Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework.

Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional 'factor-energy consumption' to three parts of 'factor-event-energy consumption', which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.

Associations between Source-Specific Fine Particulate Matter and Mortality and Hospital Admissions in Beijing, China.

The health effects of PM2.5 exposure have become a major public concern in developing countries. Identifying major PM2.5 sources and quantifying the health effects at the population level are essential for controlling PM2.5 pollution and formulating targeted emissions reduction policies. In the current study, we have obtained PM2.5 mass data and used positive matrix factorization to identify the major sources of PM2.5. We evaluated the relationship between short-term exposure to PM2.5 sources and mortality or hospital admissions in Beijing, China, using 441 742 deaths and 9 420 305 hospital admissions from 2013 to 2018. We found positive associations for coal combustion and road dust sources with mortality. Increased hospital admission risks were significantly associated with sources of vehicle exhaust, coal combustion, secondary sulfates, and secondary nitrates. Compared to the cool season, excess mortality risk estimates of coal combustion source were significantly higher in the warm season. Our findings show that reducing more toxic sources of PM2.5, especially coal emissions, and developing clean energy alternatives can have critical implications for improving air quality and protecting public health.

Targeting Atmospheric Oxidants Can Better Reduce Sulfate Aerosol in China: H2O2 Aqueous Oxidation Pathway Dominates Sulfate Formation in Haze.

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H2O2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H2O2 pathway. The collaborative effects of atmospheric oxidants and SO2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO2 concentrations in China.

Linking the Fasting Blood Glucose Level to Short-Term-Exposed Particulate Constituents and Pollution Sources: Results from a Multicenter Cross-Sectional Study in China.

Ambient PM2.5 (fine particulate matter with aerodynamic diameters ≤ 2.5 µm) is thought to be associated with the development of diabetes, but few studies traced the effects of PM2.5 components and pollution sources on the change in the fasting blood glucose (FBG). In the present study, we assessed the associations of PM2.5 constituents and their sources with the FBG in a general Chinese population aged over 40 years. Exposure to PM2.5 was positively associated with the FBG level, and each interquartile range (IQR) increase in a lag period of 30 days (18.4 µg/m3) showed the strongest association with an elevated FBG of 0.16 mmol/L (95% confidence interval: 0.04, 0.28). Among various constituents, increases in exposed elemental carbon, organic matter, arsenic, and heavy metals such as silver, cadmium, lead, and zinc were associated with higher FBG, whereas barium and chromium were associated with lower FBG levels. The elevated FBG level was closely associated with the PM2.5 from coal combustion, industrial sources, and vehicle emissions, while the association with secondary sources was statistically insignificant. Improving air quality by tracing back to the pollution sources would help to develop well-directed policies to protect human health.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution.

PM2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 µg/m3) and 33% (19.7 µg/m3) to variation in PM2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NOX. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM2.5 pollution.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA.

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Digital Twin-Based Risk Control during Prefabricated Building Hoisting Operations.

Prefabricated buildings have advantages when it comes to environmental protection. However, the dynamics and complexity of building hoisting operations bring significant safety risks. Existing research on hoisting safety risk lacks a real-time information interaction mechanism and lacks scientific control decision-making tools based on considering the correlation between safety risks. Digital twin (DT) has the advantage of real-time interaction. This paper presents a safety risk control framework for controlling prefabricated building hoisting operations based on DT. In the case of considering the correlation of the safety risk index of hoisting, the safety risk hierarchy model of hoisting is defined in the process of building the DT model. The authors have established a Bayesian network model into the process of the integrated analysis of the digital twin mechanism model and monitoring data to realize the visualization of the decision analysis process of hoisting safety risk control. The key degree of the indirect inducement variable to direct inducement variable was calculated according to probability. The key factor leading to the occurrence of risk was found. The effectiveness of the hoisting safety risk control method is verified by a large, prefabricated building project. This method provides decision tools for hoisting safety risk control, assists in formulating effective control schemes, and improves the efficiency of information integration and sharing.

Self-feedback LSTM regression model for real-time particle source apportionment.

Atmospheric particulate matter pollution has attracted much wider attention globally. In recent years, the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques. Such demands are summarized, in this paper, as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances, such as the existence of secondary source and similar source. In this study, we firstly analyze the possible and potential restraints in single particle source apportionment, then propose a novel three-step self-feedback long short-term memory (SF-LSTM) network for approximating the source contribution. The proposed deep learning neural network includes three modules, as generation, scoring and refining, and regeneration modules. Benefited from the scoring modules, SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment, meanwhile, the regeneration module calculates the source contribution in a non-linear way. The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators (residual sum of squares, stability, sparsity, negativity) for the restraints. Additionally, in short time-resolution analyzing, SF-LSTM provides better results under the restraint of stability.

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds.

Fine particulate matter (PM2.5) and ozone (O3) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O3 and secondary organic aerosols (SOA) contributing PM2.5. Herein, we investigated 54 VOCs, O3 and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O3, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O3 was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O3 and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O3 formation; alkanes (Cn, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O3 and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O3 formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O3.

Core-shell PdAu nanocluster catalysts to suppress sulfur poisoning.

Reducing sulfur poisoning is significant for maintaining the catalytic efficiency and durability of heterogeneous catalysts. We screened PdAu nanoclusters with specific Pd : Au ratios based on Monte Carlo simulations and then carried out density functional calculations to reveal how to reduce sulfur poisoning via alloying. Among various nanoclusters, the core-shell structure Pd13Au42 (Pd@Au) exhibits a low adsorption energy of SO2 (-0.67 eV), comparable with O2 (-0.45 eV) and lower than CO (-1.25 eV), thus avoiding sulfur poisoning during the CO catalytic oxidation. Fundamentally, the weak adsorption of SO2 originates from the negative d-band center of the shell and delocalized charge distribution near the Fermi level, due to the appropriate charge transfer from the core to shell. Core-shell nanoclusters with a different core (Ni, Cu, Ag, Pt) and a Pd@Au slab model were further constructed to validate and extend the results. These findings provide insights into designing core-shell catalysts to suppress sulfur poisoning while optimizing catalytic behaviors.

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources.

The submicron particulate matter (PM1) and fine particulate matter (PM2.5) are very important due to their greater adverse impacts on the natural environment and human health. In this study, the daily PM1 and PM2.5 samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin, China. The collected samples were analyzed for the carbonaceous fractions, inorganic ions, elemental species, and specific marker sugar species. The chemical characterization of PM1 and PM2.5 was based on their concentrations, compositions, and characteristic ratios (PM1/PM2.5, AE/CE, NO3-/SO42-, OC/EC, SOC/OC, OM/TCA, K+/EC, levoglucosan/K+, V/Cu, and V/Ni). The average concentrations of PM1 and PM2.5 were 32.4 µg/m3 and 53.3 µg/m3, and PM1 constituted 63% of PM2.5 on average. The source apportionment of PM1 and PM2.5 by positive matrix factorization (PMF) model indicated the main sources of secondary aerosols (25% and 34%), biomass burning (17% and 20%), traffic emission (20% and 14%), and coal combustion (17% and 14%). The biomass burning factor involved agricultural fertilization and waste incineration. The biomass burning and primary biogenic contributions were determined by specific marker sugar species. The anthropogenic sources (combustion, secondary particle formation, etc) contributed significantly to PM1 and PM2.5, and the natural sources were more evident in PM2.5. This work significantly contributes to the chemical characterization and source apportionment of PM1 and PM2.5 in near-port cities influenced by the diverse sources.

Using High-Temporal-Resolution Ambient Data to Investigate Gas-Particle Partitioning of Ammonium over Different Seasons.

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 relationship between pH and the partitioning of ammonium (ε(NH4+)) using hourly ambient samples collected from Tianjin, China. We found a "Reversed-S curve" between pH and ε(NH4+) from the ambient hourly aerosol dataset when the theoretical ε(NO3-)* (an index identified in this work) was within specific ranges. A Boltzmann function was then used to fit the Reversed-S curve. For the summer data set, when ε(NO3-)* was between 0.7 and 0.8, the fitted R2 was 0.88. Through thermodynamic analysis, we found that the values of k[H+]2 (k = 3.08 × 104 L2 mol-2) and ε(NO3-)* can influence the pH-ε(NH4+) curve. Under certain situations, the values of k[H+]2 and ε(NO3-)* are similar to each other, and ε(NH4+) is sensitive to pH, suggesting that ε(NO3-)* plays an important role in affecting the ε(NH4+). During summer, winter, and spring seasons, when the relative humidity was greater than 0.36 and ε(NO3-)* was between 0.8 and 0.95, there was an obvious Reversed-S curve, with R2 = 0.60. The theoretical k[H+]2 and ε(NO3-)* developed in this work can be used to analyze the gas-particle partitioning of ammonia-ammonium and nitrate-nitric acid in the ambient atmosphere. Also, it is the first time that we created the joint source-NH3/HNO3 maps to integrate sources, aerosol pH and liquid water content, and ions (altogether in one map), which can provide useful information for designing effective strategies to control particulate matter pollution.

Intelligent Tensioning Method for Prestressed Cables Based on Digital Twins and Artificial Intelligence.

In this study, to address the problems of multiple dimensions, large scales, complex tension resource scheduling, and strict quality control requirements in the tensioning process of cables in prestressed steel structures, the technical characteristics of digital twins (DTs) and artificial intelligence (AI) are analyzed. An intelligent tensioning of prestressed cables method driven by the integration of DTs and AI is proposed. Based on the current research status of cable tensioning and DTs, combined with the goal of intelligent tensioning, a fusion mechanism for DTs and AI is established and their integration to drive intelligent tensioning of prestressed cables technology is analyzed. In addition, the key issues involved in the construction of an intelligent control center driven by the integration of DTs and AI are discussed. By considering the construction elements of space and time dimensions, the tensioning process is controlled at multiple levels, thereby realizing the intelligent tensioning of prestressed cables. Driven by intelligent tensioning methods, the safety performance evaluation of the intelligent tensioning process is analyzed. Combined with sensing equipment and intelligent algorithms, a high-fidelity twin model and three-dimensional integrated data model are constructed to realize closed-loop control of the intelligent tensioning safety evaluation. Through the study of digital twins and artificial intelligence fusion to drive the intelligent tensioning method for prestressed cables, this study focuses on the analysis of the intelligent evaluation of safety performance. This study provides a reference for fusion applications with DTs and AI in intelligent tensioning of prestressed cables.

Sulfidation of Ag and ZnO Nanomaterials Significantly Affects Protein Corona Composition: Implications for Human Exposure to Environmentally Aged Nanomaterials.

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.

High-Resolution Data Sets Unravel the Effects of Sources and Meteorological Conditions on Nitrate and Its Gas-Particle Partitioning.

Nitrate is one of the most abundant inorganic water-soluble ions in fine particulate matter (PM2.5). However, the formation mechanism of nitrate in the ambient atmosphere, especially the impacts of its semivolatility and the various existing forms of nitrogen, remain under-investigated. In this study, hourly ambient observations of speciated PM2.5 components (NO3-, SO42-, etc.) were collected in Tianjin, China. Source contributions were analyzed by PMF/ME2 (Positive Matrix Factorization using the Multilinear Engine 2) program, and pH were estimated by ISORROPIA-II, to investigate the relationship between pH and nitrate. Five sources (factors) were resolved: secondary sulfate (SS), secondary nitrate (SN), dust, vehicle and coal combustion. SN and pH showed a triangle-shaped relationship. When SS was high, the fraction of nitrate partitioning into the aerosol phase exhibits a characteristic "S-curve" relationship with pH for different seasons. An index ( ITL) is developed and combined with pH to explore the sensitive regions of "S-curve". Controlling the emissions of anions (SO42-, Cl-), cations (Ca2+, Mg2+, etc.) and gases (NO x, NH3, SO2, etc.) will change pH, potentially reducing or increasing SN. The findings of this work provide an effective approach for exploring the formation mechanisms of nitrate under different influencing factors (sources, pH, and IRL).

Detailed Analysis of Estimated pH, Activity Coefficients, and Ion Concentrations between the Three Aerosol Thermodynamic Models.

In this work, we utilize a rich set of simulated and ground-based observational data in Tianjin, China to examine and compare the differences in aerosol acidity and composition predicted by three popular thermodynamic equilibrium models: ISORROPIA II, the Extended Aerosol Inorganics Model vision IV (E-AIM IV), and the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients model (AIOMFAC). The species used to estimate aerosol acidity for both simulated and ambient data were NH4+, Na+, SO42-, NO3-, and Cl-. For simulated data, there is good agreement between ISORROPIA II and E-AIM IV predicted acidity in the forward and metastable mode, resulting from the hydrogen ion activity coefficient (γ(H+)) and the molality (m(H+)) showing opposite trends. While almost all other inorganic species concentrations are found to be similar among the three models, such is not the case for the bisulfate ion (HSO4-), which is linked to m(H+). We find that differences in predicted bisulfate between the three models primarily result from differences in the treatment of the HSO4- ↔ H+ + SO42- reaction for highly acidic conditions. This difference in bisulfate is responsible for much of the difference in estimated pH for the ambient data (average pH of 3.5 for ISORROPIA II and 3.0 for E-AIM IV).

Source contributions to water-soluble organic carbon and water-insoluble organic carbon in PM2.5 during Spring Festival, heating and non-heating seasons.

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.

Development of PM2.5 Source Profiles Using a Hybrid Chemical Transport-Receptor Modeling Approach.

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 improved the spatial and temporal bias in modeled concentrations of several trace metal species, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for summer compared with previous studies. Source profile optimization can be useful for source apportionment studies that have limited availability of source profile data for the location of interest.

pH of Aerosols in a Polluted Atmosphere: Source Contributions to Highly Acidic Aerosol.

Acidity (pH) plays a key role in the physical and chemical behavior of PM2.5. However, understanding of how specific PM sources impact aerosol pH is rarely considered. Performing source apportionment of PM2.5 allows a unique link of sources pH of aerosol from the polluted city. Hourly water-soluble (WS) ions of PM2.5 were measured online from December 25th, 2014 to June 19th, 2015 in a northern city in China. Five sources were resolved including secondary nitrate (41%), secondary sulfate (26%), coal combustion (14%), mineral dust (11%), and vehicle exhaust (9%). The influence of source contributions to pH was estimated by ISORROPIA-II. The lowest aerosol pH levels were found at low WS-ion levels and then increased with increasing total ion levels, until high ion levels occur, at which point the aerosol becomes more acidic as both sulfate and nitrate increase. Ammonium levels increased nearly linearly with sulfate and nitrate until approximately 20 µg m-3, supporting that the ammonium in the aerosol was more limited by thermodynamics than source limitations, and aerosol pH responded more to the contributions of sources such as dust than levels of sulfate. Commonly used pH indicator ratios were not indicative of the pH estimated using the thermodynamic model.

Size distribution, directional source contributions and pollution status of PM from Chengdu, China during a long-term sampling campaign.

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.