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AlMgB14–TiB2 composites with ideal structures are successfully prepared by field activated and pressure assisted synthesis. The effects of different TiB2 contents on the relative hardness and toughness of the composites were investigated. The results showed adding TiB2 could both increase the hardness of AlMgB14 and improve the fracture toughness. The TiB2 contributed more to the hardness than to the toughness. The microstructure analysis shows that the main toughening mechanisms of AlMgB14–TiB2 composites are hard phase dispersion strengthening, high-strength interface bonding and the high elastic modulus of TiB2. Therefore, reducing the particle size of TiB2 to nanoscale is an efficient way to improve the toughness and hardness. The results of friction and wear experiment at room temperature have shown that the addition of TiB2 into AlMgB14 enhances the abrasion–resistant property.

In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM

In this paper, the microstructure, electromagnetic shielding properties and mechanical properties of Mg-6Zn-3Sn-0.5Cu alloys are studied. As indicated from the results, the phases in the as-cast alloy comprise α-Mg, MgZnCu, Mg2Sn and Mg2Zn3 phases. After homogenization, Mg2Sn and Mg2Zn3 phases are decomposed, but the MgZnCu phase remains. During extrusion, complete dynamic recrystallization (DRX) occurs, and the spherical Mg2Sn phase precipitates dynamically. Due to considerable rod-like β′1 phases precipitate by complying with the [0001] Mg direction, the mechanical properties of aged alloys are remarkably enhanced. The peak-aged alloy achieves the tensile strength of 366 MPa, the yield strength of 358 MPa and the elongation of 7%. With the increase in the size of the β′1 phase, the mechanical properties of the over-aged alloy are reduced. Since the phases precipitate, the electrical conductivity of the alloy is enhanced, and the internal reflective interface increases, so the aged alloys exhibit improved electromagnetic shielding performance. The electromagnetic shielding efficiency of the peak-aged alloy exceeds 105 dB in the range of 30–1500 MHz, showing the best match between mechanical and electromagnetic shielding properties.

Juvenile hormone esterase (JHE) is a key enzyme for insects, playing an important role in the regulation of insect growth, development, diapause and reproduction. We identified a complete putative JHE of Cydia pomonella (CpJHE-like) which is comprised of a 1 761 bp coding sequence (CDS) encoding 587 amino acid residues from the transcriptome data. The deduced protein sequence of CpJHE-like showed the highest identity of 60.44% with the Adoxophyes honmai JHE (AhJHE) and the minimal identity of 25.81% with Aedes aegypti JHE (AaJHE). CpJHE-like exhibited all the seven typical motifs of the functional JHEs and had the highly consistent tertiary structure with Manduca sexta JHE (MsJHE). Phylogenetic analysis showed that the CpJHE-like was close to two JHEs from the family Tortricidae. The CpJHE-like transcript level take a leap in the 3-day-old fifth instar larva, increased about 300-fold compared to the basal level. Tissue-specific expression profile showed that the CpJHE-like transcript was expressed mainly in the fat body. This study indicates that the CpJHE-like is the functional JHE, which may play vital roles in the development and reproduction of C. pomonella.

Source apportionment studies have been performed on online receptor datasets in recent years (called online source apportionment), including mass spectra and online chemical dataset. Single particle aerosol mass spectrometry (SPAMS), an important online technique, has the ability to analyze mass spectrum (MS) and particular size information of a single particle in real time. Clustering methods have been widely applied to MS dataset to investigate the sources of particles, although the receptor models are the common tools to probe the particle sources based on the receptor dataset. This work developed a new method (SPAMS-RM) that employed the receptor model (RM) on an MS dataset from SPAMS to identify particle sources. Particles were measured by SPAMS from July 14 to August 15, 2015, at an urban site in Tianjin, China. Multilinear Engine-2 (ME2) and adaptive resonance theory-based neural networks-2a (ART-2a) were separately used to analyze the single particle MS dataset. This work also evaluated the performance of SPAMS-RM method. Concentrations of chemical components of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) and gaseous pollutants were measured by independent online instruments (1 h resolution). Source apportionment was separately conducted using two receptor models, Positive Matrix Factorization (PMF) and ME2, based on the 1 h resolution chemical dataset. This method was called online chemical source apportionment (OCSA-RM). ART-2a obtained 19 clusters that merged into five major classes: carbon species, rich-K, sea salt, crustal dust, and industrial metals. SPAMS-RM identified eight sources by interpreting the MS characteristic of factors and investigating the relationship of the temporal trends of factor contributions, chemical species, gaseous pollutants, and particle clusters. OCSA-ME2 and OCSA-PMF both identified seven factors. Source apportionment results between SPAMS-RM and OCSA-ME2/PMF were compared. Each method identified coal combustion, biomass burning, sea salt, nitrate source, sulfate source, vehicle emission, and crustal dust. The SPAMS-RM results showed that nitrate source was the most significant contributor (34%) to the PM followed by sulfate source (17%), coal combustion (14%), crustal dust (11%), vehicle emission (10%), biomass burning-OCEC (7%), and industrial activities & sea salt (4%). Some differences between SPAMS-RM and OCSA-ME2/PMF results existed and might be due to chemical analysis methods and sampling methods. ME2 was used for the first time to identify the PM sources based on the MS dataset from SPAMS and demonstrated its capability when coupled with MS dataset from SPAMS to apportion the source of PM.

Aerosol acidity is closely related to particle properties and the explosive growth of secondary particles. Aerosol pH is difficult to measure directly but can be estimated indirectly by thermodynamic equilibrium modeling. ISORROPIA-Ⅱ is one of the most commonly used thermodynamic models and includes different modes (forward and reverse) and aerosol states (stable and metastable). Studies have shown that the calculated pH results vary with the selected mode and phase state. In addition to the selection of modes and phases, there are also other factors that influence the modeling results. In order to explore the appropriate mode and phase selection of ISORROPIA-Ⅱ as well as the factors influencing the model results under the air pollution characteristics of typical Chinese cities, the simulation results of different modes and aerosol states were analyzed by using online hourly data for Tianjin. The results showed that the pH calculations using the forward mode and metastable state were satisfactory at a higher RH. With increased temperature, the pH, aerosol water content, and concentration proportion in the aerosol phase of semi-volatile components all decreased. RH affected aerosol pH by influencing the aerosol water content and concentration of semi-volatile components. An increased cation concentration led to an increased pH and NH

Ammonium is one of the dominant inorganic water-soluble ions in fine particulate matter (PM2.5). In this study, source apportionment and thermodynamic equilibrium models were used to analyze the re...

Tianjin is located in the Beijing-Tianjin-Hebei region. Recently, particulate matter pollution has received wide attention; therefore, studying the chemical composition and sources of particulate matter in the atmospheric environment is of great significance. To clarify the mixed state and possible sources of particulate matter in the summer ambient air in Tianjin, this study used single particle aerosol mass spectrometer (SPAMS) to collect 209887 samples. Particle size and complete spectrometry information were collected in July 2017. A total of 369 particle classes were obtained with respect to clustering particles with similarities in mass spectrometry characteristics using ART-2a. Then, according to the similarity in the chemical composition (mass spectrometry) of the categories, 19 particulate matter categories were artificially merged: K-EC (0.20%), K-EC-Sec (0.18%), K-NO

To investigate the influences of anthropogenic activities on carbon aerosols, especially on water-soluble organic carbon (WSOC), PM2.5 samples were collected at an urban site in a northern city of China during Spring Festival (SF), heating season (HS), and non-heating season (NHS). Carbonaceous species and ions (Ca2+, SO42-, NO3-, etc.) were analyzed. Mass concentrations of WSOC and WSIC exhibited higher levels in SF and HS, and high WSOC/OC ratios (67.4%) on average were found. Stronger correlations between WSOC and K+, Cl- occurred in SF, which might due to contributions of firework emissions. Six major sources of PM2.5 were quantified by PMF model, which contributed in aerosol mass differently in different periods: biomass & firework burning exhibited higher contribution (11.2%) in SF; crustal dust accounted for 19.4% during NHS; secondary particles contributed most (41.0%) in HS; during SF and HS, coal combustion devoted more to aerosol mass. Contributions to WSOC were in the order of vehicular exhaust (41.0% of WSOC) > coal combustion (29.3%) > secondary formation (17.0%) > biomass & firework burning (12.7%). The 82.0% of WIOC were from coal combustion and the rest were devoted by vehicular exhaust. Obvious peaks of firework burning contributions to WSOC were observed on SF's Eve and Lantern Festival. Coal combustion contributed to organic carbons highly in SF and HS. Results implied that anthropogenic activities contributions, like firework burning and coal combustion, significantly influenced the levels of PM2.5 and WSOC.

In this study, samples of three typical coal combustion source types, including Domestic bulk coal combustion (DBCC), Heat supply station (HSS), and Power plant (PP) were sampled and large sets of their mass spectra were obtained and analyzed by SPAMS during winter in a megacity in China. A primary goal of this study involves determining representative size-resolved single particle mass spectral signatures of three source types that can be used in source apportionment activities. Chemical types describe the majority of the particles of each source type were extracted by ART-2a algorithm with distinct size characteristics, and the corresponding tracer signals were identified. Mass spectral signatures from three source types were different from each other, and the tracer signals were effective in distinguishing different source types. A high size-resolution source apportionment method were proposed in this study through matching sources' mass spectral signatures to particle spectra in a twelve days ambient sampling to source apportion the particles. Contributions of three source types got different size characteristics, as HSS source got higher contribution in smaller sizes, But PP source got higher contributions as size increased. Source contributions were also quantified during two typical haze episodes, and results indicated that HSS source (for central-heating) and DBCC source (for domestic heating and cooking) may contribute evidently to pollution formation.

PM2.5 is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM2.5. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM2.5 were collected at Tianjin, a megacity in northern China. The ambient PM2.5 dataset, source information, and gas-to-particle ratios (such as SO2/PM2.5, CO/PM2.5, and NOx/PM2.5 ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM2.5 was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

Concentrations of traffic-related air pollutants are frequently higher within commuting vehicles than in ambient air. Pollutants found within vehicles may include those generated by tailpipe exhaust, brake wear, and road dust sources, as well as pollutants from in-cabin sources. Source-specific pollution, compared to total pollution, may represent regulation targets that can better protect human health. We estimated source-specific pollution exposures and corresponding pulmonary response in a panel study of commuters. We used constrained positive matrix factorization to estimate source-specific pollution factors and, subsequently, mixed effects models to estimate associations between source-specific pollution and pulmonary response. We identified four pollution factors that we named: crustal, primary tailpipe traffic, non-tailpipe traffic, and secondary. Among asthmatic subjects (N=48), interquartile range increases in crustal and secondary pollution were associated with changes in lung function of −1.33% (95% confidence interval (CI): −2.45, −0.22) and −2.19% (95% CI: −3.46, −0.92) relative to baseline, respectively. Among non-asthmatic subjects (N=51), non-tailpipe pollution was associated with pulmonary response only at 2.5 hours post-commute. We found no significant associations between pulmonary response and primary tailpipe pollution. Health effects associated with traffic-related pollution may vary by source, and therefore some traffic pollution sources may require targeted interventions to protect health.

Day and night PM 2.5 samples were collected at coastal and inland stations in a megacity in China. Temporal, spatial, and directional characteristics of PM 2.5 concentrations and compositions were investigated. Average PM 2.5 concentration was higher at inland (153.28 μg/m 3 ) than at coastal (114.46 μg/m 3 ). PM 2.5 were significantly influenced by season and site but insignificantly by diurnal pattern. Sources were quantified by a two-way and a newly developed three-way receptor models conducted using ME2. Secondary sulfate and SOC (SSS SN and VE presented higher fractions at inland. Higher SS&SOC, SN and CC&BB in winter might be attributed to intensive coal combustion for residential warming and to stable meteorological conditions.

Source and ambient samples were collected in a city in China that uses considerable biofuel, to assess influence of biofuel combustion and other sources on particulate matter (PM). Profiles and size distribution of biofuel combustion were investigated. Higher levels in source profiles, a significant increase in heavy-biomass ambient and stronger correlations of K+, Cl−, OC and EC suggest that they can be tracers of biofuel combustion. And char-EC/soot-EC (8.5 for PM2.5 and 15.8 for PM10 of source samples) can also be used to distinguish it. In source samples, water-soluble organic carbon (WSOC) were approximately 28.0%–68.8% (PM2.5) and 27.2%–43.8% (PM10) of OC. For size distribution, biofuel combustion mainly produces smaller particles. OC1, OC2, EC1 and EC2 abundances showed two peaks with one below 1 μm and one above 2 μm. An advanced three-way factory analysis model was applied to quantify source contributions to ambient PM2.5 and PM10. Higher contributions of coal combustion, vehicular emission, nitrate and biofuel combustion occurred during the heavy-biomass period, and higher contributions of sulfate and crustal dust were observed during the light-biomass period. Mass and percentage contributions of biofuel combustion were significantly higher in heavy-biomass period. The biofuel combustion attributed above 45% of K+ and Cl−, above 30% of EC and about 20% of OC. In addition, through analysis of source profiles and contributions, they were consistently evident that biofuel combustion and crustal dust contributed more to cation than to anion, while sulfate & SOC and nitrate showed stronger influence on anion than on cation.

Laboratory-based or in situ PM2.5 source profiles may not represent the pollutant composition for the sources in a different study location due to spatially and temporally varying characteristics, such as fuel or crustal element composition, or due to differences in emissions behavior under ambient versus laboratory conditions. In this work, PM2.5 source profiles were estimated for 20 sources using a novel optimization approach that incorporates observed concentrations with source impacts from a chemical transport model (CTM) to capture local pollutant characteristics. Nonlinear optimization was used to minimize the error between source profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal variability was seen for coal combustion, dust, fires, metals processing, and other source profiles when compared to the reference profiles, with variability in species fractions over 400% (calcium in dust) compared to mean contributions of the same species. Revised profiles im...

A hybrid model based on Positive Matrix Factorization (PMF) and Health Risk Assessment Model for assessing risks associated with sources of perfluoroalkyl substances (PFASs) in water was for the first time established, and applied at Dianchi Lake to test its applicability. The new method contains two stages: (1) The sources to PFASs were apportioned by PMF model and (2) the contribution of health risks from each source was calculated by the new hybrid model. Two factors were extracted by PMF, with Factor 1 identified as aqueous fire-fighting foams (AFFFs) source, and Factor 2 as fluoropolymer (FP) manufacturing and processing & perfluorooctanoic acid production source. Health risk of PFASs in the water assessed by Health Risk Assessment Model was 9.54 × 10−7 a−1 on average, showing no obvious adverse effects to human health. The two sources' risks estimated by the new hybrid model ranged from 2.95 × 10−10 to 6.60 × 10−6 a−1 and from 1.64 × 10−7 to 1.62 × 10−6 a−1 respectively. The new hybrid model can provide useful information on the health risks of PFASs sources, which is helpful for pollution control and environmental management. This article is protected by copyright. All rights reserved

In this study, single particle mass spectra signatures of both coal burning boiler and biomass burning boiler emitted particles were studied. Particle samples were suspended in clean Resuspension Chamber, and analyzed by ELPI and SPAMS simultaneously. The size distribution of BBB (biomass burning boiler sample) and CBB (coal burning boiler sample) are different, as BBB peaks at smaller size, and CBB peaks at larger size. Mass spectra signatures of two samples were studied by analyzing the average mass spectrum of each particle cluster extracted by ART-2a in different size ranges. In conclusion, BBB sample mostly consists of OC and EC containing particles, and a small fraction of K-rich particles in the size range of 0.2–0.5 μm. In 0.5–1.0 μm, BBB sample consists of EC, OC, K-rich and Al_Silicate containing particles; CBB sample consists of EC, ECOC containing particles, while Al_Silicate (including Al_Ca_Ti_Silicate, Al_Ti_Silicate, Al_Silicate) containing particles got higher fractions as size increase. The similarity of single particle mass spectrum signatures between two samples were studied by analyzing the dot product, results indicated that part of the single particle mass spectra of two samples in the same size range are similar, which bring challenge to the future source apportionment activity by using single particle aerosol mass spectrometer. Results of this study will provide physicochemical information of important sources which contribute to particle pollution, and will support source apportionment activities.

Ambient air pollution in China has a significant spatial variation due to the uneven development and different energy structures. This study characterized ambient pollution of parent and nitrated polycyclic aromatic hydrocarbons (PAHs) through a 1-year measurement in two megacities in southwest China where regional PM2.5 levels were considerably lower than other regions. Though the annual average BaP levels in both two cities were below the national standard of 1.0 ng/m3, however, by taking other PAHs into account, PAHs pollution were serious as indicated by high BaP equivalent concentrations (BaPEQ) of 3.8 ± 2.6 and 4.4 ± 1.9 ng/m3, respectively. Risk assessment would be underestimated by nearly an order of magnitude if only using BaP in risk assessment compared to the estimation based on 26 PAHs including 16 priority and 10 non-priority isomers targeted in this study. Estimated incremental lifetime cancer risks (ILCR) were comparable at two cities, at about 330–380 persons per one million, even though the mass concentrations were significantly different. Nitrated PAHs showed distinct temporal and site differences compared to the parent PAHs. High cancer risks due to inhalation exposure of PAHs and their polar derivatives in the low PM2.5-pollution southwest China suggest essential and effective controls on ambient PAHs pollution in the region, and controls should take potential health risks into account instead of solely mass concentration.

When planning short-term and long-term measurement campaigns of particulate matter (PM), parameters such as sampling period, time resolution, sampling number, etc. are vital. To study their influence and to provide suggestion for the sampling plan of PM source apportionment (SA), ambient and synthetic speciated datasets (including a high time-resolution dataset and a long-term daily dataset) were studied. First, aiming at studying the sampling period required to generate representative and reliable results for SA, high time-resolution ambient samples were collected by online instruments in a megacity in China. Datasets with different sampling periods (four months, two months, one month, two weeks and one week) were modeled by the Positive Matrix Factorization (PMF). Compared with four month results, AAEs (percent absolute errors between true and estimated contributions) ranged from 11.2 to 27.2% (two months), 19.8–44.5% (one month), 21.0–45.9% (two weeks) and 23.9–44.6% (one week), indicating that divergence increased with decreasing sampling periods. To systematically evaluate this problem and investigate if the increasing time resolutions in a short period could enhance the modeling performance, synthetic datasets were constructed. Results revealed that a sufficient sampling period is required to ensure stable results; without sufficient sampling period, the contributions cannot be reliably estimated, even if the number of samples is large. Then, to explore the influence of variability absences, long-term daily datasets with various variability absences were apportioned and compared. The summed AAEs were 102.2% (no winter), 73.6% (no weekend), 138.7% (no weekday) and 165.6% (no autumn, winter or weekends). This general increase of AAEs can indicate that uncertainty enhanced with the increase in variability absences. When planning short-term measurement campaigns, except for number of samples, sampling period that involves sufficient source cycles has significant implications; when planning long-term sampling, more intensive sampling would increase the model performance.

Source apportionment technologies are used to understand the impacts of important sources of particulate matter (PM) air quality, and are widely used for both scientific studies and air quality management. Generally, receptor models apportion speciated PM data from a single sampling site. With the development of large scale monitoring networks, PM speciation are observed at multiple sites in an urban area. For these situations, the models should account for three factors, or dimensions, of the PM, including the chemical species concentrations, sampling periods and sampling site information, suggesting the potential power of a three-dimensional source apportionment approach. However, the principle of three-dimensional Parallel Factor Analysis (Ordinary PARAFAC) model does not always work well in real environmental situations for multi-site receptor datasets. In this work, a new three-way receptor model, called “multi-site three way factor analysis” model is proposed to deal with the multi-site receptor datasets. Synthetic datasets were developed and introduced into the new model to test its performance. Average absolute error (AAE, between estimated and true contributions) for extracted sources were all less than 50%. Additionally, three-dimensional ambient datasets from a Chinese mega-city, Chengdu, were analyzed using this new model to assess the application. Four factors are extracted by the multi-site WFA3 model: secondary source have the highest contributions (64.73 and 56.24 μg/m3), followed by vehicular exhaust (30.13 and 33.60 μg/m3), crustal dust (26.12 and 29.99 μg/m3) and coal combustion (10.73 and 14.83 μg/m3). The model was also compared to PMF, with general agreement, though PMF suggested a lower crustal contribution.

Long-term and synchronous monitoring of PM10 and PM2.5 was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models (PMF2, ME2-2way and ME2-3way). Consistent results were found: the primary source categories contributed 63.4% (PMF2), 64.8% (ME2-2way) and 66.8% (ME2-3way) to PM10, and contributed 60.9% (PMF2), 65.5% (ME2-2way) and 61.0% (ME2-3way) to PM2.5. Secondary sources contributed 31.8% (PMF2), 32.9% (ME2-2way) and 31.7% (ME2-3way) to PM10, and 35.0% (PMF2), 33.8% (ME2-2way) and 36.0% (ME2-3way) to PM2.5. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment (SDA) was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east (ENE) contributed the highest to both PM10 (12.7%) and PM2.5 (9.7%) in Chengdu, followed by the crustal dust from south-east (SE) for PM10 (9.8%) and secondary nitrate & secondary organic carbon from ENE for PM2.5 (9.6%). Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and to develop effective pollution control strategies.

Heavy metals (Cr, Co, Ni, As, Cd, and Pb) can be bound to PM adversely affecting human health. Quantifying the source impacts on heavy metals can provide source-specific estimates of the heavy metal health risk (HMHR) to guide effective development of strategies to reduce such risks from exposure to heavy metals in PM2.5 (particulate matter (PM) with aerodynamic diameter less than or equal to 2.5 μm). In this study, a method combining Multilinear Engine 2 (ME2) and a risk assessment model is developed to more effectively quantify source contributions to HMHR, including heavy metal non-cancer risk (non-HMCR) and cancer risk (HMCR). The combined model (called ME2-HMHR) has two steps: step1, source contributions to heavy metals are estimated by employing the ME2 model; step2, the source contributions in step 1 are introduced into the risk assessment model to calculate the source contributions to HMHR. The approach was applied to Huzou, China and five significant sources were identified. Soil dust is the largest source of non-HMCR. For HMCR, the source contributions of soil dust, coal combustion, cement dust, vehicle, and secondary sources are 1.0 × 10−4, 3.7 × 10−5, 2.7 × 10−6, 1.6 × 10−6 and 1.9 × 10−9, respectively. The soil dust is the largest contributor to HMCR, being driven by the high impact of soil dust on PM2.5 and the abundance of heavy metals in soil dust.

Based on the source apportionment by positive matrix factorization (PMF) model, we analyze the main sources and characteristics of aerosol fine particulate matter (PM

The physicochemical properties of engineered nanomaterials can change drastically during aging in the environment. Understanding how these changes influence protein corona formation on nanomaterials is critical for accurately predicting the human exposure risks of aged nanomaterials. Here, we show that sulfidation, a prevalently occurring environmental aging process, of Ag and ZnO nanomaterials significantly affected the protein compositions of the hard corona formed in human saliva, sweat, and bronchoalveolar lavage fluid, corresponding to three most common exposure pathways, that is, ingestion, dermal contact, and inhalation. In particular, a diverse variety of proteins selectively associated with either sulfidized or pristine nanomaterials. Random forest classification of the proteomic data revealed that this selective protein adsorption process was mainly dictated by electrostatic interaction, hydrophobic interaction, and steric hindrance between proteins and nanomaterials, which were susceptible to the changes in surface charge, hydrophobicity, and aggregation status of nanomaterials induced by sulfidation. Furthermore, even for the proteins that do not exhibit distinct adsorption selectivity between sulfidized and pristine nanomaterials, sulfidation altered the extents of impact of nanomaterials on the conformation and likely functions of the adsorbed proteins. These findings unearth a previously neglected mechanism via which environmental sulfidation process mediates the biological effects of soft-metal-containing nanomaterials.

Air pollution source tracing, technically known as "source apportionment" problem, has been an important prerequisite for urban atmospheric pollution prevention and control especially in China where the pollution sources are highly heterogeneous across regions and time. However, effective and reliable source tracing has been a difficult task due to the need for pollution inventory profile which is cost-and time-consuming to obtain, as well as the need for domain expertise in data analysis and result interpretation. As large amount of historical ambient air pollution (especially particulate matter PM) data, which consists of both its chemical composition and the pollution control conclusions provided by human experts have been accumulated in the past decade, we develop a data-driven, end-to-end Smart Pollution Source Tracing (SPST) model for fully automatic estimation of PM source contributions. The proposed model is based on Gradient Tree Boosting algorithm which learns an ensemble of regression trees to learn the nonlinear mapping between ambient pollution data and the corresponding pollution source contribution. SPST model is trained and tested on both synthetic data which has been previously used as benchmark in other models, as well as the real, 5-cities atmospheric pollution data. Performance evaluation on synthetic data shows significant improvement of SPST over previous source tracing models. SPST also achieves high accuracy on real data, showing its potential to be applied in practice.

Considering the lack of numbers and updates of particulate matter (PM) source profiles, which show PM emitted from the Chinese iron and steel industry, a dilution tunnel system was used to sample PM discharged from the three main processes (sintering, puddling, and steelmaking) of an iron and steel company in Wuhan, China. Six source profiles for fine and coarse PM were established, and their characteristics were researched. The main conclusions were as follows:① For the sintering source profiles, SO

Recently, a new method combining positive matrix factorization (PMF) and heavy metal health risk (HMHR) assessment was proposed to apportion sources of heavy metals in ambient particulate matter and the associated heavy metal cancer health risk (HMCR), which has been applied to data collected in Yangzhou, China. The annual average concentrations of six measured heavy metals were Pb (64.4 ng·m

Particulate matter source apportionment (SA) is the basis and premise for preventing and controlling haze pollution scientifically and effectively. Traditional offline SA methods lack the capability of handling the rapid changing pollution sources during heavy air pollution periods. With the development of multiple online observation techniques, online SA of particulate matter can now be realized with high temporal resolution, stable and reliable continuous observation data on particle compositions. Here, we start with a summary of online measuring instruments for monitoring particulate matters that contains both online mass concentration (online MC) measurement, and online mass spectrometric (online MS) techniques. The former technique collects ambient particulate matter onto filter membrane and measures the concentrations of chemical components in the particulate matter subsequently. The latter technique could be further divided into two categories: bulk measurement and single particle measurement. Aerosol Mass Spectrometers (AMS) could provide mass spectral information of chemical components of non-refractory aerosols, especially organic aerosols. While the emergence of single-particle aerosol mass spectrometer (SPAMS) technology can provide large number of high time resolution data for online source resolution. This is closely followed by an overview of the methods and results of SA. However, online instruments are still facing challenges, such as abnormal or missing measurements, that could impact the accuracy of online dataset. Machine leaning algorithm are suited for processing the large amount of online observation data, which could be further considered. In addition, the key research challenges and future directions are presented including the integration of online dataset from different online instruments, the ensemble-trained source apportionment approach, and the quantification of source-category-specific human health risk based on online instrumentation and SA methods.

Modeled results are very important for environmental management. Unreasonable modeled result can lead to wrong strategy for air pollution management. In this work, an improved physically constrained source apportionment (PCSA) technology known as Multilinear Engine 2-species ratios (ME2-SR) was developed to the 11-h daytime and nighttime fine ambient particulate matter in urban area. Firstly, synthetic studies were carried out to explore the effectiveness of ME2-SR. The estimated source contributions were compared with the true values. The results suggest that, compared with the positive matrix factorization (PMF) model, the ME2-SR method could obtain more physically reliable outcomes, indicating that ME2-SR was effective, especially when apportioning the datasets with no unknown source. Additionally, 11-h daytime and nighttime PM2.5 samples were collected from Tianjin in China. The sources of the 11-h daytime and nighttime fine ambient particulate matter in China were identified using the new method and the PMF model. The calculated source contributions for ME2-SR for daytime PM2.5 samples are resuspended dust (38.91 μg m(-3), 26.60%), sulfate and nitrate (38.60 μg m(-3), 26.39%), vehicle exhaust and road dust (38.26 μg m(-3), 26.16%) and coal combustion (20.14 μg m(-3), 13.77%), and those for nighttime PM2.5 samples are resuspended dust (18.78 μg m(-3), 12.91%), sulfate and nitrate (41.57 μg m(-3), 28.58%), vehicle exhaust and road dust (38.39 μg m(-3), 26.39%), and coal combustion (36.76 μg m(-3), 25.27%). The comparisons of the constrained versus unconstrained outcomes clearly suggest that the physical meaning of the ME2-SR results is interpretable and reliable, not only for the specified species values but also for source contributions. The findings indicate that the ME2-SR method can be a useful tool in source apportionment studies, for air pollution management.

Source apportionment is a key tool in establishing the relationship between the emission sources and receptors of particulate matters (PM), and it also plays a vital role on providing technical support for PM pollution control policy and its effective evaluation. Recently, a multi-time resolution PM source apportionment technique (MTR SA), based on the classic source apportionment method was developed as a new approach to explore the source, behavior and formation of PM during heavy pollution episodes, as well as to evaluate the effects of emergency control measures. In this study, three main methods of MTR SA are introduced, which based on the methods of receptor models combined with online observation of PM chemical composition. The atmospheric physical model method can be used to indentify the regional distribution and transporting characteristics of pollution sources. Combining with the classical source apportionment methods, the PM source apportionment in different air masses can be analyzed by this method, and it can investigate primary pollutants or tracers in different areas and industry sectors as well. Air quality model is a useful tool to simulate the regional air quality so as to identify the spatial distribution of the concentration of pollutants and to estimate the contribution of external pollution sources to the concentration of pollutants which can predict emission reduction in the future years. Methods of receptor model combined with online observation are more applicable to the classified quantitative analysis of the receptor particles. Atmospheric physical model and air quality model can not only provide high temporal resolution MTR SA, but also provide information about the spatial distribution and transmission pathways, which can offer decision-making basis for joint prevention and control of regional particles pollution. In order to meet the requirement of the current research and management demand, from three aspects of technology, science and application management, we put forward the future challenges of MTR SA technology. Although MTR SA technology has been applied in research and environmental management, intensive studies are still necessary to be verified, including the representativeness of the sampling results, the scientificity of calculation principles, the validity and accuracy of the simulation results, as well as the suitability and effectiveness of the method.

With real time resolved data of Particulate matter (PM) and chemical species, understanding the source patterns and chemical characteristics is critical to establish controlling of PM. In this work, PM2.5 and chemical species were measured by corresponding online instruments with 1-h time resolution in Beijing. Multilinear Engine 2 (ME2) model was applied to explore the sources, and four sources (vehicle emission, crustal dust, secondary formation and coal combustion) were identified. To investigate the sensitivity of time resolution on the source contributions and chemical characteristics, ME2 was conducted with four time resolution runs (1-h, 2-h, 4-h, and 8-h). Crustal dust and coal combustion display large variation in the four time resolutions runs, with their contributions ranging from 6.7 to 10.4 μg m−3 and from 6.4 to 12.2 μg m−3, respectively. The contributions of vehicle emission and secondary formation range from 7.5 to 10.5 and from 14.7 to 16.7 μg m−3, respectively. The sensitivity analyses were conducted by principal component analysis-plot (PCA-plot), coefficient of divergence (CD), average absolute error (AAE) and correlation coefficients. For the four time resolution runs, the source contributions and profiles of crustal dust and coal combustion were more unstable than other source categories, possibly due to the lack of key markers of crustal dust and coal combustion (e.g. Si, Al). On the other hand, vehicle emission and crustal dust were more sensitive to time series of source contributions at different time resolutions. Findings in this study can improve our knowledge of source contributions and chemical characteristics at different time solutions.

Using the wire of the same material as base metal, EW75 magnesium alloy (Mg-7Gd-5Y-1Nd-0.5Zr wt. %) plate with the thickness of 6 mm was welded by TIG to investigate the welding property. Optical microscopy (OM), scanning electron microscopy (SEM) and universal tensile testing machine were used to analyze the appearance of the welding joint, organization performance and mechanical properties.The results revealed that the fusion zone (FZ) was composed of fine equiaxed grains. Precipitated phase distributed on the grain boundary as network structure. The grain size in heat affected zone (HAZ) was coarse because of overheating. The grain size of the FZ increased sharply when the welding current was more than 125A. With the increase of welding current, the grain size of the HAZ remained relatively stable. The hardness curve presented typical “W” shape, and the microhardness of the FZ was the highest, then was the base metal (BM), while the HAZ was the lowest. The welding joint showed uniform scale shape when the welding current was125A.The tensile strength of welding joint was 228 MPa, about 89.4% of the BM, the fracture location was in the FZ and the fracture surface was characterized by mixture fracture.

To understand the influence of quarry mining dust on particulate matter, ambient PM2.5 and quarry mining dust source samples were collected in a city near quarry facilities during 2013-2014. Samples were subject to chemical analysis for dust-related species (Al, Si, Ca, Fe, Ti), tracer metals, carbon components and water-soluble ions. Seasonal variations of PM2.5 and its main chemical components were investigated. Distinctive seasonal variations of PM2.5 were observed, with the highest PM2.5 concentrations (112.42μgm(-3)) in fall and lowest concentrations in summer (45.64μgm(-3)). For dust-related species, mass fractions of Si and Al did not show obvious seasonal variations, whereas Ca presented higher fractions in spring and summer and lower fractions in fall and winter. A combined receptor model (PMF-CMB) was applied to quantify the quarry mining dust contribution to PM2.5. Seven sources were identified, including quarry mining dust, soil dust, cement dust, coal combustion vehicles, secondary sulfate and secondary nitrate. On a yearly average basis, the contribution of quarry mining dust to PM2.5 was 6%. The contribution of soil dust to PM2.5 was comparable with cement dust (13% and 13%, respectively). Other identified sources included vehicle, secondary sulfate, secondary nitrate and coal combustion, which contributed 23, 15, 9 and 18% of the total mass, respectively. Air mass residence time (AMRT) analysis showed that northeast and southeast regions might be the major PM2.5 source during the sampling campaign. The findings of this study can be used to understand the characteristics of quarry mining dust and control strategies for PM2.5.

The influence of two-step homogenization on microstructures and properties of AZ40M magnesium alloy were investigated by the scanning electronic microscopy (SEM), X-ray diffraction analysis (XRD) and tensile experiment. Through comparing the microstructure and mechanical properties of the alloy before and after homogenization treatment, a two-step homogenization process of the semi-continuous casting AZ40M magnesium alloy ingot was determined. The result shows that the as-cast AZ40M alloy mainly contained two kinds of second phase, β-Mg17Al12 and Al8Mn5. Treating at temperature of 380 °C for 8 h as the first step homogenization treatment and then treating at temperature of 420 °C for 20 h as the second step homogenization treatment made the eutectic phase (α-Mg/β-Mg17Al12) completely eliminated and the alloying elements such as Al, Zn distributed uniformly. Al8Mn5 is the residual second phase in the homogenized AZ40M alloy. During the homogenization process, the β-phase dissolved which made the content of Al increased in the matrix. The decomposition of β-phase benefits the tensile strength and elongation of the alloy. The tensile fractures of as-cast and homogenization alloys are characteristic of ductile fracture.

PM2.5 variances have adverse impacts on human beings and the environment; therefore, source apportionment is very important and is a hot global topic. In this work, a new model called WALSPMF is proposed and evaluated for its accuracy. First, a synthetic test was carried out to compare the estimated source profile and contributions with the synthetic ones. Average absolute error (AAE) values were also calculated between the estimated and synthetic source contributions; most of the values were low (

Uncertainty estimation plays an important role in source apportionment models such as the positive matrix factorization (PMF) model. In this study, synthetic datasets were generated and analyzed using PMF with specified uncertainties at different levels to investigate the impact of uncertainty inputs on the results of PMF model, as well as the benefits and risks of emphasizing on certain species. The results showed that: (1) uncertainties for the PMF model should be estimated based on characteristics of the dataset being analyzed; (2) emphasizing on correct tracers will improved model performance; and (3) emphasizing on unsuitable tracers may lead to disruptive consequences that might not be captured by the Q metric. Tests were also performed on collected ambient PM_(2.5) samples and similar conclusions were drawn: emphasizing on correct tracers was shown to improve the separation of important source categories from mixed sources. When emphasizing on incorrect tracers, a counterfeit factor of Fe industrial source was extracted, which are inconsistent with field observations. Results from this study provide insights on how uncertainties should be estimated for the PMF model.

Although three-way factor analysis models can take more information into account, their degree of accuracy must be investigated further. We simulated numerous synthetic datasets to evaluate traditional (PMF3) and advanced three-way models (AAB or ABB). On one hand, scenarios whereby sources share the same profiles but different emission patterns were constructed and introduced into PMF3 and advanced AAB models that can estimate the same source profile matrixes B but different emission pattern matrixes A. On the other hand, datasets with the same emission patterns but different profiles were set to simulate the variability of source profiles and were used to evaluate an advanced ABB model that can estimate the same emission pattern matrix A but different source profile matrixes B. The AAEs of PMF3 under two different conditions ranged from 2.95% to 90.22% and from 2.98% to 90.11%, respectively, while the results of the advanced three-way models were 2.88%–27.51% and 2.89%–29.89%, respectively. We observed that the PMF3 performed well when all sources showed strong emission patterns and source profiles were similar. The application of advanced AAB model was stable under various emission patterns; and ABB model was stable under different variability of source profiles. At the same time, several ambient datasets were estimated by the advanced three-way models. Our findings suggest that the performance of advanced three-way models make full use of spatial or size distribution information to enhance capacities to identify source categories.

Secondary inorganic aerosols (SIA, including sulfate, nitrate, and ammonium) were found to be the most dominant aerosol components during haze period. Elucidating the driving factors during liquid-phase reaction formation process of SIA is a key step for understanding the origin of airborne particulate pollution. In this work, we used online instruments and collected hourly concentrations of ions, gases, meteorological parameters in a northern city of China. Source emissions were estimated by receptor model PMF/ME2 pulling run solution (Partial Target Transformation-PMF: PTT-PMF), and six sources were identified (dust, coal combustion, vehicle exhaust, secondary sulfate, secondary nitrate and biomass burning & SOC). pH values were calculated by ISORROPIA-II, with an average value of 4.68. The relationships between RH, pH, source behaviors and concentrations of SIA during different pollution episodes were discussed. Source emissions were found to influence pH and subsequently the SIA; high RH could result in high SIA because aerosol water content in atmosphere increases with RH, which is favorable for liquid-phase reactions. Finally, we employed statistical and machine learning (Random forest classification: RFC) methods to analysis importance of driving factors on SIA formation during different pollution episodes. Results showed precursors, wet conditions, atmospheric oxidation capacity and acidity favor formation of sulfate and nitrate at different pollution levels; changes of gaseous precursors, RH and temperature are more important to determine PM2.5 pollution classification (clean, slightly polluted, moderately polluted, heavily polluted episodes). The findings of this work can provide useful information for better understanding formation mechanism of SIA, mitigating air pollution and improving human health.

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects’ mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O3 and PM2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.

A single particle source analysis was conducted for particulate matter and oxalic acid using a single particle aerosol mass spectrometer during a typical biomass burning period in Tianjin, China. The adaptive resonance theory 2 neural network algorithm was applied to analyze mass spectra data and generate 20 particle clusters. Size-resolved time series of the particle classes were put into an advanced three-way model (ABB, multi-size three-way model) for source apportionment. Seven sources were identified, including crustal dust, biomass burning, fossil fuel combustion, secondary organic, secondary nitrate and secondary sulfate. Oxalic acid containing particles accounted for 1.4% of the total detected particles during the overall sampling period, and the fraction increased to 2.6% during the biomass burning period. Oxalic acid was predominantly observed in the Na-K-EC, EC, EC-K, EC-aged, K, Levoglucosan and Fe particle classes. The mixing fraction of oxalic acid in these particle types, and their diurnal variation and size distribution patterns are related to different formation mechanisms. Source contributions to oxalic acid were apportioned, and secondary sulfate (49%), biomass burning (25%), vehicle exhaust (17%), and secondary organic sources (8%) may have contributed to the amount of oxalic acid-containing particles. The contribution of secondary sulfate sources follows the O3 concentration during the sampling period. This is likely because strong photochemical oxidation activity during the sampling period produces more oxalic acid precursors from Volatile organic compounds. Biomass burning also clearly contributed the number of oxalic acid particles, because precursors emitted by biomass burning can be quickly oxidized into oxalic acid, and more oxalic acid was formed during transportation. Fe particles may also play important role in oxalic acid deposition.

Air pollution negatively impact human health and the environment. Source apportionment is an important research method for designing effective air pollution control policies. In this study, a new PTT-PMF (Partial Target Transformation-Positive matrix factor) source apportionment method (based on Multilinear Engine 2 program) was developed, to better recognize the extracted source categories, for online dataset. By incorporating measured source profiles, this model can automatically extract factors that have physical significance and close to actual source profiles. The accuracy of the PTT-PMF method was first evaluated by using several artificial datasets, and the results were favorable. Results of the new model can better explain similar sources such as dust and coal combustion (when lack of markers such as Ai and Si), comparing with the base run obtained using PMF. Then, this model was applied to online measurement data collected in Tianjin, China. Six factors were obtained and they were close to actual source profiles: dust (6.06%), coal combustion (4.70%), secondary sulfate sources (26.36%), secondary nitrate sources (42.91%), vehicle exhaust (9.97%) and biomass burning & SOC (10.01%). Time series of source contributions can also help to identify the source categories. The work presented here may advance receptor modelling techniques and could also enhance the validity of source apportionment results.

As an important megacity of the Beijing-Tianjin-Hebei air pollution transmission channel and the Bohai Sea Economic Zone, Tianjin is influenced by air pollution in recent years, thus research on the fine particulate matter in Tianjin is of vital value. In this study, single particle aerosol mass spectrometry (SPAMS) was used to measure data of Jinnan District in Tianjin during August 2017, to describe the chemical features of fine particles in summer ambient air and estimate the potential pollution sources of fine particles. By using the ART-2a clustering method, 12 classes of PM were acquired, such as elemental carbon particles, Fe-NO

Meteorological conditions, gas-phase precursors, and aerosol acidity (pH) can influence the formation of secondary inorganic aerosols (SIA) in fine particulate matter (PM2.5). Most works related to the influence of pH and gas-phase precursors on SIA have been laboratory research, but field observation research is very scarce, especially in arid environments. The relationship among SIA, pH, gas-phase precursors, and meteorological conditions are investigated in Hohhot, a major city in China with an arid environment. Secondary inorganic species, e.g., SO4 2−, NO3 −, were typically found at low levels, reflecting the low level of secondary aerosol. It is interesting to note that the level of SO2 in Hohhot was higher than in other cities while SO4 2− was relatively lower than in other cities. Multiple receptor models were used to explore the contributions to the SIA and quantify the source impacts on the SIA. Annual average aerosol pH in Hohhot was 5.6 (range 1.1-8.4) which was estimated by a thermodynamic equilibrium model. Additionally, a statistical method was used to evaluate the influence of SIA sources on ambient aerosol concentrations. Aerosol water content and particulate acidity were found to be positively associated with secondary SO4 2−, while NO2 and RH had a significant impact on secondary NO3 − in an arid atmosphere. The findings explain the relationship between gaseous precursors, relative humidity, aerosol pH and temperature in the arid city of Hohhot. © 2018 Elsevier B.V.