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.

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

Evaluation of Aviation Emissions and Environmental Costs in Europe Using OpenSky and OpenAP

In this paper, we propose a data-driven approach that estimates cruise-level flight emissions over Europe using OpenSky ADS-B data and OpenAP emission models. Flight information, including position, altitude, speed, and the vertical rate are obtained from the OpenSky historical database, gathered at a sample rate of 15 s. Emissions from each flight are estimated at a 30-s time interval. This study makes use of the first four months of flights in 2020 over the major part of Europe. The dataset covers the period before and at the start of the COVID-19 pandemic. The aggregated results show cruise-level flight emissions by different airlines, geographic regions, altitudes, and timeframe (e.g., weeks). We also estimate environmental costs associated with aviation in Europe by using marginal cost values from the literature. Overall, we have demonstrated how open flight data from OpenSky can be employed to rapidly assess aviation emissions at varying spatio-temporal resolutions on a continental scale.

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.

Photospheric Prompt Emission From Long Gamma-ray Burst Simulations. I. Optical Emission

A complete understanding of gamma-ray bursts (GRBs) has been difficult to achieve, due to our incomplete knowledge of the radiation mechanism that is responsible for producing the prompt emission. This emission, which is detected in the first tens of seconds of the GRB, is typically dominated by hard X-ray and gamma-ray photons, although there have also been a few dozen prompt optical detections. These optical detections have the potential to discriminate between plausible prompt emission models, such as the photospheric and synchrotron shock models. In this work, we use an improved MCRaT code, which includes cyclo-synchrotron emission and absorption, to conduct radiative transfer calculations from optical to gamma-ray energies under the photospheric model. The calculations are conducted using a set of two-dimensional relativistic hydrodynamic long GRB jet simulations, consisting of a constant and a variable jet. We predict the correlations between the optical and gamma-ray light curves as functions of observer angle and jet variability, and find that there should be extremely dim optical prompt precursors for large viewing angles. Additionally, the detected optical emission originates from dense regions of the outflow, such as shock interfaces and the jet-cocoon interface. Our results also show that the photospheric model is unable to account for the current set of optical prompt detections that have been made and therefore additional radiative mechanisms are needed to explain these prompt optical observations. These findings show the importance of conducting global radiative transfer simulations using hydrodynamically calculated jet structures.

Constraining positron emission from pulsar populations with AMS-02 data

The cosmic-ray flux of positrons is measured with high precision by the space-borne particle spectrometer AMS-02. The hypothesis that pulsar wind nebulae (PWNe) can significantly contribute to the excess of the positron (e+) cosmic-ray flux has been consolidated after the observation of a γ-ray emission at TeV energies of a few degree size around Geminga and Monogem PWNe. In this work we undertake massive simulations of galactic pulsars populations, adopting different distributions for their position in the Galaxy, intrinsic physical properties, pair emission models, in order to overcome the incompleteness of the ATNF catalog. We fit the e+ AMS-02 data together with a secondary component due to collisions of primary cosmic rays with the interstellar medium. We find that several mock galaxies have a pulsar population able to explain the observed e+ flux, typically by few, bright sources. We determine the physical parameters of the pulsars dominating the e+ flux, and assess the impact of different assumptions on radial distributions, spin-down properties, Galactic propagation scenarios and e+ emission time.