Evaluating dust emission model performance using dichotomous satellite observations of dust emission

Measurements of dust in the atmosphere have long been used to calibrate dust emission models. However, there is growing recognition that atmospheric dust confounds the magnitude and frequency of emission from dust sources and hides potential weaknesses in dust emission model formulation. In the satellite era, dichotomous (presence = 1 or absence = 0) observations of dust emission point sources (DPS) provide a valuable inventory of regional dust emission. We used these DPS data to develop an open and transparent framework to routinely evaluate dust emission model (development) performance using coincidence of simulated and observed dust emission (or lack of emission). To illustrate the utility of this framework, we evaluated the recently developed albedo-based dust emission model (AEM) which included the traditional entrainment threshold (u*ts) at the grain scale, fixed over space and static over time, with sediment supply infinite everywhere. For comparison with the dichotomous DPS data, we reduced the AEM simulations to its frequency of occurrence in which soil surface wind friction velocity (us*) exceeds the u*ts, P(us* > u*ts). We used a global collation of nine DPS datasets from established studies to describe the spatio-temporal variation of dust emission frequency. A total of 37,352 unique DPS locations were aggregated into 1,945 1° grid boxes to harmonise data across the studies which identified a total of 59,688 dust emissions. The DPS data alone revealed that dust emission does not usually recur at the same location, are rare (1.8 %) even in North Africa and the Middle East, indicative of extreme, large wind speed events. The AEM over-estimated the occurrence of dust emission by between 1 and 2 orders of magnitude. More diagnostically, the AEM simulations coincided with dichotomous observations ~71 % of the time but simulated dust emission ~27 % of the time when no dust emission was observed. Our analysis indicates that u*ts was typically too small, needed to vary over space and time, and at the grain-scale u*ts is incompatible with the us* scale (MODIS 500 m). During observed dust emission, us* was too small because wind speeds were too small and/or the wind speed scale (ERA5; 11 km) is incompatible with the us* scale. The absence of any limit to sediment supply caused the AEM to simulate dust emission whenever P (us* > u*ts), producing many false positives when and where wind speeds were frequently large. Dust emission model scaling needs to be reconciled and new parameterisations are required for u*ts and to restrict sediment supply varying over space and time. Whilst u*ts remains poorly constrained and unrealistic assumptions persist about sediment supply and availability, the DPS data provide a basis for the calibration of dust emission models for operational use. As dust emission models develop, these DPS data provide a consistent, reproducible, and valid framework for their routine evaluation and potential model optimisation. This work emphasises the growing recognition that dust emission models should not be evaluated against atmospheric dust.

Research on the construction of carbon emission model of power generation industry in Jilin province

Although Jilin Province has abundant forest reserves and has a relatively large carbon neutral advantage compared to other provinces, the installed capacity of thermal power is still relatively high, and the installed capacity of renewable energy such as wind power, photovoltaic and hydropower is insufficient. This paper builds a carbon emission model for the power generation industry in Jilin Province based on the characteristics of the power generation industry in Jilin Province and years of field test experience.

A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.

Vinyl Acetate Emission Rates and Explosions in Tank Farms in Dilovasi and Yumurtalik, Turkey: A Case Study

The emission estimations for vinyl acetate from storage tanks located in Dilovasi and Yumurtalik, Turkey, were completed by using the US EPA standard regulatory storage tanks emission model (TANKS 4.9b). Total annual emission was determined to be 7,603.15 kg/year for Yumurtalik and 6,057.06 kg/year for Dilovasi. In addition, ALOHA software was used in order to define emergency responses required in the case of vinyl acetate leakage based on different scenarios. According to ALOHA program modelling results, the threat regions occurred were 113 and 236 m for the red threat region, 299 and 663 m for the orange threat region, and 790 m and 2.0 km for the yellow threat region for vinyl acetate toxic vapour in Dilovasi and Yumurtalik, respectively. The threat regions determined were 10 and 15 m for the red threat region, 9.14 m for orange threat region, and 20 and 49 m for the yellow threat region for modelling of flammable area for the vapour cloud of vinyl acetate in Dilovasi and Yumurtalik, respectively. The amount of thermal radiation was determined to be 10 kW/m2 at a distance of 9.96 m from the tanks in both Dilovasi and Yumurtalik during a jet fire.

The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Nonthermal Radiation of the Extreme TeV Blazar 1ES 0229+200 from Electromagnetic Cascades on Infrared Photon Field

Extreme TeV blazars (ETBs) are active galactic nuclei with jets presumably pointing towards the observer having their intrinsic (compensated for the effect of γ-ray absorption on extragalactic background light photons) spectral energy distributions (SEDs) peaked at an energy in excess of 1 TeV. These sources typically reveal relatively weak and slow variability as well as higher frequency of the low-energy SED peak compared to other classes of blazars. It proved to be exceedingly hard to incorporate all these peculiar properties of ETBs into the framework of conventional γ-ray emission models. ETB physics have recently attracted great attention in the astrophysical community, underlying the importance of the development of self-consistent ETB emission model(s). We propose a new scenario for the formation of X-ray and γ-ray spectra of ETBs assuming that electromagnetic cascades develop in the infrared photon field surrounding the central blazar engine. This scenario does not invoke compact fast-moving sources of radiation (so-called “blobs”), in agreement with the apparent absence of fast and strong variability of ETBs. For the case of the extreme TeV blazar 1ES 0229+200 we propose a specific emission model in the framework of the considered scenario. We demonstrate that this model allows to obtain a good fit to the measured SED of 1ES 0229+200.

The “Bi-drifting” subpulses of PSR J0815+0939 observed with the Five-hundred-meter Aperture Spherical radio Telescope

We report the “Bi-drifting” subpulses observed in PSR J0815+0939 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The observation at band from 1050-1450MHz is evenly divided into two bands, i.e., the bands at center frequencies 1150MHz and 1350 MHz. The mean pulse profiles and the “Bi-drifting” subpulses at this two bands are investigated. It is found that the pulse profiles at this two frequencies show four emission components, and the peak separations between four emission components decrease with the increase of frequency. In addition, the ratio of peak intensity of each component to the intensity of component IV at 1150MHz is larger than that at 1350 MHz. We carry out an analysis of the longitude-resolved fluctuation spectrum and two-dimensional fluctuation spectrum for each emission component, and find that the P3 of components I, II and III are about 10.56, 10.57 and 10.59 s at 1150MHz and 1350 MHz. However, the reliable measurements of P3 of component IV and P2 for these four components were not obtained due to the low signal-to-noise ratio of observation data. The pulse energy distributions at frequencies 1150 and 1350MHz are presented, and it is found that no nulling phenomenon have been found in this pulsar. With our observation from the FAST, the “Bi-drifting” subpulse phenomenon of PSR J0815+0939 is expanded from 400MHz to 1350 MHz, which is helpful for the relevant researchers to test and constrain the pulsar emission model, especially the model of “Bi-drifting” subpulse.

Barrier properties and current conduction mechanism for metal contacts to lightly and highly doped p-type 4H-SiC

The barrier properties of Ti, Ni and Pt contact to lightly (9×1016 cm-3) and highly (9×1018 cm-3) doped p-type 4H-SiC were investigated. It is found that the barrier heights and ideality factors estimated from thermionic emission model for the lightly doped samples are non-ideal and abnormally temperature dependent. The anomalies have been successfully explained in terms of both pinch-off model and Gaussian distribution of inhomogeneous barrier heights. In addition, the evaluated homogeneous barrier heights are reasonably close to the average barrier heights from capacitance-voltage measurements. For the highly doped samples, thermionic field emission (TFE) is found to be the dominant carrier transport mechanism. The barrier heights estimated from TFE model are temperature independent. If the barrier inhomogeneities and tunneling effects are considered, the experimental results of the samples are in well agreement with the theoretical calculations.