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We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

The mass concentration of atmospheric particulate matter (PM) has been continuously decreasing in the Beijing-Tianjin-Hebei region. However, health endpoints do not exhibit a linear correlation with PM mass concentrations. Thus, it is urgent to clarify the prior toxicological components of PM to further improve air quality. In this study, we analyzed the long-term oxidative potential (OP) of water-soluble PM2.5, which is generally considered more effective in assessing hazardous exposure to PM in Beijing from 2018 to 2022 based on the dithiothreitol assay and identified the crucial drivers of the OP of PM2.5 based on online monitoring of air pollutants, receptor model, and random forest (RF) model. Our results indicate that dust, traffic, and biomass combustion are the main sources of the OP of PM2.5 in Beijing. The complex interactions of dust particles, black carbon, and gaseous pollutants (nitrogen dioxide and sulfur dioxide) are the main factors driving the OP evolution, in particular, leading to the abnormal rise of OP in Beijing in 2022. Our data shows that a higher OP is observed in winter and spring compared to summer and autumn. The diurnal variation of the OP is characterized by a declining trend from 0:00 to 14:00 and an increasing trend from 14:00 to 23:00. The spatial variation in OP of PM2.5 was observed as the OP in Beijing is lower than that in Shijiazhuang, while it is higher than that in Zhenjiang and Haikou, which is primarily influenced by the distribution of black carbon. Our results are of significance in identifying the key drivers influencing the OP of PM2.5 and provide new insights for advancing air quality improvement efforts with a focus on safeguarding human health in Beijing.

Abstract The heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) is an important pathway in nitrate formation; however, its formation rate and relative contribution to total particulate nitrate (pNO3‐) are highly variable. Here we report that nocturnal pNO3‐ formation via N2O5 hydrolysis is dependent on the regime defined by the ratio of NO2 to O3. Nocturnal pNO3‐ formation via N2O5 hydrolysis is suppressed in an O3‐limited regime but enhanced in a NO2‐limited regime. The results have crucial implications for effective control of nitrate pollution in the future. An exclusive decrease in NO2 will decrease nocturnal pNO3‐ formation in a NO2‐limited regime but may be less effective or even increase nocturnal pNO3‐ formation in an O3‐limited regime.

Liu et al. found that the formation rate of particulate ammonium is slower in the atmosphere than that observed in the laboratory, while it is sped up due to an increase in aerosol acidity driven by an emission reduction of dust in North China Plain.

Ultrafine particles (UFPs) usually explosive growth during new particle formation (NPF) events. However, the risk of exposure to UFPs on NPF days has been ignored due to the prevalence of mass-based air quality standards. In this study, the daily deposited doses, i.e., the daily deposited particle number dose (DPNd), mass dose (DPMd), and surface area dose (DPSd), of ambient particles in the human respiratory tract in Beijing were evaluated based on the particle number size distribution (3 nm-10 µm) from June 2018 to May 2019 utilizing a Multiple-Path Particle Dosimetry Model (MPPD) after the hygroscopic growth of particles in the respiratory tract had been accounted for. Our observations showed a high frequency (72.6%) of NPF on excellent air quality days, with daily mean PM2.5 concentrations less than 35 μg m−3. The daily DPNd on excellent air quality days was comparable with that on polluted days, although the DPMd on excellent air quality days was as low as 15.6% of that on polluted days. The DPNd on NPF days was ~1.3 times that on non-NPF days. The DPNd in respiratory tract regions decreased in the order: tracheobronchial (TB) > pulmonary (PUL) > extrathoracic (ET) on NPF days, while it was PUL > TB > ET on non-NPF days. The number of deposited nucleation mode particles, which were deposited mainly in the TB region (45%), was 2 times higher on NPF days than that on non-NPF days. Our results demonstrated that the deposition potential due to UFPs in terms of particle number concentrations is high in Beijing regardless of the aerosol mass concentration. More toxicological studies related to UFPs on NPF days, especially those targeting tracheobronchial and pulmonary impairment, are required in the future.

Understanding the immunological characteristics of monocytes—including the characteristics associated with fibrosis—in severe coronavirus disease 2019 (COVID-19) is crucial for understanding the pathogenic mechanism of the disease and preventing disease severity. In this study, we performed single-cell transcriptomic sequencing of peripheral blood samples collected from six healthy controls and 14 COVID-19 samples including severe, moderate, and convalescent samples from three severely/critically ill and four moderately ill patients. We found that the monocytes were strongly remodeled in the severely/critically ill patients with COVID-19, with an increased proportion of monocytes and seriously reduced diversity. In addition, we discovered two novel severe-disease-specific monocyte subsets: Mono 0 and Mono 5. These subsets expressed amphiregulin (AREG), epiregulin (EREG), and cytokine interleukin-18 (IL-18) gene, exhibited an enriched erythroblastic leukemia viral oncogene homolog (ErbB) signaling pathway, and appeared to exhibit pro-fibrogenic and pro-inflammation characteristics. We also found metabolic changes in Mono 0 and Mono 5, including increased glycolysis/gluconeogenesis and an increased hypoxia inducible factor-1 (HIF-1) signaling pathway. Notably, one pre-severe sample displayed a monocyte atlas similar to that of the severe/critical samples. In conclusion, our study discovered two novel severe-disease-specific monocyte subsets as potential predictors and therapeutic targets for severe COVID-19. Overall, this study provides potential predictors for severe disease and therapeutic targets for COVID-19 and thus provides a resource for further studies on COVID-19.

Lv et al. associate the gut mycobiota with clinical features and the bacterial microbiota by comparing COVID-19 patients to those infected with H1N1 and healthy controls. They find that gut mycobiota dysbiosis occurs in both COVID-19 patients and those infected with H1N1 and that it does not improve until patients no longer require medical attention, providing insights into a better healthcare guideline.

Hepatitis B virus (HBV) can hijack the host bile acids (BAs) metabolic pathway during infection in cell and animal models. Additionally, microbiome was known to play critical role in the enterohepatic cycle of BAs. However, the impact of HBV infection and associated gut microbiota on the BA metabolism in chronic hepatitis B (CHB) patients is unknown. This study aimed to unveil the distinct BA profiles in chronic HBV infection (CHB) patients with no or mild hepatic injury, and to explore the relationship between HBV, microbiome and BA metabolism with clinical implications.Methods: Serum BA profiles were compared between CHB patients with normal ALT (CHB-NALT, n = 92), with abnormal ALT (CHB-AALT, n = 34) and healthy controls (HCs, n = 28) using UPLC-MS measurement. Hepatic gene expression in CHB patients were explored using previously published transcriptomic data. Fecal microbiome was compared between 30 CHB-NALT and 30 HCs using 16S rRNA sequencing, and key microbial function was predicted by PICRUSt analysis.Results: Significant higher percentage of conjugated BAs and primary BAs was found in CHB patients even without apparent liver injury. Combinatory BA features can discriminate CHB patients and HCs with high accuracy (AUC = 0.838). Up-regulation of BA importer Na+ taurocholate co-transporting peptide (NTCP) and down-regulation of bile salt export pump (BSEP) was found in CHB-NALT patients. The microbial diversity and abundance of Lactobacillus, Clostridium, Bifidobacterium were lower in CHB-NALT patients compared to healthy controls. Suppressed microbial bile salt hydrolases (BSH), 7-alpha-hydroxysteroid dehydrogenase (hdhA) and 3-dehydro-bile acid Delta 4, 6-reductase (BaiN) activity were found in CHB-NALT patients.Conclusion: This study provides new insight into the BA metabolism influenced both by HBV infection and associated gut microbiome modulations, and may lead to novel strategy for clinical management for chronic HBV infection.

Coronavirus disease 2019 (COVID-19) broke out and then became a global epidemic at the end of 2019. With the increasing number of deaths, early identification of disease severity and interpretation of pathogenesis are very important. Aiming to identify biomarkers for disease severity and progression of COVID-19, 75 COVID-19 patients, 34 healthy controls and 23 patients with pandemic influenza A(H1N1) were recruited in this study. Using liquid chip technology, 48 cytokines and chemokines were examined, among which 33 were significantly elevated in COVID-19 patients compared with healthy controls. HGF and IL-1β were strongly associated with APACHE II score in the first week after disease onset. IP-10, HGF and IL-10 were correlated positively with virus titers. Cytokines were significantly correlated with creatinine, troponin I, international normalized ratio and procalcitonin within two weeks after disease onset. Univariate analyses were carried out, and 6 cytokines including G-CSF, HGF, IL-10, IL-18, M-CSF and SCGF-β were found to be associated with the severity of COVID-19. 11 kinds of cytokines could predict the severity of COVID-19, among which IP-10 and M-CSF were excellent predictors for disease severity. In conclusion, the levels of cytokines in COVID-19 were significantly correlated with the severity of the disease in the early stage, and serum cytokines could be used as warning indicators of the severity and progression of COVID-19. Early stratification of disease and intervention to reduce hypercytokinaemia may improve the prognosis of COVID-19 patients.