Transient Emissions Research Articles

Transient NOx emission modeling of a hydrogen-diesel engine using hybrid machine learning methods

One promising approach to reduce carbon foot print of internal combustion engines (ICEs) is using alternative fuels like hydrogen, particularly by converting medium and heavy-duty diesel engines to dual-fuel hydrogen-diesel engines. To minimize elevated NOx emissions from hydrogen-fueled engine, fast and accurate emission models are essential for engine model-based control and for engine calibration and optimization using hardware-in-the-loop (HIL) setups. In this study, a fast-response NOx emissions sensor is used to measure the transient NOx emissions from a dual-fuel hydrogen-diesel engine. Subsequently, steady-state models (SSMs), quasi steady-state models (QSSMs), and transient sequential models (TSMs) in the form of black-box (BB) and gray-box (GB) models are developed for transient NOx emissions prediction. GB models utilize both information from a one dimensional (1D) physical engine model and experimental data for training, while BB models only use experimental data. SSMs are optimized artificial neural networks (ANNs) trained using steady-state data, QSSMs are optimized ANNs trained using transient data, and TSMs are time-series networks trained using transient data. Long short-term memory (LSTM) and gated recurrent unit (GRU) networks are used as the time-series deep learning networks. The results showed that the 1D physical model has the poorest performance with successive model performance improvement from SSM to QSSM and from QSSM to TSM. The developed BB TSM model in this study can predict transient NOx emissions with an R2 value greater than 0.96 at 89,000 predictions per second which makes this model suitable for real-time engine model-based control where computational efficiency is crucial. The developed GB TSM model can predict transient NOx emissions with an R2 value greater than 0.97 but it is computationally more expensive. The extra accuracy of the GB TSM models makes them the best choice for HIL setups where more computational power is available, and accuracy is more crucial.

Closing the Net on Transient Sources of Ultrahigh-energy Cosmic Rays

The arrival directions of ultrahigh-energy cosmic rays (UHECRs) observed above 4 × 1019 eV provide evidence of localized excesses that are key to identifying their sources. We leverage the 3D matter distribution from optical and infrared surveys as a density model of UHECR sources, which are considered to be transient. Agreement of the sky model with UHECR data imposes constraints on both the emission rate per unit matter and the time spread induced by encountered turbulent magnetic fields. Based on radio measurements of cosmic magnetism, we identify the Local Sheet as the magnetized structure responsible for the kiloyear duration of UHECR bursts for an observer on Earth and find that the turbulence amplitude must be within 0.5–20 nG for a coherence length of 10 kpc. At the same time, the burst-rate density must be above 50 Gpc−3 yr−1 for Local Sheet galaxies to reproduce the UHECR excesses and below 5000 Gpc−3 yr−1 (30,000 Gpc−3 yr−1) for the Milky Way (Local Group galaxies) not to outshine other galaxies. For the transient emissions of protons and nuclei to match the energy spectra of UHECRs, the kinetic energy of the outflows responsible for UHECR acceleration must be below 4 × 1054 erg and above 5 × 1050 erg (2 × 1049 erg) if we consider the Milky Way (or not). The only stellar-sized transients that satisfy both Hillas’ and our criteria are long-duration gamma-ray bursts.

Transient Emissions Forecasting of Off-Road Construction Machinery Based on Long Short-Term Memory Network

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO2, and NOx, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO2, and NOx emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery.

Assessment of transient hydrogen sulfide peak emissions caused by biogas plant operation

Odor nuisance is a major impediment for public acceptance and wider implementation of biogas plants. Knowledge on composition and dynamics of emissions from these plants is highly needed for development of robust mitigation solutions. In this study, odorant emissions were investigated from two biogas plants of different sizes and capacities. Measurements using proton-transfer-reaction mass spectrometer (PTR-MS) revealed that hydrogen sulfide was the main odorant and constituted 82% and 71% of the odor activity sums (SOAV) at the two studied plants, respectively. Long-term time-resolved measurements furthermore revealed that emissions were highly dynamic with distinct short-term emission peaks. By deploying an array of electrochemical sensors, the main sources of hydrogen sulfide emission were subsequently identified and correlated with operational data to identify the major causes for the highly fluctuating emissions. Addition of biomasses in the form of animal slurry to pre-tanks, were shown to be the major cause of periodic hydrogen sulfide emission within the range of 50–1200 ppm. Such hydrogen sulfide peak emissions could challenge odor abatement from biogas plants and should be taken into account when designing odor abatement technologies for biogas plants.

Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine.

With emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine development and computational modelling has led to the generation of a large amount of high-fidelity emissions and in-cylinder data, making it attractive to use data-driven emissions prediction and control models. While pure data-driven methods have shown robustness in NOx prediction during steady-state engine operation, deficiencies are found under transient operation and at engine conditions far outside the training range. Therefore, physics-based, mean value models that capture cyclic-level changes in in-cylinder thermo-chemical properties appear as an attractive option for transient NOx emissions modelling. Previous experimental studies have highlighted the existence of a very strong correlation between peak cylinder pressure and cyclic NOx emissions. In this study, a cyclic peak pressure-based semi-empirical NOx prediction model is developed. The model is calibrated using high-speed NO and NO2 emissions measurements during transient engine operation and then tested under different transient operating conditions. The transient performance of the physical model is compared to that of a previously developed data-driven (artificial neural network) model, and is found to be superior, with a better dynamic response and low (<10%) errors. The results shown in this study are encouraging for the use of such models as virtual sensors for real-time emissions monitoring and as complimentary models for future physics-guided neural network development.

An Assessment of Solar Cycle 25 progress through observation of SRBs and associated Geomagnetic Storms

Geomagnetic storms are severe aspects of Space Weather. They originate due to solar transient emissions such as coronal mass ejections (CMEs), whose energetic materials propagate in the Interplanetary medium and are coupled with the magnetosphere system. CME driven Geomagnetic storms are often associated with solar radio bursts (SRBs), particularly type II and type IV bursts. In this study, we present the preliminary results of solar radio observations and their associated geomagnetic activity during solar cycle 25 (SC 25) from January 2020 to June 2023, focusing on the cycle’s first four intense geomagnetic storms. The study used various radio telescopes, mainly the compound astronomical low-frequency low-cost instrument for spectroscopy and transportable observatory (CALLISTO), as well as OMNI data and the World Data Center for Geomagnetism. During the study period, it was found that 23 solar radio bursts diagnosed the geomagnetic storms with Dst <-50nT from 35 reported, including three severe storms of the SC 25. The time delay between the solar radio bursts and the arrival of CMEs and/or HSS near the Earth’s magnetosphere is estimated with an average value of 79 h within the [48–120 h] range for 23 geomagnetic storms associated with solar radio bursts. Among 35 geomagnetic storms recorded, five are recurring geomagnetic storms associated with coronal high-speed streams (HSS), while CMEs cause the rest with average speeds of 750 km/s. The current SC 25 recognizes four major storms within the scope of the study. On 21 April 2023, a type II radio burst followed by a type IV burst diagnosed the first severe geomagnetic storm on 24 April 2023. The second severe storm was unusual and detected in the absence of the precursor as a solar radio burst. The SRBs of type II burst and type IV burst extending in IP medium on 1 November 2021 tracked the third major storm of the cycle while the group of type III radio bursts, type II and type IV bursts on 24 February 2023, predicted the major storm on 27 February 2023. These major geomagnetic storms are linked to CMEs that show expanding flux ropes, which are signatures of type II and moving type IV radio bursts identified. Furthermore, the detected SRBs and related major geomagnetic storms are proof of high solar and magnetic activity of the ascending phase of SC 25. The SC 25 has been characterized overall, and its current progress is being tracked using observations of SRBs and magnetic activity during its rising phase.

Measurement of transient nanoparticle emissions of a municipal biomass incineration plant equipped with pulse-jet cleaned filters

The nanoparticle emission of a biomass incineration plant for district heating was investigated during a field measurement campaign. Pulse-jet cleaned filters were used as the main waste-gas treatment technology for the plant, downstream of a multi-cyclone which served as a pre-separator for the coarser size spectrum of fly ash particles. These filters feature a characteristic transient emission behavior consisting of distinct emission peaks, lasting from several seconds to many minutes after filter regeneration. This transient behavior required the development of an adapted measurement setup and methodology when using a scanning mobility particle sizer (SMPS) for the determination of the size distribution. The concentration of particle size fractions (during both, SMPS scans and measurements with fixed classifier voltage) and the total concentration were measured simultaneously applying two condensation particle counters (CPC). The decay behavior of the emission peaks of certain size fractions (at fixed classifier voltage) and the total concentration are in good agreement, enabling post-processing of the SMPS scans, which are affected by the inlet concentration decay during measurement. The size distributions of the particle emission from filter bags made of needle-felt media (aged and newly installed) and of membrane filter medium with taped seams (newly installed) were obtained following the measurement methodology. The emission of the aged needle-felt and the membrane filter medium rapidly declined to zero, allowing only for a single scan for each regeneration event. The membrane filter medium shows the lowest emission at an average number concentration of approx. 13 #/cm³ with a number-based median diameter x50,0 of 70 – 80 nm. The results demonstrate the possible high number based separation efficiency (e.g. above 99.999 % for the membrane filter bag) of pulse-jet cleaned filters regarding particle sizes not properly represented by conventional gravimetrical measurements.

Experimental study of smouldering combustion and transient emissions from forest duff with dual layers

Smouldering combustion in wildfires is responsible for large amounts of carbon emissions and regional haze episodes. In the wildland, there are dual or multiple layers of forest duff with varying particle sizes, forming a non-homogeneous fuel system. However, understanding of the effects of the fuel heterogeneity on smouldering combustion is limited. In this study, we experimentally investigated the smouldering behaviour and gas emissions of forest duff consisting of dual layers with particle diameters ranging in three sizes (1 mm ≤ d1 < 2 mm, 0.425 mm ≤ d2 < 1 mm, and d3 < 0.425 mm). A comprehensive smouldering combustion rig was designed and built to simultaneously measure the mass loss, temperature profile, time-resolved gas concentration for 20 species, and visual and infrared imaging for 6 dual-layered duff samples (d1 - d1, d1 - d2, d1 - d3, d2 - d2, d2 - d3, d3 - d3) under a controlled environment. A terrace smouldering front structure was observed for dual-layered fuel beds with coarse duff particle (d1 and d2) staying in the upper layer (50% of the volume of an open-top reactor with dimensions of 20 × 20 × 10 cm), while a surface cracking phenomenon was observed for sample consisting of fine duff particles d3 - d3. Increases in the particle size at the lower-layer with a fixed d1 upper-layer particles were found to have a greater impact on the oxygen supply, leading to a higher lateral spread rate, a higher modified combustion efficiency (MCE), and a lower fire-averaged CO/CO2. In contrast, changes in the upper-layer particle size with a fixed d3 lower-layer particles have a greater impact on the heat transfer. These findings advance the scientific understanding of smouldering fires spreading in dual forest fuel system, ultimately contributing to the development of wildfire mitigating technologies.

Sub‐Diurnal Methane Variations on Mars Driven by Barometric Pumping and Planetary Boundary Layer Evolution

AbstractIn recent years, the Tunable Laser Spectrometer within the Sample Analysis at Mars (TLS‐SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub‐diurnally, which is difficult to reconcile with an as‐yet‐unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically induced transport of methane from an underground source to the surface, modulated by temperature‐dependent adsorption. The subsurface fractured‐rock seepage model is coupled to a simplified 1‐D atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub‐diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for a MSL Curiosity sampling campaign. Our analysis suggests that there is a lower limit to the subsurface fracture density that can produce the observed methane patterns, below which the atmospheric methane variations would be out of phase with the observations. The best‐performing model scenarios indicate a significant, short‐lived methane pulse just prior to sunrise, the detection of which by TLS‐SAM would be a potential indicator of the contribution of barometric pumping to Mars' atmospheric methane variations.

A Comparative Investigation of the Emissions of a Heavy-Duty Diesel Engine under World Harmonized Transient Cycle and Road Spectrum Cycle

In the present study, detailed comparative experiments on a heavy-duty diesel engine used in the world harmonized transient cycle (WHTC) and road spectrum reversely deduced cycle (RSRDC, which was derived from a road test) were carried out. Fuel consumption and gaseous and particulate pollutants, along with some engine operation parameters, were measured transiently; thus, specific emissions can be calculated. Results showed that the BSFC of WHTC and RSRDC was 201.8 and 210 g/kW·h, respectively, because the real road driving cycle (RSRDC) had wider operating point distributions and more points located in the low-efficiency zone relative to WHTC. Thus, WHTC operations exhibited higher raw CO (abundant CO formation needed a specific temperature threshold) and NOx but lower HC. Furthermore, with aftertreatment, all pollutants met the newest China regulation limit. Finally, transient emissions were analyzed in detail. Although the specific emissions of some pollutants were similar in value for both cycles, transient processes may largely be different. Therefore, the current study is meaningful, and we not only provide broad and detailed information but also directly compare two types of operations (one is a real road driving cycle) in the laboratory: this is rarely discussed in the literature.

Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure.

We demonstrate geostationary satellite monitoring of large transient methane point sources with the US Geostationary Operational Environmental Satellites (GOES). GOES provides continuous 5- to 10-min coverage of the Americas at 1 to 2 km nadir pixel resolution in two shortwave infrared spectral bands from which large methane plumes can be retrieved. We track the full evolution of an extreme methane release from the El Encino-La Laguna natural gas pipeline in Durango, Mexico on 12 May 2019. The release lasted 3 h at a variable rate of 260 to 550 metric tons of methane per hour and totaled 1,130 to 1,380 metric tons. We report several other detections of transient point sources from oil/gas infrastructure, from which we infer a detection limit of 10 to 100 t h-1. Our results show that extreme releases of methane can last less than an hour, as from deliberate venting, and would thus be difficult to identify and quantify with low-Earth orbit satellites.

Solar Radio Spectro-polarimeter (50–500 MHz). I. Design, Development, and Characterization of a Cross-polarized, Log-periodic Dipole Antenna

The Zeeman effect has been routinely used to image and quantify the solar photospheric magnetic field (B). Such a direct measuring technique is not yet available for the corona (Lin et al. 2004). Since almost all transient nonthermal radio emissions from the corona are either partially or fully circularly polarized, observing their polarization signatures over broad frequency ranges would be of help to estimate B as a function of heliocentric height. This article aims to describe the design and development of a Cross-polarized Log-Periodic Dipole Antenna (CLPDA), an integral part of a radio spectro-polarimeter, which works in the 50–500 MHz frequency-range and to explain the tests that were carried out to characterize it. The above frequency range corresponds to a heliocentric height range ≈1.03 < r < 2.5 R ⊙ (R ⊙ = photospheric radius), wherein the numerous coronal nonthermal transients associated with space-weather effects are observed to originate. The CLPDA is used to determine the strength and sense of polarization of the received radio signal. The uncertainty involved in the determination depends on the polarization-isolation (PI) between the two orthogonal components of a CLPDA. Some of the recent advancements made in the antenna design concepts at high frequencies (∼GHz) were adopted to reduce the PI at low frequencies (∼MHz). Throughout the above frequency range, the CLPDA has a gain, return loss, and PI of ≈6.6 dBi, ≲−10 dB, and ≲−27 dB, respectively. The average PI of the CLPDA varies from −30 to −24 dB over an azimuthal angle range 0° to ±45° within which the observations are performed regularly.

Impact on Hearing Among SARS COV-2 Infected Adults and Children Born to Gestational COVID-19 Infected Mothers: A Prospective Case Control Study.

SARS COV 2 infection affects primarily the respiratory system and various organs in humans is responsible for higher mortality secondary to cytokine storm during the COVID-19 pandemic. It affects the internal auditory system is responsible for Sensory neural hearing loss in adults as well as children born to COVID-19 infected mothers. This study was aimed to detect the pattern of hearing loss in COVID-19 infected adults and pattern of hearing loss in children born to gestational COVID-19 infected mothers. Fifty asymptomatic RT-PCR COVID-19 infected adults and age, sex matched healthy controls were evaluated for audiological profile using Pure Tone Audiometry (PTA). Children born to COVID-19 infected mothers were tested using Transient product otoacoustic emissions and click-evoked auditory brainstem responses (ABRs) compared with children born to non-COVID mothers. PTA auditory profile of COVID-19 infected adults on day 7 had 30% (16 out of 50) significant high frequency sensory neural hearing impairment for 4000Hz (p value 0.003), 6000Hz (p value 0.001), 8000Hz (p value 0.001). Repeat PTA testing on day 30 showed normal hearing. Whereas in children, 40% (n = 20) born to COVID-19 infected mothers had OAE as "Refer". BERA (ABRs) testing of that OAE "Refer" children revealed 30% (n = 6) hearing impairment. COVID-19 infection cause transient type of high frequency sensory neural hearing loss in adults. Whereas in children born to COVID-19 infected mothers there is risk of developing permanent, progressive or long-standing transient type of sensory neural hearing loss.

Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2)

Abstract. We implemented the Community Aerosol and Radiation Model for Atmospheres (CARMA) in both the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2). CARMA is a sectional microphysical model, which we use for aerosol in both the troposphere and stratosphere. CARMA is fully coupled to chemistry, clouds, radiation, and transport routines in CESM2. This development enables the comparison of simulations with a sectional (CARMA) and a modal (MAM4) aerosol microphysical model in the same modeling framework. The new implementation of CARMA has been adopted from previous work, with some additions that align with the current CESM2 Modal Aerosol Model (MAM4) implementation. The main updates include an interactive secondary organic aerosol description in CARMA, using the volatility basis set (VBS) approach, updated wet removal, and the use of transient emissions of aerosols and trace gases. In addition, we implemented an alternative aerosol nucleation scheme in CARMA, which is also used in MAM4. Detailed comparisons of stratospheric aerosol properties after the Mount Pinatubo eruption reveal the importance of prescribing sulfur injections in a larger region rather than in a single column to better represent the observed evolution of aerosols. Both CARMA and MAM4 in CESM2 are able to represent stratospheric and tropospheric aerosol properties reasonably well when compared to observations. Several differences in the performance of the two aerosol models show, in general, an improved representation of aerosols when using the sectional aerosol model in CESM2. These include a better representation of the aerosol size distribution after the Mount Pinatubo volcanic eruption in CARMA compared to MAM4. MAM4 produces on average smaller aerosols and less removal than CARMA, which results in a larger total mass. Both CARMA and MAM4 reproduce the stratospheric aerosol optical depth (AOD) within the error bar of the observations between 2001 and 2020, except for recent larger volcanic eruptions that are overestimated by both model configurations. The CARMA background surface area density and aerosol size distribution in the stratosphere and troposphere compare well to observations, with some underestimation of the Aitken-mode size range. MAM4 shows shortcomings in reproducing coarse-mode aerosol distributions in the stratosphere and troposphere. This work outlines additional development needs for CESM2 CARMA to improve the model compared to observations in both the troposphere and stratosphere.

Morphological evidence for nanoflares heating warm loops in the solar corona

Context. Nanoflares are impulsive energy releases that occur due to magnetic reconnection in the braided coronal magnetic field, which is a potential mechanism for heating the corona. However, there are still sporadic observations of the interchange of braiding structure segments and footpoints inside coronal loops, which is predicted to be the morphological evolution of the reconnecting magnetic bundles in the nanoflare picture. Aims. This work aims to detect the evolutions of the pairs of braiding strands within the apparent single coronal loops observed in Atmospheric Imaging Assembly (AIA) images. Methods. The loop strands were detected on two kinds of upsampled AIA 193 Å images, which were obtained by upscaling the point spread function matched AIA images via bicubic interpolation and were generated using a super-resolution convolutional neural network. The architecture of the network is designed to map the AIA images to unprecedentedly high spatial resolution coronal images taken by the High-resolution Coronal Imager (Hi-C) during its brief flight. Results. At times, pairs of separate strands that appear braided together later evolved into pairs of almost parallel strands with completely exchanged parts. These evolutions offer morphological evidence that magnetic reconnections between the braiding strands have taken place, which is further supported by the appearance of transient hot emissions containing significant high-temperature components (T &gt; 5 MK) at the footpoints of the braiding structures. Conlusions. The brief appearances of the two rearranging strands support the idea that magnetic reconnections have occurred within what appears to be a single AIA loop.

Methanol (M85) Port-Fuel-Injected Spark Ignition Motorcycle Engine Development—Part 2: Dynamic Performance, Transient Emissions, and Catalytic Converter Effectiveness

&lt;div&gt;Methanol is emerging as an alternate internal combustion engine fuel. It is getting attention in countries such as China and India as an emerging transport fuel. Using methanol in spark ignition engines is easier and more economical than in compression ignition engines via the blending approach. M85 (85% v/v methanol and 15% v/v gasoline) is one of the preferred blends with the highest methanol concentration. However, its physicochemical properties significantly differ from gasoline, leading to challenges in operating existing vehicles. This experimental study addresses the challenges such as cold-start operation and poor throttle response of M85-fueled motorcycle using a port fuel injection engine. In this study, M85-fueled motorcycle prototype is developed with superior performance, similar/better drivability, and lower emissions than a gasoline-fueled port-fuel-injected motorcycle. An open electronic control unit was installed using suitable wiring harness/sensors and actuators to control the engine. Then the motorcycle electronic control unit was calibrated for transient operations on a chassis dynamometer. The motorcycle was tested under road load simulation and wide-open throttle conditions on the chassis dynamometer to compare its performance with a baseline gasoline-fueled motorcycle. Evaluation parameters included power at wheels, maximum vehicle speed, and time-based and speed-based acceleration characteristics. Transient emissions were evaluated following the Indian driving cycle protocols. The effectiveness of the catalytic converter for M85 fueling was assessed by comparing various emissions upstream and downstream of the catalytic converter. M85-fueled motorcycle generated higher power at wheels and similar maximum speeds as baseline gasoline-fueled motorcycle. Fine-tuned M85-fueled motorcycle exhibited superior acceleration characteristics over baseline gasoline-fueled motorcycle, indicating that an appropriate tuning strategy could tackle the issue of “drivability.” M85-fueled motorcycle emitted lower carbon monoxide and hydrocarbon during the warm-up cycles in the Indian driving cycle protocol. The inherent fuel oxygen of M85 enhanced the carbon monoxide–carbon dioxide conversion, reducing carbon monoxide emissions in the engine exhaust. The existing catalytic converter was also suitable for M85 fueling since the hydrocarbon, nitric oxide, and carbon monoxide emissions were effectively reduced downstream of the catalytic converter in all test conditions.&lt;/div&gt;

Automated detection and monitoring of methane super-emitters using satellite data

Abstract. A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.

Artificial Intelligence Strategies for the Development of Robust Virtual Sensors: An Industrial Case for Transient Particle Emissions in a High-Performance Engine

&lt;div&gt;The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed. In the present work, the authors present a detailed overview of the problematics arising in the development of a virtual sensor, with particular focus on the transient particulate number (diameter &amp;lt;10 nm) emissions, overcome by leveraging data-driven algorithms and a profound knowledge of the underlying physical limitations. The workflow has been tested and validated using a complete dataset composed of more than 30 full driving cycles obtained from industrial experimentations, underlying the importance of each step and its possible variations. The final results show that a reliable model for transient particulate number emissions is possible and the accuracy reached is compatible with the intrinsic cycle to cycle variability of the phenomenon, while ensuring control over the quality of the predicted values, in order to provide valuable insight for the actions to perform.&lt;/div&gt;

A new transient NOx prediction model for diesel engine based on neural network model with Pelican Optimization Algorithm

The World Harmonized Steady Cycle (WHSC) and the World Harmonized Transient Cycle (WHTC) are used to simulate the real driving conditions of a vehicle, which provides an opportunity for researchers to predict engine emissions through the use of models and thus investigate engine emission technologies or technology optimization to meet regulations. In this study, a Back-Propagation neural network (BPNN) is used to build an engine NOx prediction model, determine the number of nodes in the hidden layer, and optimize its weights and thresholds using the Pelican Optimization Algorithm (POA). NOx emissions from diesel engines are predicted by using the WHTC. The results show that the proposed model has a high R coefficient value of 0.9641. The results of the cold WHTC cycle NOx emission prediction shows that the mean absolute error (MAE) for the three cycles is about 32–35 ppm. The MAE was approximately 2.8% of the maximum NOx emissions in all cycles. These results indicate that the model has high accuracy in predicting transient NOx emissions.

Regulation of the fuel injection advance angle in a transport diesel engine

The toxicity indicators of exhaust gases of a diesel engine largely depend on the quality of the fuel supply process. The possibility of a significant improvement in the toxicity of the exhaust gases of a diesel engine by optimizing the phase of the start of fuel supply - the fuel injection advance angle is shown. To assess the influence of the fuel injection advance angle on the dynamic performance of the engine and the toxicity of exhaust gases, a mathematical model of the engine has been developed. The model contains differential equations for the main elements of the combined engine - its piston part, turbocharger, in take and exhaust pipelines. These equations include polynomial dependences of the main engine parameters on its operating parameters. The object of research was diesel type D-245 (4 ChN 11/12.5) produced by the Minsk Motor Plant. Calculations of the acceleration of the engine with a variable fuel injection advance angle have been carried out. The influence of the law of regulation of the advance angle of fuel injection on the duration of the transient process and emissions of normalized toxic components is determined.