Emission Model Research Articles

Characterizing and Predicting nvPM Size Distributions for Aviation Emission Inventories and Environmental Impact.

Concerns about civil aviation's air quality and environmental impacts have led to recent regulations on nonvolatile particulate matter (nvPM) mass and number emissions. Although these regulations do not mandate measuring particle size distribution (PSD), understanding PSDs is vital for assessing the environmental impacts of aviation nvPM. This study introduces a comprehensive data set detailing PSD characteristics of 42 engines across 19 turbofan types, ranging from unregulated small business jets to regulated large commercial aircraft. Emission tests were independently performed by using the European and Swiss reference nvPM sampling and measurement systems with parallel PSD measurements. The geometric mean diameter (GMD) at the engine exit strongly correlated with the nvPM number-to-mass ratio (N/M) and thrust, varying from 7 to 52 nm. The engine-exit geometric standard deviation ranged from 1.7 to 2.5 (mean of 2.05). The study proposes empirical correlations to predict GMD from N/M data of emissions-certified engines. These predictions are expected to be effective for conventional rich-burn engines and might be extended to novel combustor technologies if additional data become available. The findings support the refinement of emission models and help in assessing the aviation non-CO2 climate and air quality impacts.

The electrical transport property and gas detection behaviour of polyvinyl alcohol and γ-Fe2O3 nanocomposite film

This paper presents the electrical and gas detection behaviour of polyvinyl alcohol (PVA)/γ-Fe2O3 nanocomposite films. The physical characterization includes X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and UV–Visible spectroscopy. The analysis of both the dc resistivity and ac conductivity using Correlated Barrier Hoping (CBH) conduction process shows that the conductivity enhances with the decreasing of activation energy in case of the PVA/γ-Fe2O3 nanocomposite film which is the indication of interfacial defect states. The current voltage study at various temperatures follows the thermoionic emission model of Schottky Barrier diode. Moreover, the dielectric constant enhances for PVA/γ-Fe2O3 nanocomposite film, which indicates homogeneous distribution of γ-Fe2O3 nanoparticles within PVA matrix and the aggregation of trapped charges found in interface develops an interfacial polarization. As a result, the gas adsorption capacity of the nanocomposite film increases which improves the signal-to-noise ratio, leading to higher gas detection sensitivity.

Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

Numerical prediction model for long- and short-term concentration of indoor volatile organic compounds from building materials

ABSTRACT Emission models of volatile organic compounds (VOCs) from individual indoor building materials have been developed and validated. However, multiple indoor building materials release VOCs simultaneously, and neither single building material nor multiple building material emission models can predict the entire release cycle of VOCs accurately. This study established a long- and short-term numerical prediction model for indoor VOC concentration. The model includes an attenuation coefficient θ. To describe the decay rate of the total VOC content, which is mainly influenced by time, and by designing experiments and testing in environmental warehouses under different seasonal conditions, the value of θ was first obtained. Then, after successfully plotting the emission curve of indoor pollutant concentration over time through numerical solution and using θ, the VOC content was corrected for various seasonal conditions. On the basis of this model, an exposure dose integration algorithm was proposed to evaluate the environmental health risks, as an application of this model. In comparison with previous research results and experimental data, this model has better predictive performance.

Analysis of Carbon Emission Reduction Paths for Ships in the Yangtze River: The Perspective of Alternative Fuels

In recent years, carbon emission reduction in the shipping sector has increasingly garnered scholarly attention. This study delves into the pathways for carbon emission reduction in shipping across the Yangtze River, emphasizing fuel alternatives. It initiates by introducing a novel ship carbon emission calculation methodology predicated on voyage data, followed by the development of a predictive model for ship carbon emissions tailored to specific voyages. Then, emission reduction scenarios for various voyage categories are designed and exemplary alternative fuels selected to assess their potential for emission mitigation. Subsequently, scenario analysis is employed to scrutinize the CO2 emission trajectories under diverse conditions, pinpointing the most efficacious route for carbon emission abatement for inland vessels. Finally, the proposed method is applied to the middle and lower reaches of the Yangtze River. The results indicate that accelerating the adoption of alternative fuels for long-distance cargo ships would greatly accelerate the development of environmentally friendly shipping. Under a scenario prioritizing zero-carbon growth, emissions from inland vessels are anticipated to reach their zenith by 2040. These findings can provide theoretical guidance for emission reductions in inland shipping and effectively promote the green and sustainable development of the shipping sector.

AGNfitter-rx : Modeling the radio-to-X-ray spectral energy distributions of AGNs

We present new advancements in the modeling of the spectral energy distributions (SEDs) of active galaxies by introducing the radio-to-X-ray fitting capabilities of the publicly available Bayesian code AGNfitter . The new code release, called AGNfitter-rx models the broad-band photometry covering the radio, infrared (IR), optical, ultraviolet (UV), and X-ray bands consistently using a combination of theoretical and semi-empirical models of the active galactic nucleus (AGN) and host-galaxy emission. This framework enables the detailed characterization of four physical components of the active nuclei, namely the accretion disk, the hot dusty torus, the relativistic jets and core radio emission, and the hot corona, and can be used to model three components within the host galaxy: stellar populations, cold dust, and the radio emission from the star-forming regions. Applying AGNfitter-rx to a diverse sample of 36 AGN SEDs at $z 0.7$ from the AGN SED ATLAS, we investigated and compared the performance of state-of-the-art torus and accretion disk emission models in terms of fit quality and inferred physical parameters. We find that clumpy torus models that include polar winds and semi-empirical accretion disk templates including emission-line features significantly increase the fit quality in 67<!PCT!> of the sources by reducing by $2 fit residuals in the $1.5-5 1mm m$ and $0.7 1mm m$ regimes. We demonstrate that, by applying AGNfitter-rx to photometric data, we are able to estimate the inclination and opening angles of the torus, consistent with spectroscopic classifications within the AGN unified model, as well as black hole masses congruent with virial estimates based on Halpha . We investigate wavelength-dependent AGN fractions across the spectrum for Type 1 and Type 2 AGNs, finding dominant AGN fractions in radio, mid-infrared, and X-ray bands, which are in agreement with the findings from empirical methods for AGN selection. The wavelength coverage and the flexibility for the inclusion of state-of-the-art theoretical models make AGNfitter-rx a unique tool for the further development of SED modeling for AGNs in present and future radio-to-X-ray galaxy surveys.

Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir

AbstractExtreme hydrological and thermal regimes characterize the Mediterranean zone and can influence the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH4 emissions substantially increased during stratification, influenced predominantly by the increase in water temperature, net ecosystem production, and the decline in reservoir mean depth. N2O emissions mirrored CH4's seasonal trend, significantly correlating to water temperature, wind speed, and gross primary production. Conversely, CO2 emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature—an unexpected deviation from CH4 and N2O emission patterns—likely associated with photosynthetic uptake of bicarbonate and formation of intracellular calcite that might be exported to sediments. This investigation highlights the imperative of integrating these idiosyncratic patterns into GHG emissions models, enhancing their predictive power.

Life cycle approach for evaluating the environmental and economic viability of low-noise asphalt pavements

This paper aims to make recommendations about the convenience of using low-noise pavements to mitigate traffic noise under different traffic and population scenarios. To do so, a life cycle approach has been applied by performing a Life-cycle Assessment (LCA) and Life-cycle Cost Analysis (LCCA). To account for the noise mitigation achieved by the road surfaces, which is the main benefit compared to conventional road surfaces, the damage effect of traffic noise has been incorporated in both analyses. Thus, open source noise emission and propagation modelling and Geographical Information System (GIS) tools have been used to determine the noise level at which residents are exposed. The impact of noise emissions on human health is estimated and expressed in disability-adjusted life years (DALYs) and the related external cost have been calculated by monetarizing the environmental impacts and by integrating them within the cost assessment. The methodology followed allowed the comparison of different scenarios and the results show a huge potential for reducing environmental impacts and costs when using low-noise road surfaces although accurate information about the acoustic aging of the materials is key in order to ensure that benefits are obtained.

Loss of Coherence and Change in Emission Physics for Radio Emission from Very Inclined Cosmic-Ray Air Showers.

Next-generation radio experiments such as the radio detector of the upgraded Pierre Auger Observatory and the planned GRAND and BEACON arrays target the detection of ultra-high-energy particle air showers arriving at low elevation angles. These inclined cosmic-ray air showers develop higher in the atmosphere than vertical ones, enhancing magnetic deflections of electrons and positrons inside the cascade. We evidence two novel features in their radio emission: a new polarization pattern, consistent with a geosynchrotron emission model and a coherence loss of the radio emission, both for showers with zenith angle θ≳65° and strong enough magnetic field amplitude (typical strength of B∼50 μT). Our model is compared with both ZHAireS and CoREAS MonteCarlo simulations. Our results break the canonical description of a radio signal made of Askaryan and transverse current emission only, and provide guidelines for the detection and reconstruction strategies of next-generation experiments, including cosmic-ray or neutrino discrimination.

Predictive modeling of engine performance and emissions for castor oil ethyl ester biodiesel blends: A Gaussian process regression approach

Replacing fossil fuels with cleaner alternatives is essential. This study examines a biodiesel-diesel blend containing 8 % castor oil ethyl ester (COEE8) and its impact on engine performance, combustion characteristics, and exhaust emissions. A single-cylinder diesel engine was tested under consistent conditions: an engine speed of 1500 rpm, varying engine loads (25 %, 50 %, and 75 % of maximum torque), and compression ratios (16, 17, and 18). Engine-out emissions were measured with Testo flue gas analyzers. The results showed that COEE8 combustion significantly decreased HC, CO, and smoke emissions compared to diesel fuel but increased NOx emissions. Additionally, COEE8 exhibited comparable brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) to diesel fuel. The optimal engine operating parameters were determined using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a multi-objective optimization technique. Due to limited data availability, Gaussian Process Regression (GPR), a machine learning algorithm for small datasets, modeled the multi-objective functions with compression ratio and engine load as input variables. The GPR model demonstrated high prediction performance across all output parameters (BSFC, BTE, HC, smoke, NOx, and CO) for both diesel and COEE8 fuels, with average coefficients of determination (R2) of 0.9896 and 0.9953, respectively, indicating a strong correlation between predicted and actual values. The mean absolute percentage error (MAPE) was also low, averaging 3.11 % and 2.26 %, respectively, demonstrating the model's accuracy. Using the GPR model, the NSGA-II identified the optimal trade-off between NOx and smoke index for both diesel and COEE8 fuels. The optimal compression ratio was 16 for both fuels, while the optimal engine load varied, ranging from 20 % to 30 % for COEE8 and around 30 % (almost 40 %) for diesel fuel.

Railway vibration emission model based on a multi-contact source-receiver mobility approach

Railway vibration emission model based on a multi-contact source-receiver mobility approach

Swift X-ray and UV observations of six Gaia binaries supposedly containing a neutron star

Recent observations have led to the discovery of numerous optically selected binaries containing an undetected component with mass consistent with a compact object (neutron star (NS) or white dwarf). Using the Neil Gehrels Swift Observatory, we carried out X-ray and UV observations of a small sample of these binaries. Four systems are wide (with an orbital period P > 300 d), and were chosen because of their small distance (d < 250 pc) and because the mass of the collapsed component favors a NS. Two others are compact systems (P < 0.9 d) that show strong signs of containing a NS. The source 2MASS J15274848+3536572 was detected in the X-ray band with a flux of 5 × 10−13 erg cm−2 s−1 and a spectrum well fitted by a power law or a thermal plasma emission model. This source also shows a UV (2200 Å) excess, which might indicate the presence of mass accretion. For the other targets, we derived X-ray flux upper limits of the order of 10−13 erg cm−2 s−1. These results are consistent with the hypothesis that the collapsed component in these six systems is a NS.

Revisiting the abundance pattern and charge-exchange emission in the centre of M 82

Context. The interstellar medium (ISM) in starburst galaxies contains many chemical elements that are synthesised by core-collapse supernova explosions. By measuring the abundances of these metals, we can study the chemical enrichment within the galaxies and the transportation of metals into the circumgalactic environment through powerful outflows. Aims. We performed a spectral analysis of the X-ray emissions from the core of M 82 using the Reflection Grating Spectrometer (RGS) on board XMM-Newton to accurately estimate the metal abundances in the ISM. Methods. We analysed over 300 ks of RGS data observed with 14 position angles, covering a cross-dispersion width of 80 arcsec. We employed multi-temperature thermal plasma components in collisional ionisation equilibrium (CIE) to reproduce the observed spectra, each of which exhibited a different spatial broadening. Results. The O VII band CCD image shows a broader distribution that those for the O VIII and Fe-L bands. The O VIII line profiles have a prominent double-peaked structure that corresponds to the north- and southward outflows. The O VII triplet feature exhibits marginal peaks. A single CIE component that is convolved with the O VII band image approximately reproduces the spectral shape. A CIE model combined with a charge-exchange emission model also successfully reproduces the O VII line profiles. However, the ratio of these two components varies significantly with the observed position angles, which is physically implausible. Spectral fitting of the broadband spectra suggests a multi-temperature phase in the ISM that is approximated by three components at 0.1, 0.4, and 0.7 keV. Notably, the 0.1 keV component exhibits a broader distribution than the 0.4 and 0.7 keV plasmas. The derived abundance pattern shows super-solar N/O, solar Ne/O and Mg/O, and half-solar Fe/O ratios. These results indicate the chemical enrichment by core-collapse supernovae in starburst galaxies.

Broadband near-infrared emission in silicon waveguides

Silicon photonic integrated circuit foundries enable wafer-level fabrication of entire electro-optic systems-on-a-chip for applications ranging from datacommunication to lidar to chemical sensing. However, silicon’s indirect bandgap has so far prevented its use as an on-chip optical source for these systems. Here, we describe a fullyintegrated broadband silicon waveguide light source fabricated in a state-of-the-art 300-mm foundry. A reverse-biased p-i-n diode in a silicon waveguide emits broadband near-infrared optical radiation directly into the waveguide mode, resulting in nanowatts of guided optical power from a few milliamps of electrical current. We develop a one-dimensional Planck radiation model for intraband emission from hot carriers to theoretically describe the emission. The brightness of this radiation is demonstrated by using it for broadband characterization of photonic components including Mach-Zehnder interferometers and lattice filters, and for waveguide infrared absorption spectroscopy of liquid-phase analytes. This broadband silicon-based source can be directly integrated with waveguides and photodetectors with no change to existing foundry processes and is expected to find immediate application in optical systems-on-a-chip for metrology, spectroscopy, and sensing.

Searching for gamma-ray emission from stellar flares

Abstract Flares from magnetically active dwarf stars should produce relativistic particles capable of creating γ-rays. So far, the only isolated main sequence star besides the Sun to have been detected in γ-rays is TVLM 513-46546. Detecting γ-ray flares from more dwarf stars can improve our understanding of their magnetospheric properties, and could also indicate a diminished likelihood of their planets’ habitability. In this work, we stack data from the Fermi Gamma-ray Space Telescope during a large number of events identified from optical and X-ray flare surveys. We report an upper limit of γ-ray emission from the population of flare stars. Stacking results towards control positions are consistent with a non-detection. We compare these results to observed Solar γ-ray flares and against a model of emission from neutral pion decay. The upper limit is consistent with Solar flares when scaled to the flare energies and distances of the target stars. As with Solar flares, the neutral pion decay mechanism for γ-ray production is also consistent with these results.

The effect of grain size on diode parameters of Ag/p-NiOx/n-Si/Al based Schottky diode

The effect of grain size on optical properties and designed Ag/p-NiOx/n-Si/Al-based Schottky diode have been investigated. The XRD patterns are used to calculate the grain size of NiOx film, which increases with increasing thickness. Fractal dimensions were estimated from the power spectral density (PSD) algorithm using AFM images. The band gaps were calculated from recorded transmission spectra, showing the reduction of band gap from 3.88 eV to 3.67 eV with increasing grain size. The effect of grain size on the performance of Ag/p-NiOx/n-Si/Al-based Schottky-diodes are analyzed through I-V measurement. The diode parameters of the designed device were calculated using the thermionic emission, Norde, and Cheung models. It is found that the reduction of barrier height of the Schottky diode from 0.95 eV to 0.84 eV with increasing grain size of NiOx films. It is found that the grain size of film strongly impacted on the diode parameters, such as ideality factor (η), potential barrier (φh), series resistance (Rs) performance.

Systematic underestimation of polycyclic aromatic hydrocarbon aqueous concentrations in rivers

Polycyclic aromatic hydrocarbons (PAHs) are a widespread group of organic contaminants whose presence in water bodies is cause of severe concern. With few exceptions, the majority of PAHs is hydrophobic, presents a high adsorption affinity, and is thus primarily transported within river systems during high-flow events together with suspended particulate matter (SPM). Evidence exists of analytical challenges related to the incomplete extraction of PAHs adsorbed to solids and thus to a potential negative bias in the chemical analysis of PAHs in bulk water samples with high SPM content. Despite this, partly due to the elevated efforts required to collect representative samples containing sufficient SPM for the separate PAH analysis in this matrix, several investigations rely on the analysis of aqueous samples. This study tests the hypothesis that surveys based exclusively on bulk water may lead to a systematic underestimation of the real contamination level and transport of PAHs in rivers. Six high-turbidity events were examined in three Austrian rivers applying time-integrated sampling and simultaneously analyzing PAHs in total bulk water, filtered water, SPM, and supernatant. Despite an unavoidable degree of uncertainty in such challenging sampling scheme, the results indicate that measurements performed with best available standard methods in bulk water samples determined in average only about 40% of the theoretically expected total PAHs concentrations derived from the analyses in SPM. Such deviation has important implications for the reliable assessment of the compliance with environmental quality standards as well as for surveys aimed to estimate riverine loads, validate emission models, and understand the transport dynamics of PAHs in rivers. Whereas the first objective, e.g., in European countries, is alternatively achieved via monitoring in biota, the latter ones require efforts directed to complement monitoring campaigns with separate sampling of SPM, with monitoring of suspended solids transport to appropriately select and interpret the results of water samples and to improve the chemical analysis of PAHs in bulk water samples with high solids content.

Quantification of methane oxidation measuring isotopic signal in 13C on Spanish landfills

Landfill emissions, particularly methane leaks detected by satellite, are drawing great attention in the last years as a mean for evaluate their contribution to the global warming effect. In contrast, models like IPCC often overestimate landfill methane emissions, prompting verification and mitigation system evaluation. Methane's higher warming capacity than carbon dioxide underscores the importance of promoting its oxidation as it traverses landfill layers. This oxidation raises the CH4–13C/12C ratio via bacterial biooxidation. This study quantified this fractionation using soil and surface gas samples from Spanish landfills and their degassing systems. Sampling relied on walkover surveys collecting samples in Tedlar bags, to analyze isotopic signals in the laboratory using WS-CRDS. Fractionation factors (α) ranged from 1.020 to 1.030, while the oxidized fraction (fox) spanned from no oxidation to 55% (δ13C of −45.83‰). Each ratio correlates with emission types like fugitive and dispersed emissions on plateaus, berms, slopes, sealing cracks, or pits. Understanding the methane oxidized fraction in each landfill is relevant for greenhouse gas emission model integration.

Modeling of MoS2/Si heterostructure to study charge transfer dynamics

Here, we synthesized a MoS2/Si heterojunction device using a scalable approach involving DC sputtering coupled with sulfurization. The observed current–voltage characteristics unequivocally indicate a rectifying behavior at MoS2/Si heterointerface. To quantitatively assess the carrier dynamics, a comprehensive analysis utilizing thermionic emission and Landauer transport formalism model was employed. The spatial variation in current across the MoS2/Si devices suggests a potential influence of MoS2’s in-plane series resistance. Furthermore, the electrical behavior of the device is found to be temperature-dependent, with higher temperatures resulting in enhanced conductivity attributed to an increase in thermally generated charge carriers. As temperature rises, the Landauer current model observes an increased ratio of density of states to carrier injection rate, along with other temperature-dependent terms. Meanwhile, the thermionic current model maintains a fixed effective value for its material-dependent term, the Richardson constant, irrespective of temperature changes. Therefore, a comparative analysis between thermionic emission and Landauer transport formalism reveals that the conventional thermionic emission model better aligns with experimentally observed leakage current in reverse bias, showcasing a minimal barrier height at the heterojunction. This comprehensive investigation provides valuable insights into the charge transfer mechanisms at the MoS2/Si interface, opening avenues for its potential innovative applications in electronic devices.

The ALMA-QUARKS Survey. II. The ACA 1.3 mm Continuum Source Catalog and the Assembly of Dense Gas in Massive Star-Forming Clumps

Leveraging the high resolution, sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for “Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures”, is observing 139 massive star-forming clumps at ALMA Band 6 (λ ∼ 1.3 mm). This paper introduces the Atacama Compact Array (ACA) 7 m data of the QUARKS survey, describing the ACA observations and data reduction. Combining multi-wavelength data, we provide the first edition of QUARKS atlas, offering insights into the multiscale and multiphase interstellar medium in high-mass star formation. The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources. Their gas kinetic temperatures are estimated using three formaldehyde transitions with a non-LTE radiation transfer model, and the mass and density are derived from a dust emission model. The ACA sources are massive (16–84 percentile values of 6–160 M ⊙), gravity-dominated (M ∝ R 1.1) fragments within massive clumps, with supersonic turbulence ( M>1 ) and embedded star-forming protoclusters. We find a linear correlation between the masses of the fragments and the massive clumps, with a ratio of 6% between the two. When considering fragments as representative of dense gas, the ratio indicates a dense gas fraction (DGF) of 6%, although with a wide scatter ranging from 1% to 10%. If we consider the QUARKS massive clumps to be what is observed at various scales, then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation. With the ACA data over four orders of magnitude of luminosity-to-mass ratio (L/M), we find that the DGF increases significantly with L/M, which indicates clump evolutionary stage. We observed a limited fragmentation at the subclump scale, which can be explained by a dynamic global collapse process.