Source Profiles Research Articles

Uncovering the authorship: Linking media content to social user profiles

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews, an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles.

Emission Factors and Source Profiles of Volatile Organic Compounds in the Petroleum Refining Industry through On-Site Measurement from Multiple Refineries

Emission Factors and Source Profiles of Volatile Organic Compounds in the Petroleum Refining Industry through On-Site Measurement from Multiple Refineries

Automatic identification of factor profiles can be achieved by improved machine learning model

The identification of factor profiles is a pivotal step in the source apportionment model. Currently, this process heavily relies on human experience, resulting in high subjectivity in the results and requiring a time-consuming procedure. In this study, a pseudo label extra trees classifier model was proposed to facilitate the automated identification of factor profiles. The source profiles serve as domain knowledge to train the model, as they accurately reflect the distinctive characteristics of emission sources. The findings indicate that the recognition rate of seven factors is 94.3%, significantly outperforming four factors (25%), five factors (30%), six factors (60%). Significantly, the model demonstrates its proficiency in determining the optimal number of factors. And the factor profiles identified using this approach demonstrate complete concurrence with manual recognition. For offline datasets, the model is also proficient at identifying factor profiles and exhibits excellent generalization. This approach facilitates the identification of emission sources in intricate environments and advances the model's capacity to automatically discern source categories by utilizing domain knowledge characteristics.

Human arm redundancy: a new approach for the inverse kinematics problem.

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Numerical simulation of temperature gradient effects on gallium nitride crystal growth by sodium-flux method

During the growth of gallium nitride single crystals by sodium-flux method, temperature significantly impacts crystal quality. In this study, the mechanism of the effect of different temperature gradients on crystal growth is analyzed in depth using a combination of numerical simulation and experiment. The experimental results show that epitaxial growth of crystals occurs under positive temperature gradient conditions, while there is dissolution of seed crystals under negative temperature gradient conditions. The temperature, flow, and concentration data of the melted material during crystal growth were calculated using numerical simulation. The simulation findings reveal that the distribution of solution supersaturation varies according to temperature. High supersaturation at the bottom of the melt is favorable for crystal epitaxial growth on the surface of seed crystals under a positive temperature gradient. Meanwhile, low supersaturation at the top of the melt suppresses the hard polycrystalline layer here. Under negative temperature gradient conditions, low supersaturation at the bottom of the melt may lead to remelting of seed crystals, which is consistent with the experimental phenomenon. Furthermore, we propose an optimized heat source profile. This profile manages high supersaturation near seed crystals, aiding continuous growth. Finally, we have applied the curve in an applied way by proposing a multi-stage heating device, based on which the desired arbitrary temperature profile can be modulated. This research has broad applications in a variety of crystal growth experiments using fluid as the mother phase.

Eliminating collinearity observed in chemical mass balance analysis using multi linear regression and synthetic receptor source profile dataset

Eliminating collinearity observed in chemical mass balance analysis using multi linear regression and synthetic receptor source profile dataset

Pedestal main ion particle transport inference through gas puff modulation with experimental source measurements

Transport in the DIII-D high confinement mode (H-mode) pedestal is investigated through a periodic edge gas puff modulation (GPM) which perturbs the deuterium density and source profiles. By using absolutely calibrated experimental edge ionization profile measurements, radial profiles of diffusion (D) and convection (v) are calculated into the pedestal region without depending on modeling the edge ionization source. An analytic approach with closed-form expressions for the D and v profiles and a more advanced Bayesian approach show evidence of an inward particle convection on the order of 1 m s−1 extending to normalized poloidal flux ( ΨN ) of 0.98. Meanwhile, diffusion reaches a minimum value of (0.03±0.02) m2 s−1 in the pedestal region. Notably, the Bayesian approach, which utilizes the Aurora 1.5 D forward model inside the IMPRAD OMFIT module, provides radially resolved transport profiles with associated uncertainty without requiring an explicit form for the perturbation to the density profile or source. The combination of experimental ionization measurements and Bayesian inference provides an enhanced robust framework for investigating edge particle transport coefficients to experimentally test transport physics in order to improve predictive capabilities in the tokamak edge.

Contribution of Ship Emission to Volatile Organic Compounds Based on One‐Year Monitoring at a Coastal Site in the Pearl River Delta Region

AbstractShip emission impacts ambient air quality, especially in coastal regions, by emitting air pollutants such as fine particles, nitrogen oxides (NOx), and sulfur dioxide (SO2), yet its contributions to volatile organic compounds (VOCs) and the formation of secondary organic aerosol (SOA) and ozone (O3) are much less constrained with the challenge in distinguish ship emission from land diesel emission. In this study, we conducted a 1‐year online measurement of VOCs with a 1‐hr resolution at a coastal site in south China's Pearl River Delta region, which holds three of the world's top 10 container ports. The results revealed that C10–C12 n‐alkanes, as typical diesel‐related emission tracers, were significantly enhanced and strongly related to oceanic air masses. Receptor modeling revealed two diesel‐related sources of land diesel emission and ship emission, which could be differentiated based on their source profiles, seasonal trends and air mass back trajectories. Ship emissions contributed 6.4%, 5.0%, and 13.6% of total VOC mixing ratios, ozone formation potentials (OFPs), and secondary organic aerosol formation potentials (SOAFPs), while these percentages were 3.4%, 14.7%, and 15.9% for land diesel emission, respectively. In particular, in July, ship emissions could contribute 21.7%, 14.6%, and 31.2% of VOCs, OFPs, and SOAFPs, respectively. Our results highlight the important contribution of diesel‐related emission VOCs in forming O3 and SOA in coastal regions, and ship emission is a non‐negligible source of VOCs, particularly after the strict control of land emission sources.

The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city

This paper analyses the impact of urban mobility (UM) on air pollution by studying the effects of an intervention on local air quality. The study focuses on the PM2.5 levels in Kampala, the capital of Uganda, and considers COVID-19 as an unintentional intervention. The PM2.5 level of the city was obtained from a network of low-cost calibrated sensors, while UM is characterized by open-access Google reports. The period under consideration excludes the weeks immediately before and after the first lockdown. PM2.5 data were deweathered using the machine learning technique of random forest (RF) to exclude the variation of meteorological factors, seasonality, and weekday-weekend effect, and then the impact of the pandemic was parametrised. The traffic pattern is discussed, and air mass clustering and pollution polar plots are used to analyse the distribution of long- and short-range sources, respectively. The percentage change from the baseline (PCfB) of the average of UM dimensions is then assessed against that of deweathered PM2.5 level to investigate the impact of UM on the PM2.5 level. Our analysis shows a strong correlation between urban mobility and roadside PM2.5 levels and a weaker relationship with urban PM2.5 levels. The profile of long-range emission sources was consistent over the study period, with more than 61% of the modelled air masses that arrived in Kampala first passing over Kenya and Tanzania. Overall, the COVID-19 pandemic reduced PM2.5 levels in Kampala by about 10%, which is relatively small compared to many other cities that have been studied around the world.

Short-term source apportionment of fine particulate matter with time-dependent profiles using SoFi Pro: exploring the reliability of rolling positive matrix factorization (PMF) applied to bihourly molecular and elemental tracer data

Abstract. Positive matrix factorization (PMF) has been widely used to apportion the sources of fine particulate matter (PM2.5) by utilizing PM chemical speciation data measured at the receptor site(s). Traditional PMF, which typically relies on long-term observational datasets of daily or lower time resolution to meet the required sample size, has its reliability undermined by changes in source profiles; thus, it is inherently ill-suited for apportioning sporadic sources or ephemeral pollution events. In this study, we explored short-term source apportionment of PM2.5 using a set of bihourly chemical speciation data over a period of 37 d in the winter of 2019–2020. PMF run with campaign-wide data as input (PMFref) was initially conducted to obtain reference profiles for the primary source factors. Subsequently, short-term PMF analysis was performed using the Source Finder Professional (SoFi Pro). The analysis sets a window length of 18 d and constrained the primary source profiles using the a-value approach embedded in SoFi Pro software. Rolling PMF was then conducted with a fixed window length of 18 d and a step of 1 d using the remaining dataset. By applying the a-value constraints to the primary sources, the rolling PMF effectively reproduced the individual primary sources, as evidenced by the slope values close to unity (i.e., 0.9–1.0). However, the estimation for the firework emission factor in the rolling PMF was lower compared with PMFref (slope: 0.8). These results suggest the unique advantage of short-term PMF analysis in accurately apportioning sporadic sources. Although the total secondary sources were well modeled (slope: 1.0), larger biases were observed for individual secondary sources. The variation in source profiles indicated higher variabilities for the secondary sources, with average relative differences ranging from 42 % to 173 %, while the primary source profiles exhibited much smaller variabilities (relative differences of 8 %–26 %). This study suggests that short-term PMF analysis with the a-value constraints in SoFi Pro can be utilized to apportion primary sources accurately, while future efforts are needed to improve the prediction of individual secondary sources. Additionally, future rapid source apportionment analysis can benefit from utilizing a library of source profiles derived from existing measurement data, thereby significantly reducing the time lag associated with receptor modeling source apportionment techniques.

Organophosphate esters in the urban atmosphere of Thessaloniki city, Greece

The occurrence of organophosphate esters (OPEs) in the atmosphere of the urban area of Thessaloniki city, Greece was studied. OPEs were determined in particulate matter (PM2.5) and precipitation during the period 2020–2021. ∑OPEs in rainwater ranged from 520 to 4719 ng L−11 (mean: 1662 ng L−1) with tris (2-butoxyethyl) phosphate (TBOEP) and tris (1-chloro-2-propyl) phosphate (TClPP) being the most abundant compounds. TBOEP and TClPP as well as triphenylphosphine oxide (TPPO) and tris (chloroethyl) phosphate (TCEP) were the dominant OPEs in PM2.5. Concentrations of ∑OPEs in PM2.5ranged from 2.82 to 13.3 ng m−3 (mean: 5.93 ng m−3). Wet deposition fluxes of OPEs were estimated and air mass back trajectories were used to elucidate possible source profiles. An overall low health risk for local population via inhalation of OPEs was revealed.

Source profile of PM2.5 emissions from different primary sources in the coal capital city Dhanbad, India

The rise of fine particulate matter (PM2.5) levels in urban areas, driven by traffic, construction, and combustion emissions, has prompted urgent air quality concerns. Understanding source-specific PM2.5 chemical characteristics and developing associated source markers is essential for knowing their accurate contribution to atmospheric PM2.5. This study focuses on developing PM2.5 chemical source profiles from nine different emissions, primarily categorized under traffic and combustion sources. The chemical characterization included the carbonaceous thermal fractions, inorganic ions, and elemental composition. Heterogeneity in chemical composition across emission sources was examined using the coefficient of divergence and diagnostic ratio, and finally, source-specific chemical fingerprints were developed using the ratio normalization approach. The finding revealed significant inter and intra-variation in the chemical composition among traffic and combustion emission sources. Organic matter is observed significantly higher in combustion sources (84%–92%) than in traffic sources (22%–45%). Both OC/EC and char-EC/soot-EC values showed much higher values for combustion sources than traffic emissions, with cow dung cake burning emissions displaying the largest values. Also, char-EC/soot-EC values showed a similar trend with OC/EC values and thus can be used as an additional marker for deciphering emission sources. The ion-balance ratio revealed particle emissions from coal, cow dung, and garbage burning to be highly acidic, while traffic and construction sources were alkaline. Source marker results provide new insights into differences in the chemical fingerprint of specific emission sources. A new set of source markers was seen for garbage burning while coal-burning emissions showed varying chemical fingerprints and were found to be dependent on coal processing. Among elements, bromine and chlorine are found to be the unique markers for cow dung cake-burning emissions. Receptor models can use the database developed from the current work to demarcate the emission sources accurately and benefit the regulatory bodies in developing efficient control measures.

The UNCOVER Survey: A First-look HST+JWST Catalog of Galaxy Redshifts and Stellar Population Properties Spanning 0.2 ≲ z ≲ 15

The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster A2744 to create the deepest view of our Universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ∼ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 ≲ z ≲ 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-β priors on the masses and star formation histories to produce joint redshift and stellar populations posteriors. Additionally, lensing magnification is performed on the fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with σ NMAD ∼ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of this Treasury survey, we publicly release the stellar population catalog with this paper, derived from our photometric catalog adapting aperture sizes based on source profiles. This release (the catalog and all related documentation are accessible via the UNCOVER survey web page: https://jwst-uncover.github.io/DR2.html#SPSCatalogs with a copy deposited to Zenodo at doi:10.5281/zenodo.8401181) includes posterior moments, maximum likelihood spectra, star formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.

Source Profiles of Particle‐Bound Phenolic Compounds and Aromatic Acids From Fresh and Aged Solid Fuel Combustion: Implication for the Aging Mechanism and Newly Proposed Source Tracers

AbstractPhenolic compounds and aromatic acids, as oxygenated aromatic compounds, can participate in photochemical reactions to form secondary organic aerosols (SOAs), and thus strongly impact climate and human health. In the present study, on‐site combustion experiments were conducted to determine primary emissions and secondary formation of phenolic compounds and aromatic acids released from burning of a variety of solid fuels using a potential aerosol mass‐oxidation flow reactor (PAM‐OFR). Emission factors (EFs) of phenolic compounds and aromatic acids from aged samples were 1.04 to 4.04 and 0.90 to 2.80 times those in the fresh PM2.5, respectively, implying significant amounts of these compounds produced from atmospheric aging processes. Substantially different emission profiles of phenolic compounds were observed between coal and biomass burning, with coal combustion mainly released single‐ring species (82%–86% in primary and 86%–89% in secondary emissions), while biomass burning released more two‐, three‐, and four‐ring species (59%–69% in primary and 50%–58% in secondary emissions). Aromatic acids emission profiles from coal and biomass burning also differed considerably, with biomass burning producing significantly higher (>2 times) abundance of dibasic acids than coal combustion, suggesting higher potential of producing additional ‐COOH group from biomass burning. Benzenediol, cresol, dimethylphenol, 1‐pyrenol, phenanthrenol, and hydroxylbenzonic acid were identified as SOA as they were mainly formed during simulated aging processes. Benzenediol acid/phenanthrenol was much lower from biomass (3.70 ± 1.29) than coal (62.7 ± 9.61), and these values remained stable after aging, suggesting this ratio being suitable as tracer for distinguishing different fuels combustion in source apportionment analysis.

Chemically speciated air pollutant emissions from open burning of household solid waste from South Africa

Abstract. Open burning of household solid waste is a large source of air pollutants worldwide, especially in the Global South. However, waste burning emissions are either missing or have large uncertainties in local, regional, or global emission inventories due to limited emission factor (EF) and activity data. Detailed particulate matter (PM) chemical speciation data are even less available. This paper reports source profiles and EFs for PM2.5 species as well as acidic and alkali gases measured from laboratory combustion of 10 waste categories that represent open burning in South Africa. Carbonaceous materials contributed more than 70 % of PM2.5 mass. Elemental carbon (EC) was most abundant from flaming materials (e.g., plastic bags, textiles, and combined materials), and its climate forcing exceeded the corresponding CO2 emissions by a factor of 2–5. Chlorine had the highest EFs among elements measured by X-ray fluorescence (XRF) for all materials. Vegetation emissions showed high abundances of potassium, consistent with its use as a marker for biomass burning. Fresh PM2.5 emitted from waste burning appeared to be acidic. Moist vegetation and food discards had the highest hydrogen fluoride (HF) and PM fluoride EFs due to fluorine accumulation in plants, while burning rubber had the highest hydrogen chloride (HCl) and PM chloride EFs due to high chlorine content in the rubber. Plastic bottles, plastic bags, rubber, and food discards had the highest EFs for polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs as well as their associated toxicities. Distinct differences between odd and even carbon preferences were found for alkanes from biological and petroleum-based materials: dry vegetation, paper, textiles, and food discards show preference for the odd-numbered alkanes, while the opposite is true for plastic bottles, plastic bags, and rubber. As phthalates are used as plasticizers, their highest EFs were found for plastic bottles and bags, rubber, and combined materials. Data from this study will be useful for health and climate impact assessments, speciated emission inventories, source-oriented dispersion models, and receptor-based source apportionment.

Process-specific volatile organic compounds emission characteristics, environmental impact and health risk assessments of the petrochemical industry in the Beijing-Tianjin-Hebei region.

Volatile organic compounds (VOCs) concentration, source profiles, O3 and SOA formation, and health risks were investigated in the petrochemical industry in Beijing-Tianjin-Hebei. The results showed that total VOCs concentrations were 547.1-1956.5μg·m-3, and alkanes were the most abundant group in all processes (31.4%-54.6%), followed by alkenes (20.6%-29.2%) and aromatics (10.1%-25.1%). Moreover, ethylene (11.3%), iso-pentane (7.1%), n-hexane (5.1%), benzene (4.9%) and 2,2-dimethylbutae (4.8%) were identified as the top five species released for the whole petrochemical industry. The coefficient of divergence between the source profiles from different processes was 0.49-0.73, indicating that most source profiles must not be similar. Moreover, because of the different raw materials and technologies used, the source profiles in this study are significantly different from those of other regions. The ozone and secondary organic aerosol formation potentials (OFPs and SOAPs) were evaluated, suggesting that ethylene, propylene, 1-butene, m,p-xylene, and 1,3-butadiene should be preferentially controlled to reduce OFPs. That benzene, toluene, ethylbenzene, m,p-xylene, isopropylbenzene, o-ethyltoluene, and 1,3,5-trimethylbenzene should be priority control compounds for SOAPs. Additionally, the total hazard ratio for non-cancer risk ranged from 0.9 to 7.7, and only living area was unlikely to be related to adverse health effects. Cancer risks associated with organic chemicals, rubber synthesis, oil refining, and wastewater collection and treatment have definite risks, whereas other processes have probable risks. This study provides a scientific basis for VOCs emission control and management and guides human health in the petrochemical industry.

Total contents, fractionation and bioaccessibility of nine heavy metals in household dust from 14 cities in China

The potential health risk caused by long-term exposure to heavy metals in household dust is not only depended on their total content, but also bioaccessibility. In this study, twenty-one dust samples were collected from residential buildings, schools, and laboratories in 14 provincial-capital/industrial cities of China, aiming to evaluate the total contents, fractionation, bioaccessibility and health risks of nine heavy metals (As, Cd, Cr, Ni, Pb, Mn, Zn, Fe, and Cu). Results showed that the highest levels of Cd, Cr, Ni and Zn were found in laboratory dust, As, Pb and Mn in school dust, and Fe and Cu in residential dust, indicating different source profiles of the heavy metals. The mean bioaccessibility of the heavy metals across all samples as evaluated using SBRC (Solubility Bioavailability Research Consortium), IVG (In Vitro Gastrointestinal), and PBET (Physiologically Based Extraction Test) assays was 58.4%, 32.4% and 17.2% in gastric phase (GP), and 24.9%, 21.9% and 9.39% in intestinal phase (IP), respectively. Cadmium had the highest content in the fractions of E1+C2 (43.7%), as determined by sequential extraction, and Pb, Mn, and Zn had a higher content in E1+C2+F3 (64.2%, 67.2%, 78.8%), resulting in a higher bioaccessibility of these heavy metals than others. Moreover, the bioaccessibility of most heavy metals was inversely related to dust pH (R = −0.18 in GP; −0.18 in IP; P < 0.01) and particle size, while a positive correlation was observed with total organic carbon (R = 0.40 in GP; 0.38 in IP; P < 0.01). The exposure risk calculated by the highest bioaccessibility was generally lower than that calculated by the total content. However, Pb in one school dust sample had an unacceptable carcinogenic risk (adult risk = 1.19 × 10−4; child risk = 1.08 × 10−4). This study suggests that bioaccessibility of heavy metals in household dust is likely related to geochemical fractions and physical/chemical properties. Further research is needed to explore the sources of bioaccessible heavy metals in household dust.

Finite integral transform with homogenized boundary conditions solution of transient heat conduction applied to thermal plug and abandonment of oil wells

After the end of productive life of a geological oil reservoir, a set of operations for well Plug and Abandonment (P&A) is performed to recover the soil layers to its natural state. As traditional P&A techniques are expensive activities, alternative technologies are gaining renewed interest for capital expenditure reduction and leak risks mitigation. Hence, the Thermal Plug and Abandonment (TP&A) procedure is here investigated by focusing on the fulfillment of two intermediate milestones, combining new technology with conventional P&A practices. The first milestone consists in applying a thermite exothermic reaction to generate enough heat for melting the entire thickness of the production tube. The second milestone aims to determine if the resulting molten section has sufficient dimensions to allow for the passage of the cement pumped downhole, thus ensuring the complete sealing of the oil well's cross-section. The thermal investigation was conducted by applying a homogenized form of the Finite Integral Transform (FIT) analytical framework to solve the transient heat conduction equation in 2-D polar coordinates. The well assembly was geometrically discretized as a multilayered circular domain. The thermite reaction was modeled as a theoretical volumetric heat source profile dependent on both time and space, placed in the innermost layer. While a pure FIT approach may only be used to solve Neumann boundary conditions, the combined scheme applied here copes with any type of boundary condition. Hence, the integration of FIT with homogenization satisfies the Dirichlet condition requirement of the TP&A procedure. The methodology was verified through an equivalent Finite Volume Method (FVM) solution obtained using a commercial code. The results evidenced that within the 5 min duration of thermite reaction, the entire circumferential section of the production tube exceeds the steel melting temperature by at least 227 °C, thus fulfilling both milestones set. The research outcomes are part of a series of investigation steps to thoroughly analyze the new TP&A technology with regard to its compliance with the regulatory norms established to P&A operations.

Bulk and molecular-level composition of primary organic aerosol from wood, straw, cow dung, and plastic burning

Abstract. During the past decades, the source apportionment of organic aerosol (OA) in ambient air has been improving substantially. The database of source retrieval model-resolved mass spectral profiles for different sources has been built with the aerosol mass spectrometer (AMS). However, distinguishing similar sources (such as wildfires and residential wood burning) remains challenging, as the hard ionization of the AMS mostly fragments compounds and therefore cannot capture detailed molecular information. Recent mass spectrometer technologies of soft ionization and high mass resolution have allowed for aerosol characterization at the molecular formula level. In this study, we systematically estimated the emission factors and characterized the primary OA (POA) chemical composition with the AMS and the extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) for the first time from a variety of solid fuels, including beech logs, spruce and pine logs, spruce and pine branches and needles, straw, cow dung, and plastic bags. The emission factors of organic matter estimated by the AMS and hydrocarbon gases estimated by the total hydrocarbon analyzer are 16.2 ± 10.8 g kg−1 and 30.3 ± 8.5 g kg−1 for cow dung burning, which is generally higher than that of wood (beech, spruce, and pine), straw, and plastic bag burning (in the range from 1.1 to 6.2 g kg−1 and 14.1 to 19.3 g kg−1). The POA measured by the AMS shows that the f60 (mass fraction of m/z 60) varies from 0.003 to 0.04 based on fuel types and combustion efficiency for wood (beech, spruce, and pine) and cow dung burning. On a molecular level, the dominant compound of POA from wood, straw, and cow dung is C6H10O5 (mainly levoglucosan), contributing ∼ 7 % to ∼ 30 % of the total intensity, followed by C8H12O6 with fractions of ∼ 2 % to ∼ 9 %. However, as they are prevalent in all burning of biomass material, they cannot act as tracers for the specific sources. By using the Mann–Whitney U test among the studied fuels, we find specific potential new markers for these fuels from the measurement of the AMS and EESI-TOF. Markers from spruce and pine burning are likely related to resin acids (e.g., compounds with 20–21 carbon atoms). The product from the pyrolysis of hardwood lignins is found especially in beech log burning. Nitrogen-containing species are selected markers primarily for cow dung open burning. These markers in the future will provide support for the source apportionment.

The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2

Abstract. A chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in reproducing the observations of some major chemical components, for example, sulfate, nitrate, ammonium and organic carbon. Studies have suggested that the uncertainties in the model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile (used to create speciated emission inventories for CTMs) of PM2.5 has not been fully taken into account in current numerical simulation. Based on the characteristics and variation rules of chemical components in typical PM2.5 sources, different simulation scenarios were designed and the sensitivity of simulated PM2.5 components to the source chemical profile was explored. Our findings showed that the influence of source profile changes on simulated PM2.5 components' concentrations cannot be ignored. Simulation results of some components were sensitive to the adopted source profile in CTMs. Moreover, there was a linkage effect: the variation in some components in the source profile would bring changes to the simulated results of other components. These influences are connected to chemical mechanisms of the model since the variation in species allocations in emission sources can affect the potential composition and phase state of aerosols, chemical reaction priority, and multicomponent chemical balance in thermodynamic equilibrium systems. We also found that the perturbation of the PM2.5 source profile caused variation in simulated gaseous pollutants, which indirectly indicated that the perturbation of source profile would affect the simulation of secondary PM2.5 components. Our paper highlights the necessity of paying enough attention to the representativeness and timeliness of the source profile when using CTMs for simulation.