SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field.

AVAILABILITY AND IMPLEMENTATION

The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI.

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

Towards texture accurate slice interpolation of medical images using PixelMiner

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations

Data-driven thalamic nuclei parcellation depends on high-quality manual annotations. However, the small size and low contrast changes among thalamic nuclei, yield annotations that are often incomplete, noisy, or ambiguously labelled. To train a robust thalamic nuclei parcellation model with noisy annotations, we propose a label propagation algorithm based on random walker to refine the annotations before model training. A two-step model was trained to generate first the whole thalamus and then the nuclei masks. We conducted experiments on a mild traumatic brain injury~(mTBI) dataset with noisy thalamic nuclei annotations. Our model outperforms current state-of-the-art thalamic nuclei parcellations by a clear margin. We believe our method can also facilitate the training of other parcellation models with noisy labels.

[Prevalence of colorectal cancer in 2020: a comparative analysis between China and the world]

Objective: To demonstrate the disease burden and epidemiological characteristics of colorectal cancer in different regions by analyzing the incidence and mortality data in China and worldwide in 2020. Methods: Estimation of the incidence and mortality data of colorectal cancer were obtained from the GLOBOCAN 2020 database. The incidence, death, age standardized incidence rate (ASIR) and age standardized mortality rate (ASMR) of colorectal cancer in China and 20 regions in the world were compared. The correlation between the Human Development Index (HDI) and ASIR/ASMR was analyzed. Results: In 2020, the number of new cases of colorectal cancer in the world reached 1 931 600, and the number of deaths reached 935 200. The incidence and mortality in all regions of the world continued to rise in the age group above 50 years old. The morbidity and mortality in male were higher than those in female. East Asia ranked the highest number of incidence cases and deaths in the world, which were 740 000 and 360 100 respectively. There were significant differences in incidence and mortality among regions in the world. The highest ASIR and ASMR were observed in Northern Europe (33.61/100 000) and Eastern Europe (14.53/100 000), whereas the lowest ASIR and ASMR were both observed in South-Central Asia (5.46/100 000 and 3.16/100 000). HDI had significant exponential relationship with ASIR (r(2)=0.59, P<0.001) and ASMR (r(2)=0.38, P<0.001). There were 555 500 new cases and 286 200 death cases of colorectal cancer in China, accounting for about 30% of the world and more than 75% of East Asia. The ASIR of China was 24.07/100 000, ranking at the medium level, while the ASMR was 12.07/100 000, ranking at the high level of world. Conclusion: The incidence and mortality of colorectal cancer are highly correlated with HDI. China is one of the countries with the heaviest disease burden of colorectal cancer in the world.

Mid-infrared silicon metasurfaces for near-field enhancement of molecular fingerprints

Mid-infrared dielectric metasurfaces are promising fundamental building blocks for integrated sensing with high sensitivity, compositional selectivity, and low loss. We have designed and fabricated a silicon metasurface with resonance properties in the 4∼5 µm mid-infrared region and a volume enhancement of up to 9 times. Benchmark FTIR characterizations of solutions of tungsten hexacarbonyl molecules showed a detection limit of 1 mg/mL without the usage of surface enrichment treatment. We further rationalize the detection limit of the molecules-nanostructure open interface with volume field enhancement analysis. Our results show that mid-infrared silicon metasurfaces may be a suitable platform for potential integration with microfluidic for in vivo detection.

Polylactic-co-glycolic acid-based nanoparticles modified with peptides and other linkers cross the blood-brain barrier for targeted drug delivery

Because of the blood-brain barrier, only a limited fraction of drugs can penetrate the brain. As a result, there is a need to take larger doses of the drug, which may result in numerous undesirable side effects. Over the past few decades, a plethora of research has been conducted to address this issue. In recent years, the field of nanomedicine research has reported promising findings. Currently, numerous types of polylactic-co-glycolic acid-based drug-delivery systems are being studied, and great progress has been made in the modification of their surfaces with a variety of ligands. In this review, the authors highlight the preparation of polylactic-co-glycolic acid-based nanoparticles and single- and dual-targeted peptide modifications for site-specific drug delivery into the brain.

CXCR2 expression during melanoma tumorigenesis controls transcriptional programs that facilitate tumor growth

Background

Though the CXCR2 chemokine receptor is known to play a key role in cancer growth and response to therapy, a direct link between expression of CXCR2 in tumor progenitor cells during induction of tumorigenesis has not been established.

Methods

To characterize the role of CXCR2 during melanoma tumorigenesis, we generated tamoxifen-inducible tyrosinase-promoter driven Braf ^V600E /Pten ^-/- /Cxcr2 ^-/- and NRas ^Q61R /INK4a ^-/- /Cxcr2 ^-/- melanoma models. In addition, the effects of a CXCR1/CXCR2 antagonist, SX-682, on melanoma tumorigenesis were evaluated in Braf ^V600E /Pten ^-/- and NRas ^Q61R /INK4a ^-/- mice and in melanoma cell lines. Potential mechanisms by which Cxcr2 affects melanoma tumorigenesis in these murine models were explored using RNAseq, mMCP-counter, ChIPseq, and qRT-PCR; flow cytometry, and reverse phosphoprotein analysis (RPPA).

Results

Genetic loss of Cxcr2 or pharmacological inhibition of CXCR1/CXCR2 during melanoma tumor induction resulted in key changes in gene expression that reduced tumor incidence/growth and increased anti-tumor immunity. Interestingly, after Cxcr2 ablation, Tfcp2l1 , a key tumor suppressive transcription factor, was the only gene significantly induced with a log ₂ fold-change greater than 2 in these three different melanoma models.

Conclusions

Here, we provide novel mechanistic insight revealing how loss of Cxcr2 expression/activity in melanoma tumor progenitor cells results in reduced tumor burden and creation of an anti-tumor immune microenvironment. This mechanism entails an increase in expression of the tumor suppressive transcription factor, Tfcp2l1, along with alteration in the expression of genes involved in growth regulation, tumor suppression, stemness, differentiation, and immune modulation. These gene expression changes are coincident with reduction in the activation of key growth regulatory pathways, including AKT and mTOR.

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

Tailoring particle size of PbI2 towards efficient perovskite solar cells under ambient air conditions

The PbI2 films as templates are essential to the quality of perovskite (PVK) film in two-step sequential deposition process. Herein, we regulate PbI2 films by tailoring the particle size of...

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device

In clinical conditions, polysomnography (PSG) is regarded as the "golden standard" for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into "good sleep" and "poor sleep" based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F₁-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring.

The Mechanical Behavior and Enhancement Mechanism of Short Carbon Fiber Reinforced AFS Interface

The aluminum foam sandwich (AFS), which perfectly combines the excellent merits of an aluminum foam core and face sheet materials, has extensive and reliable applications in many fields, such as aerospace, military equipment, transportation, and so on. Adhesive bonding is one of the most widely used methods to produce AFS due to its general applicability, simple process, and low cost, however, the bonding interface is known as the weak link and may cause a serious accident. To overcome the shortcomings of a bonded AFS interface, short carbon fiber as a reinforcement phase was introduced to epoxy resin to reinforce the interface adhesion strength of AFS. Single lap shear tests and three-point bending tests were conducted to study the mechanical behavior of the reinforced interface and AFS, respectively. The failure mechanism was studied through a macro- and microanalysis. The result showed that after the reinforcement of carbon fiber, the tangential shear strength of the interface increased by 73.65%. The effective displacement of AFS prepared by the reinforced epoxy resin is 125.95% more than the AFS prepared by the unreinforced epoxy resin. The flexure behavior of the reinforced AFS can be compared with AFS made through a metallurgical method. Three categories of reinforcement mechanisms were discovered: (a) the pull off and pull mechanism: when the modified carbon fiber performed as the bridge, the bonding strength improved because of the pull off and pull out of fibers; (b) adhesion effect: the carbon fiber gathered in the hole edge resulted in epoxy resins being gathered in there too, which increased the effective bonding area of the interface; (c) mechanical self-locking effect: the carbon fiber enhanced the adhesive filling performance of aluminum foam holes, which improved the mechanical self-locking effect of the bonding interface.

10.3% Efficient Green Cd-Free Cu2 ZnSnS4 Solar Cells Enabled by Liquid-Phase Promoted Grain Growth

Small grain size and near-horizontal grain boundaries are known to be detrimental to the carrier collection efficiency and device performance of pure-sulfide Cu₂ ZnSnS₄ (CZTS) solar cells. However, forming large grains spanning the absorber layer while maintaining high electronic quality is challenging particularly for pure sulfide CZTS. Herein, a liquid-phase-assisted grain growth (LGG) model that enables the formation of large grains spanning across the CZTS absorber without compromising the electronic quality is demonstrated. By introducing a Ge-alloyed CZTS nanoparticle layer at the bottom of the sputtered precursor, a Cu-rich and Sn-rich liquid phase forms at the high temperature sulfurization stage, which can effectively remove the detrimental near-horizontal grain boundaries and promote grain growth, thus greatly improving the carrier collection efficiency and reducing nonradiative recombination. The remaining liquid phase layer at the rear interface shows a high work function, acting as an effective hole transport layer. The modified morphology greatly increases the short-circuit current density and fill factor, enabling 10.3% efficient green Cd-free CZTS devices. This work unlocks a grain growth mechanism, advancing the morphology control of sulfide-based kesterite solar cells.

A Novel ECG-Derived Respiration Method Combining Frequency-Domain Feature and Interacting Multiple Model Smoother

OBJECTIVE

ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses.

APPROACH

In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: (1) the performance of extracted feature was verified against R-peak misalignment and noise. (2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database.

RESULTS

The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database.

SIGNIFICANCE

The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.

A pilot study of deep learning-based CT volumetry for traumatic hemothorax

PURPOSE

We employ nnU-Net, a state-of-the-art self-configuring deep learning-based semantic segmentation method for quantitative visualization of hemothorax (HTX) in trauma patients, and assess performance using a combination of overlap and volume-based metrics. The accuracy of hemothorax volumes for predicting a composite of hemorrhage-related outcomes - massive transfusion (MT) and in-hospital mortality (IHM) not related to traumatic brain injury - is assessed and compared to subjective expert consensus grading by an experienced chest and emergency radiologist.

MATERIALS AND METHODS

The study included manually labeled admission chest CTs from 77 consecutive adult patients with non-negligible (≥ 50 mL) traumatic HTX between 2016 and 2018 from one trauma center. DL results of ensembled nnU-Net were determined from fivefold cross-validation and compared to individual 2D, 3D, and cascaded 3D nnU-Net results using the Dice similarity coefficient (DSC) and volume similarity index. Pearson's r, intraclass correlation coefficient (ICC), and mean bias were also determined for the best performing model. Manual and automated hemothorax volumes and subjective hemothorax volume grades were analyzed as predictors of MT and IHM using AUC comparison. Volume cut-offs yielding sensitivity or specificity ≥ 90% were determined from ROC analysis.

RESULTS

Ensembled nnU-Net achieved a mean DSC of 0.75 (SD: ± 0.12), and mean volume similarity of 0.91 (SD: ± 0.10), Pearson r of 0.93, and ICC of 0.92. Mean overmeasurement bias was only 1.7 mL despite a range of manual HTX volumes from 35 to 1503 mL (median: 178 mL). AUC of automated volumes for the composite outcome was 0.74 (95%CI: 0.58-0.91), compared to 0.76 (95%CI: 0.58-0.93) for manual volumes, and 0.76 (95%CI: 0.62-0.90) for consensus expert grading (p = 0.93). Automated volume cut-offs of 77 mL and 334 mL predicted the outcome with 93% sensitivity and 90% specificity respectively.

CONCLUSION

Automated HTX volumetry had high method validity, yielded interpretable visual results, and had similar performance for the hemorrhage-related outcomes assessed compared to manual volumes and expert consensus grading. The results suggest promising avenues for automated HTX volumetry in research and clinical care.

Facet-selective etching trajectories of individual semiconductor nanocrystals

The size and shape of semiconductor nanocrystals govern their optical and electronic properties. Liquid cell transmission electron microscopy (LCTEM) is an emerging tool that can directly visualize nanoscale chemical transformations and therefore inform the precise synthesis of nanostructures with desired functions. However, it remains difficult to controllably investigate the reactions of semiconductor nanocrystals with LCTEM, because of the highly reactive environment formed by radiolysis of liquid. Here, we harness the radiolysis processes and report the single-particle etching trajectories of prototypical semiconductor nanomaterials with well-defined crystalline facets. Lead selenide nanocubes represent an isotropic structure that retains the cubic shape during etching via a layer-by-layer mechanism. The anisotropic arrow-shaped cadmium selenide nanorods have polar facets terminated by either cadmium or selenium atoms, and the transformation trajectory is driven by etching the selenium-terminated facets. LCTEM trajectories reveal how nanoscale shape transformations of semiconductors are governed by the reactivity of specific facets in liquid environments.

Lactate promotes metastasis of normoxic colorectal cancer stem cells through PGC-1α-mediated oxidative phosphorylation

Uneven oxygen supply in solid tumors leads to hypoxic and normoxic regions. Hypoxic cells exhibit increased secretion of lactate, which creates an acidic tumor microenvironment (TME). This acidic TME is positively associated with tumor metastasis. Despite the increased metastatic capacity of hypoxic cells, they are located relatively further away from the blood vessels and have limited access to the circulatory system. Studies have shown that cancer stem cells (CSCs) are enriched for tumor metastasis-initiating cells and generally undergo aerobic respiration, which could be enhanced by lactate. We therefore hypothesized that TME-derived lactate may promote the metastasis of normoxic CSCs. In the present study, the abundance of hypoxic and normoxic CSCs was analyzed in primary CRC tumors. It was found that the proportion of normoxic CSCs was positively associated with tumor stage. Using two human CRC cell lines, LoVo and SW480, and a patient-derived xenograft (XhCRC), it was found that treatment with lactate promoted normoxic CSC metastasis. Metabolism analysis indicated that, upon treatment with lactate, oxidative phosphorylation (OXPHOS) activity in normoxic CSCs was enhanced, whereas hypoxic CSCs were rarely altered. At the molecular level, the expression of peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α), a master regulator of lactate oxidation, was found to be elevated in normoxic CSCs. Furthermore, PGC-1α knockdown markedly reduced the metastatic potential of normoxic CSCs. Notably, both the PGC-1α-mediated OXPHOS activity and metastatic potential were impaired when hypoxia-inducible factor-1α (HIF-1α) was activated in normoxic CSCs. Together, these findings provide a therapeutic strategy against tumor metastasis through the targeting of PGC-1α and, thus, the suppression of lactate-feeding OXPHOS in normoxic CSCs may improve the therapeutic benefit of patients with cancer, particularly CRC.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Biogenic volatile organic compound emission patterns and secondary pollutant formation potentials of dominant greening trees in Chengdu, southwest China

Integral to the urban ecosystem, greening trees provide many ecological benefits, but the active biogenic volatile organic compounds (BVOCs) they release contribute to the production of ozone and secondary organic aerosols, which harm ambient air quality. It is, therefore, necessary to understand the BVOC emission characteristics of dominant greening tree species and their relative contribution to secondary pollutants in various urban contexts. Consequently, this study utilized a dynamic enclosure system to collect BVOC samples of seven dominant greening tree species in urban Chengdu, Southwest China. Gas chromatography/mass spectrometry was used to analyze the BVOC components and standardized BVOC emission rates of each tree species were then calculated to assess their relative potential to form secondary pollutants. We found obvious differences in the composition of BVOCs emitted by each species. Ficus virens displayed a high isoprene emission rate at 31.472 μgC/(gdw (g dry weight)•hr), while Cinnamomum camphora emitted high volumes of D-Limonene at 93.574 μgC/(gdw•hr). In terms of the BVOC emission rates by leaf area, C. camphora had the highest emission rate of total BVOCs at 13,782.59 μgC/(m²•hr), followed by Cedrus deodara with 5466.86 μgC/(m²•hr). Ginkgo biloba and Osmanthus fragrans mainly emitted oxygenated VOCs with lower overall emission rates. The high BVOC emitters like F. virens, C. camphora, and Magnolia grandiflora have high potential for significantly contributing to environmental secondary pollutants, so should be cautiously considered for future planting. This study provides important implications for improving urban greening efforts for subtropical Chinese urban contexts, like Chengdu.

Observed coupling between air mass history, secondary growth of nucleation mode particles and aerosol pollution levels in Beijing

Atmospheric aerosols have significant effects on the climate and on human health. New particle formation (NPF) is globally an important source of aerosols but its relevance especially towards aerosol mass loadings in highly polluted regions is still controversial. In addition, uncertainties remain regarding the processes leading to severe pollution episodes, concerning e.g. the role of atmospheric transport. In this study, we utilize air mass history analysis in combination with different fields related to the intensity of anthropogenic emissions in order to calculate air mass exposure to anthropogenic emissions (AME) prior to their arrival at Beijing, China. The AME is used as a semi-quantitative metric for describing the effect of air mass history on the potential for aerosol formation. We show that NPF events occur in clean air masses, described by low AME. However, increasing AME seems to be required for substantial growth of nucleation mode (diameter < 30 nm) particles, originating either from NPF or direct emissions, into larger mass-relevant sizes. This finding assists in establishing and understanding the connection between small nucleation mode particles, secondary aerosol formation and the development of pollution episodes. We further use the AME, in combination with basic meteorological variables, for developing a simple and easy-to-apply regression model to predict aerosol volume and mass concentrations. Since the model directly only accounts for changes in meteorological conditions, it can also be used to estimate the influence of emission changes on pollution levels. We apply the developed model to briefly investigate the effects of the COVID-19 lockdown on PM_2.5 concentrations in Beijing. While no clear influence directly attributable to the lockdown measures is found, the results are in line with other studies utilizing more widely applied approaches.