolites and metabolic pathways of SM2 in carp and provided a reference for further pharmacodynamic evaluation and use in aquaculture.

Exosomal miR-205-5p contributes to the immune liver injury induced by trichloroethylene: Pivotal role of RORα mediating M1 Kupffer cell polarization.

Trichloroethylene (TCE) is a common environmental contaminant that can induce occupational dermatitis medicamentosa-like TCE (ODMLT), where the liver damage is the most common complication. The study aims to uncover the underlying mechanism of TCE-sensitization-induced liver damage by targeting specific exosomal microRNAs (miRNAs). Among the enriched serum exosomal miRNAs of ODMLT patients, miR-205-5p had a significant correlation coefficient with the liver function damage indicators. Moreover, retinoic acid receptor-related orphan receptor α (RORα) was identified as a direct target of miR-205-5p via specific binding. Further experiments showed that kupffer cells (KCs) underwent M1 phenotypic and functional changes in liver injury induced by TCE which were alleviated by reducing the expression of miR-205-5p. However, this alleviation was reversed by the RORα antagonist SR1001. In vitro experiments showed that miR-205-5p promoted M1 polarization of macrophages and enhanced the secretion of inflammatory factors by regulating RORα. An increase in RORα reversed the polarization direction of M1-type macrophages and reduced the secretion of proinflammatory factors. In addition, pretreatment of mice with SR1078, a specific RORα agonist, effectively blocked M1 polarization of KCs and reduced the severity of TCE-induced liver injury. Our study uncovers that miR-205-5p regulates KC M1 polarization by targeting RORα in immune liver injury induced by TCE sensitization, providing new insight into the molecular mechanisms and new therapeutic targets for ODMLT.

Extending Plasmonic Enhancement Limit with Blocked Electron Tunneling by Monolayer Hexagonal Boron Nitride.

Fabricating ultrasmall nanogaps for significant electromagnetic enhancement is a long-standing goal of surface-enhanced Raman scattering (SERS) research. However, such electromagnetic enhancement is limited by quantum plasmonics as the gap size decreases below the quantum tunneling regime. Here, hexagonal boron nitride (h-BN) is sandwiched as a gap spacer in a nanoparticle-on-mirror (NPoM) structure, effectively blocking electron tunneling. Layer-dependent scattering spectra and theoretical modeling confirm that the electron tunneling effect is screened by monolayer h-BN in a nanocavity. The layer-dependent SERS enhancement factor of h-BN in the NPoM system monotonically increases as the number of layers decreases, which agrees with the prediction by the classical electromagnetic model but not the quantum-corrected model. The ultimate plasmonic enhancement limits are extended in the classical framework in a single-atom-layer gap. These results provide deep insights into the quantum mechanical effects in plasmonic systems, enabling the potential novel applications based on quantum plasmonic.

Self-Optimized Ligand Effect of Single-Atom Modifier in Ternary Pt-Based Alloy for Efficient Hydrogen Oxidation.

Modifying the atomic and electronic structure of platinum-based alloy to enhance its activity and anti-CO poisoning ability is a vital issue in hydrogen oxidation reaction (HOR). However, the role of foreign modifier metal and the underlying ligand effect is not fully understood. Here, we propose that the ligand effect of single-atom Cu can dynamically modulate the d-band center of Pt-based alloy for boosting HOR performance. By in situ X-ray absorption spectroscopy, our research has identified that the potential-driven structural rearrangement into high-coordination Cu-Pt/Pd intensifies the ligand effect in Pt-Cu-Pd, leading to enhanced HOR performance. Thereby, modulating the d-band structure leads to near-optimal hydrogen/hydroxyl binding energies and reduced CO adsorption energies for promoting the HOR kinetics and the CO-tolerant capability. Accordingly, PtPdCu1/C exhibits excellent CO tolerance even at 1,000 ppm impurity.

Research on characteristics and influencing factors of road dust emission in a southern city in China.

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.