Effects of Ni Content and Heat Treatment on the Properties, Microstructures, and Precipitates of Cu-0.2 wt% Be-x wt% Ni Alloys.

Cu-Be alloys exhibit excellent comprehensive performance in electrics, thermotics, and mechanics, and hence, they attract much attention. Among them, low-Be copper alloys are more environmentally friendly and promising. This study explores the effects of different Ni contents and heat treatment parameters on the properties, microstructures, and precipitates of Cu-0.2 wt% Be-x wt% Ni (0 < x < 2.0) alloys. The experimental results demonstrate that the fast cooling rate of cast alloys during solidification contributes to retention of the solute atoms in the copper matrix, which is beneficial for subsequent solid solution treatment. Furthermore, solid solution treatment slightly reduces the electrical conductivities, microhardness values, and compressive yield strengths of Cu-0.2 wt% Be-1.0/1.6 wt% Ni alloys. The optimal solution temperature and time are about 925 ℃ and 60 min, respectively. Aging treatment significantly increases the electrical conductivities, microhardness values, and compressive yield strengths of Cu-0.2 wt% Be-1.0/1.6 wt% Ni alloys. The best aging temperature is around 450 ℃. However, the properties of Cu-0.2 wt%Be-0.4 wt%Ni alloys remain unaffected by solution and aging treatments. Around x = 1.0, Cu-0.2 wt% Be-x wt% Ni alloys possess the best comprehensive properties, which are about 72%IACS of electrical conductivity, 241 HV of microhardness, and 281MPa of compressive yield strength, respectively. TEM and EDS analyses reveal that the precipitate evolution of Cu-0.2 wt% Be-1.0 wt% Ni alloys with aging time is GP zones → γ″ → γ'. Notably, a distinct double-peak age strengthening phenomenon emerges with Cu-0.2 wt% Be-1.0/1.6 wt% Ni alloys. The precipitation of plenty of GP zones at the early stage of aging should account for the first strengthening peak, and the strengthening mechanism transformation of the γ″ or γ' phase from shear to Orowan should induce the second strengthening peak. This work may help to design new low-Be copper alloys and their preparation processes.

Extended-age Out-of-sample Validation of Risk Stratification Index 3.0 Models Using Commercial All-payer Claims.

BACKGROUND: The authors previously reported a broad suite of individualized Risk Stratification Index 3.0 (Health Data Analytics Institute, Inc., USA) models for various meaningful outcomes in patients admitted to a hospital for medical or surgical reasons. The models used International Classification of Diseases, Tenth Revision, trajectories and were restricted to information available at hospital admission, including coding history in the previous year. The models were developed and validated in Medicare patients, mostly age 65 yr or older. The authors sought to determine how well their models predict utilization outcomes and adverse events in younger and healthier populations. METHODS: The authors' analysis was based on All Payer Claims for surgical and medical hospital admissions from Utah and Oregon. Endpoints included unplanned hospital admissions, in-hospital mortality, acute kidney injury, sepsis, pneumonia, respiratory failure, and a composite of major cardiac complications. They prospectively applied previously developed Risk Stratification Index 3.0 models to the younger and healthier 2017 Utah and Oregon state populations and compared the results to their previous out-of-sample Medicare validation analysis. RESULTS: In the Utah dataset, there were 55,109 All Payer Claims admissions across 40,710 patients. In the Oregon dataset, there were 21,213 admissions from 16,951 patients. Model performance on the two state datasets was similar or better than in Medicare patients, with an average area under the curve of 0.83 (0.71 to 0.91). Model calibration was reasonable with an R2 of 0.93 (0.84 to 0.97) for Utah and 0.85 (0.71 to 0.91) for Oregon. The mean sensitivity for the highest 5% risk population was 28% (17 to 44) for Utah and 37% (20 to 56) for Oregon. CONCLUSIONS: Predictive analytical modeling based on administrative claims history provides individualized risk profiles at hospital admission that may help guide patient management. Similar predictive performance in Medicare and in younger and healthier populations indicates that Risk Stratification Index 3.0 models are valid across a broad range of adult hospital admissions.

Risk Stratification Index 3.0, a Broad Set of Models for Predicting Adverse Events during and after Hospital Admission.

BACKGROUND: Risk stratification helps guide appropriate clinical care. Our goal was to develop and validate a broad suite of predictive tools based on International Classification of Diseases, Tenth Revision, diagnostic and procedural codes for predicting adverse events and care utilization outcomes for hospitalized patients. METHODS: Endpoints included unplanned hospital admissions, discharge status, excess length of stay, in-hospital and 90-day mortality, acute kidney injury, sepsis, pneumonia, respiratory failure, and a composite of major cardiac complications. Patient demographic and coding history in the year before admission provided features used to predict utilization and adverse events through 90 days after admission. Models were trained and refined on 2017 to 2018 Medicare admissions data using an 80 to 20 learn to test split sample. Models were then prospectively tested on 2019 out-of-sample Medicare admissions. Predictions based on logistic regression were compared with those from five commonly used machine learning methods using a limited dataset. RESULTS: The 2017 to 2018 development set included 9,085,968 patients who had 18,899,224 inpatient admissions, and there were 5,336,265 patients who had 9,205,835 inpatient admissions in the 2019 validation dataset. Model performance on the validation set had an average area under the curve of 0.76 (range, 0.70 to 0.82). Model calibration was strong with an average R 2 for the 99% of patients at lowest risk of 1.00. Excess length of stay had a root-mean-square error of 0.19 and R 2 of 0.99. The mean sensitivity for the highest 5% risk population was 19.2% (range, 11.6 to 30.1); for positive predictive value, it was 37.2% (14.6 to 87.7); and for lift (enrichment ratio), it was 3.8 (2.3 to 6.1). Predictive accuracies from regression and machine learning techniques were generally similar. CONCLUSIONS: Predictive analytical modeling based on administrative claims history can provide individualized risk profiles at hospital admission that may help guide patient management. Similar results from six different modeling approaches suggest that we have identified both the value and ceiling for predictive information derived from medical claims history.

Characteristics of secondary organic aerosols tracers in PM2.5 in three central cities of the Yangtze river delta, China.

Secondary organic aerosols (SOA) are important atmospheric pollutants that affect air quality, radiation, and human health. In this study, 14 typical SOA tracers were measured in PM2.5 collected from three central cities of the Yangtze River Delta (YRD) region in the winter of 2014 and the summer of 2015. Among the determined SOA tracers, α/ß-pinene SOA tracers contributed 55.9%, followed by isoprene SOA tracers (33.7%), anthropogenic benzene SOA tracer (6.4%) and ß-caryophyllene SOA tracer (4.0%). There was no significant difference in the concentration of individual SOA tracers among the three cities (p > 0.05), indicating a high degree of regional consistency. The concentrations of isoprene, α/ß-pinene, and toluene SOA tracers were significantly higher in summer than in winter. A correlation of SOA tracers with temperature implies that the isoprene and α/ß-pinene SOA tracers in summer were greatly boosted by plant emissions and the high DHOPA in summer could be attributed to evaporation of paint and solvent. In contrast, the elevated ß-caryophyllene SOA tracer in winter was likely associated with active biomass burning. Furthermore, we observed a close correlation of summer isoprene and α/ß-pinene SOA tracers with sulfate only in Shanghai, which verifies that biogenic SOA formation was facilitated by high concentration of sulfate. The ratios of MGA/MTLs and P/M were applied to reveal the impact of NOx on SOA formation and the aging degree of SOA, respectively. The MGA/MTLs ratios were comparable for the three cities, but much higher than the background value of this region as expected. The P/M ratios suggest that the aging degree of SOA in the YRD region was generally low, but the winter SOA were fresher than the summer SOA. Our research helps to understand pollution characteristics of SOA tracers in the urban agglomeration.

Predicting Successes and Failures of Clinical Trials With Outer Product-Based Convolutional Neural Network.

Despite several improvements in the drug development pipeline over the past decade, drug failures due to unexpected adverse effects have rapidly increased at all stages of clinical trials. To improve the success rate of clinical trials, it is necessary to identify potential loser drug candidates that may fail at clinical trials. Therefore, we need to develop reliable models for predicting the outcomes of clinical trials of drug candidates, which have the potential to guide the drug discovery process. In this study, we propose an outer product-based convolutional neural network (OPCNN) model which integrates effectively chemical features of drugs and target-based features. The validation results via 10-fold cross-validations on the dataset used for a data-driven approach PrOCTOR proved that our OPCNN model performs quite well in terms of accuracy, F1-score, Matthews correlation coefficient (MCC), precision, recall, area under the curve (AUC) of the receiver operating characteristic, and area under the precision-recall curve (AUPRC). In particular, the proposed OPCNN model showed the best performance in terms of MCC, which is widely used in biomedicine as a performance metric and is a more reliable statistical measure. Through 10-fold cross-validation experiments, the accuracy of the OPCNN model is as high as 0.9758, F1 score is as high as 0.9868, the MCC reaches 0.8451, the precision is as high as 0.9889, the recall is as high as 0.9893, the AUC is as high as 0.9824, and the AUPRC is as high as 0.9979. The results proved that our OPCNN model shows significantly good prediction performance on outcomes of clinical trials and it can be quite helpful in early drug discovery.

Air pollution increases human health risks of PM2.5-bound PAHs and nitro-PAHs in the Yangtze River Delta, China.

Identifying the nature and extent of atmospheric PM2.5-bound toxic organic pollutants is beneficial to evaluate human health risks of air pollution. Seasonal observations of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in the Yangtze River Delta (YRD) were investigated, along with criteria air pollutants and meteorological parameters. With the elevated PM2.5 level, the percentage of 4-ring PAHs and typical NPAH including 3-Nitrobiphenyl (3-NBP) and 2-Nitrofluoranthene (2-NFLT) increased by 19-40%. PM2.5-bound 2-NFLT was positively correlated with O3 and NO2, suggesting the contribution of atmospheric oxidation capacity to enhance the secondary formation of NPAHs in the atmosphere. Positive matrix factorization (PMF) analysis indicated that traffic emissions (44.9-48.7%), coal and biomass combustion (27.6-36.0%) and natural gas and volatilization (15.3-27.5%) were major sources of PAHs, and secondary formation (39.8-53.8%) was a predominant contributor to total NPAH concentrations. Backward trajectory analysis showed that air masses from North China transported to the YRD region increased PAH and NPAH concentrations. Compare to clean days, the BaP equivalent concentrations of total PAHs and NPAHs during haze pollution days were enhanced by 10-25 and 2-6 times, respectively. The Incremental Lifetime Cancer Risks (ILCRs) of PAHs by inhalation exposure also indicated high potential health risks in the YRD region. The results implied that the health risks of PM2.5-bound PAHs and NPAHs could be sharply enhanced with the increase of PM2.5 concentrations.

Prediction of drug-target binding affinity using similarity-based convolutional neural network.

Identifying novel drug-target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug-target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.

Deep learning method for comet segmentation and comet assay image analysis.

Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

Blocking C-Raf alleviated high-dose small-volume radiation-induced epithelial mesenchymal transition in mice lung.

The goal of this study was to develop a potential druggable target for lung injury after SABR through the small animal model. Utilising the model, a radiation dose of 70 Gy or 90 Gy was focally (small volume) delivered to the left lung of mice. The highly expressed phosphorylation form of C-Raf was discovered through a protein array experiment, with the protein being extracted from the area of radiated mouse lung tissue, and was confirmed by IHC and western blot. C-Raf activation, along with morphological change and EMT (Epithelial to Mesenchymal Transition) marker expression, was observed after radiation to the mouse type II alveolar cell line MLE-12. C-Raf inhibitor GW5074 was able to reverse the EMT in cells effectively, and was found to be dependent on Twist1 expression. In the animal experiment, pretreatment of GW5074 alleviated EMT and lung injury after 70 Gy radiation was focally delivered to the lung of mice. Conclusively, these results demonstrate that C-Raf inhibitor GW5074 inhibits high-dose small-volume radiation-induced EMT via the C-Raf/Twist1 signalling pathway in mice. Therefore, pharmacological C-Raf inhibitors may be used effectively as inhibitors of SABR-induced lung fibrosis.

The air pollution governed by subtropical high in a coastal city in Southeast China: Formation processes and influencing mechanisms.

To investigate the impact of the Western Pacific subtropical high (WPSH) on the air pollution episode of Xiamen, a coastal city in Southeastern China, this study focused on formation processes and influencing mechanisms of an air pollution episode from 17th to 23rd September 2017. The results showed that the WPSH fluctuated in this period and intensified this air pollution with local emissions. The episode was divided into four stages according to WPSH center locations to diagnose the air pollution. Visibility declined below 10 km twice while fine particulate matte (PM2.5) concentration was up to 89.05 µg/m3 during this episode. As a consequence of high temperature (28.33 ±â€¯1.25 °C) resulted from WPSH, atmospheric oxidation at high level (140.81 ±â€¯56.49 µg/m3) was the driving force of secondary aerosols generations. Oxidation determined photo-chemical reactions with the pathways of gas-phase and heterogeneous formation. Sulfate was formed from gas-phase oxidation by SO2 in daytime while heterogeneous reaction occurred at night. Nitrate generation was dominated by not only excess ammonium but also intense oxidation. Reconstruction light extinction results coupling with trajectories revealed that (NH4)2SO4, NH4NO3 and OM were the priority factors to the reduction of atmospheric visibility. These findings provided new insights of air pollution episode diagnosis and indicative function of WPSH impacts on local air quality in Southeast China.

Seasonal characteristics and health risks of PM2.5-bound organic pollutants in industrial and urban areas of a China megacity.

Organic pollutants are important harmful components in atmospheric fine particulate matters (PM2.5), health risks of which varied with temporal and spatial distributions. To clarify the characteristics of atmospheric organic pollution, the concentrations, sources, and human health risks of typical organic compositions in PM2.5 samples from both industrial and urban areas of Nanjing in eastern China were investigated monthly for a year. Results showed that, the concentrations of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and n-alkanes were higher in winter and spring than those in summer and autumn. The organic pollution was slightly higher in industrial than urban area, though the PAHs in autumn and the n-alkanes in warm season (summer and autumn) were higher in urban area. With regards to the pollutant sources, the atmospheric PAHs were dominated by motor vehicle exhaust in the urban area, and combined with coal combustion emission in the industrial area. Airborne n-alkanes were mainly from biological source accompanied by fossil fuel combustion in industrial area. The PM2.5-bound PAHs indicated higher risks to adults in industrial area than in urban area with the seasonal patterns: winter > spring > autumn > summer. More attention should be paid to the health risks of exposure to organic pollutants accumulated in PM2.5 during cold season. Controlling vehicle emissions might be the key measure for alleviating atmospheric PAHs and n-alkanes pollution in megacities, while coal purification can be an effective control method in industrial areas.

Risk factors to identify patients who may not benefit from whole brain irradiation for brain metastases - a single institution analysis.

BACKGROUND: Radiotherapy plays a major role in the management of brain metastases. This study aimed to identify the subset of patients with multiple brain metastases who may not benefit from whole brain irradiation (WBI) due to a short survival time regardless of treatment. METHODS: We analyzed a total of 339 patient records with brain metastases treated with whole brain radiotherapy from January 2009 to January 2016. External beam radiotherapy techniques were used to deliver 33 Gy in 11 fractions (4 fractions per week) to the whole brain. Eight clinical factors with a potential influence on survival were investigated using the Kaplan-Meier method. All factors with a P < 0.05 in univariate analysis were entered into multivariate analysis using Cox regression. RESULTS: In the present series of 339 patients, median survival time was 2.5 months (M; range, 0-61 months). Four risk factors Karnofsky Performance Score (KPS) < 70, age > 70, > 3 of metastases intracranial, uncontrolled primary tumor) were identified that were significant and negatively correlated with median survival time. Patients with no risk factors had a median survival of 4.7 M; one risk factor, 2.5 M; two risk factors, 2.3 M; and 3-4 risk factors, 0.4 M (p < 0.00001). CONCLUSIONS: Patients with identified risk factors might have a negatively impacted overall survival after WBI. Accordingly, patients who will not benefit from WBI can be easily predicted if they have 3-4 of these risk factors.

MicroRNA-26a-CD36 signaling pathway: Pivotal role in lipid accumulation in hepatocytes induced by PM2.5 liposoluble extracts.

Exposure to ambient particular matters (PM) has been associated with the development of non-alcoholic fatty liver disease (NAFLD), but the underlying mechanism remains unclear. Given that microRNA (miRNA) is recognized as a key regulator of lipid metabolism and a potential mediator of environmental cues, this study aimed to explore the role of miRNA-mRNA regulation underlying abnormal lipid metabolism triggered by PM2.5liposoluble extracts. We confirmed that 72-h exposure to liposoluble extracts of PM2.5 from Nanjing at 25 µg/cm2 induced lipid accumulation in HepG2 cells by promoting uptake of free fatty acids (FFAs). Notably, lipid accumulation induced by PM2.5 liposoluble extracts was associated with decreased expression of miR-26a and consequent upregulation of fatty acid translocase (FAT, also known as CD36). Using gain- and loss-of-function assays, we demonstrated that miR-26a negatively regulated CD36 to mediate lipid accumulation in HepG2 cells. We further confirmed that miR-26a directly acted on the 3' untranslated region (3'UTR) of CD36. Furthermore, overexpression of miR-26a abolished steatosis in HepG2 cells treated with PM2.5 liposoluble extracts by suppressing CD36. In addition, we demonstrated that PM2.5 liposoluble extracts caused inflammation in HepG2 cells by raising p65 phosphorylation, thereby fuelling the transition from simple non-alcoholic fatty liver to non-alcoholic steatohepatitis. In conclusion, this study demonstrated a novel mechanism by which miR-26a-CD36 pathway mediated lipid accumulation induced by PM2.5 liposoluble extracts in hepatocytes. Lipid accumulation and inflammation induced by PM2.5 liposoluble extracts implied the potential role of PM2.5 in developing NAFLD.

Characteristics of atmospheric volatile organic compounds (VOCs) at a mountainous forest site and two urban sites in the southeast of China.

Volatile organic compounds (VOCs) are important trace gases in the atmosphere, affecting air quality (e.g. ozone and secondary organic aerosol formation) and human health. To understand the emission, transport and chemistry of VOCs in the southeast of China (Fujian Province), a campaign was conducted in summer and winter of 2016 at three contrasting sites in close proximity. One measurement site (Mt. Wuyi) is a mountainous forest site (1139 m a.s.l.) located in a natural reserve, while the other two sites (Fuzhou, Xiamen) are coastal urban sites with high population and vehicle density. Comparison of VOCs at these three sites provides a valuable perspective on regional air pollution and transport. Many of the measured alkanes, alkenes and aromatics exhibited clear seasonal and diurnal patterns, driven by variations of hydroxyl (OH) radicals, which is the predominant oxidant of VOCs in the atmosphere. By examining tracer-tracer correlations for VOCs, variability-lifetime analysis and 36 h backward trajectories, strong emissions from vehicular exhaust, liquefied petroleum gas (LPG) and solvent usage were identified as key sources in Fuzhou and Xiamen, whereas at Mt. Wuyi the main emission sources were local emissions (e.g. biomass burning) in summer and long-range transport in winter. The results indicate that natural sites could be impacted strongly by surrounding urbanization. Isoprene and propylene in summer and propylene in winter contributed the most to ozone formation at the three sites. The data in this study provides a useful benchmark for future research on air quality monitoring and emission sources in the region.

Composition, mixing state, and size distribution of single submicron particles during pollution episodes in a coastal city in southeast China.

Size-resolved particle composition, size distribution, and mixing state were characterized at the single-particle level during two air pollution episodes during 12-25 January, 2016 in a coastal city in southeast China. The two pollution episodes occurred under distinct meteorological conditions (i.e., different wind speeds, relative humidity, and backward trajectories); thus, they were assigned to stagnation and transport episodes, respectively. Single-particle data, obtained from single-particle aerosol mass spectrometry (SPAMS), showed that carbonaceous particles were the predominant particles during the whole study period, accounting for more than 60% of the total particles. However, the number fractions of carbonaceous particles and nitrate-containing particles significantly increased in the stagnation episode, while the number fractions of sulfate- and ammonium-containing particles both increased in the transport episode compared to the levels over the whole study period. The potassium-rich (K-rich) particle class was more abundant and more strongly mixed with sulfate in the transport episode, which indicates the impact of biomass burning emissions and the subsequent aging process by acquiring sulfate during transport. The particle classes (e.g., carbonaceous and K-rich classes) had a broader size distribution during the pollution episodes than during the clean episode. The diameters of the size distribution peak for all particle classes (except for dust class) were observed to be larger in the transport episode than in the stagnation episode. This suggests that the particles underwent an extensive aging process through the addition of sulfate and ammonium during transport, leading to the growth of particles.

Secondary organic aerosol of PM2.5 in a mountainous forest area in southeastern China: Molecular compositions and tracers implication.

Secondary organic aerosol (SOA) plays an important role in global climate change and air quality. PM2.5 (particles with aerodynamic diameters ≤2.5 µm) samples were collected at a mountainous forest site (Mt. Wuyi) in southeastern China between November 2015 and July 2016. Fourteen PM2.5-bound SOA tracers, including isoprene, α/ß­pinene, ß­caryophyllene, and toluene, were measured using the gas-chromatography-mass-spectrometry method. The total concentrations of the isoprene, α/ß­pinene, ß­caryophyllene, and toluene SOA tracers were 45.28 ±â€¯65.52, 30.66 ±â€¯24.44, 5.99 ±â€¯7.25, and 0.62 ±â€¯0.72 ng m-3, respectively. The isoprene SOA tracers exhibited the highest concentration (145.97 ±â€¯53.78 ng m-3) and accounted for 76 ±â€¯9% of the total concentration of SOA tracers in summer. In fall-winter, the mass fraction of 2­methylglyceric acid was significantly enhanced because of the lower temperature and higher NOx level. As later-generation products of α/ß­pinene tracers, high proportions of 3­hydroxyglutaric acid and 3­methyl­1,2,3 butanetricarboxylic acid were observed on Mt. Wuyi, suggesting that the aerosols were highly oxidized. Biomass burning events affected by local and regional sources were identified by analyzing typical SOA tracers. Significant positive correlation (R2 = 0.74) was found between the ß­caryophyllene tracer and levoglucosan. The average concentration of secondary organic carbon (SOC) as estimated from SOA tracers was 1.46 µgC m-3. The isoprene SOC accounted for 70% of the total SOC in summer, whereas the ß­caryophyllene SOC was the predominant component in winter. Meanwhile, the estimated toluene SOC accounted for 11.6% of the total SOC during the study period. The study helps understanding the characteristics and the formation of SOA in a mountainous forest area of southeastern China.

Source identification of PM2.5 at a port and an adjacent urban site in a coastal city of China: Impact of ship emissions and port activities.

Daily PM2.5 samples were collected simultaneously at an urban site (UB) and a nearby port-industrial site (PI) on the coast of southeastern China from April 2015 to January 2016. The PM2.5 mass concentration at the PI (51.9µgm-3) was significantly higher than that at the UB. The V concentration at the PI was also significantly higher and well-correlated to the urban value, which suggests that shipping emissions had a significant impact on the PI and, to a lesser extent, on the urban area. A positive matrix factorization (PMF) analysis showed that secondary aerosols were the dominant contribution of PM2.5 at both sites (36.4% at the PI and 27.2% at the UB), while the contribution of industry and ship emissions identified by V, Mn, and Ba at the PI (26.1%) were double those at the UB. The difference in each source contribution among the trajectory clusters that included significant differences and insignificant differences from the UB to the PI provided insight into the role of local impacts. With regards to the UB, local potential sources play important roles in industry and ship emissions, traffic emissions, fugitive dust, and in their contributions to secondary aerosols. A conditional probability function further revealed that the ship emissions and port activities distributed in the NE, E, and SSE wind sectors were responsible for the source contributions of industry and ship emissions and secondary aerosols at the UB. This study provides an example of investigating the impact of ship emissions and port activities on the surrounding air environment using land-based measurements.

Spatiotemporal distribution and source apportionment of low molecular weight organic acids in wet precipitation at a coastal city, China.

To investigate the characteristics and sources of low molecular weight (LMW) organic acids in wet precipitation at a coastal city, Xiamen, a total of 313 rainwater samples were collected at seven different functional areas from September 2012 to August 2013. Spatiotemporal characteristics of LMW organic acids as well as pH and electrical conductivity were analyzed. Meanwhile, air mass clusters in different seasons and the positive matrix factorization (PMF) source apportion model were comprehensively used to identify the sources of organic acids. In conclusion, the volume-weighted mean (VWM) concentration of formic (3.20 µmol/L), acetic (1.84 µmol/L), lactic (0.44 µmol/L), and oxalic acid (0.53 µmol/L) were obtained, which jointly contributed to 4.33% of the total free acidity (TFA). At the same time, the highest wet deposition flux of LMW organic acids and contribution of that to TFA were achieved at the forest protection area during growing season in Xiamen. In addition, biogenic emissions (77.12%), sea salts (13.77%), regional agriculture activities (3.92%), soil emissions (2.56%), biomass burning (1.47%), and secondary aerosols (1.15%) were determined as the source of LMW organic acids. Besides, the dominancy of biomass burning via long-range transport in non-growing season (NGS) and the contribution of biogenic emission in growing season (GS) were recognized. Finally, the considerable influence of sea salts on the LMW organic acids (13.77%) in Xiamen was quantified, especially for oxalic acid.

Pattern of atmospheric mercury speciation during episodes of elevated PM2.5 levels in a coastal city in the Yangtze River Delta, China.

Measurement of atmospheric mercury speciation was conducted in a coastal city of the Yangtze River Delta, China from July 2013 to January 2014, in conjunction with air pollutants and meteorological parameters. The mean concentrations of gaseous elemental mercury (GEM), particulate bound mercury (HgP) and reactive gaseous mercury (RGM) were 3.26 ± 1.63 ng m-3, 659 ± 931 pg m-3, and 197 ± 246 pg m-3, respectively. High percentages of HgP during haze days were found, due to the increase in direct emissions and gas-particle partitioning of RGM. The average gas-particle partitioning coefficients (Kp) during moderate or severe haze days (PM2.5 > 150 µg m-3) were obviously decreased. GEM and HgP were positively correlated with PM2.5, SO2, NO2 and CO, suggesting a significant contribution of anthropogenic sources. Elevated HgP concentrations in cold seasons and in the morning were observed while RGM exhibited different seasonal and diurnal pattern. The ratio of HgP/SO2 and Pearson correlation analysis suggested that coal combustion was the main cause of increasing atmospheric Hg concentrations. The monitoring site was affected by local, regional and interregional sources. The back trajectory analysis suggested that air mass from northwest China and Huabei Plain contributed to elevated atmospheric Hg in winter and autumn, while southeast China with clean air masses were the major contributor in summer.