Fine particulate matter contributes to COPD-like pathophysiology: experimental evidence from rats exposed to diesel exhaust particles.

BACKGROUND: Ambient fine particulate matter (PM2.5) is considered a plausible contributor to the onset of chronic obstructive pulmonary disease (COPD). Mechanistic studies are needed to augment the causality of epidemiologic findings. In this study, we aimed to test the hypothesis that repeated exposure to diesel exhaust particles (DEP), a model PM2.5, causes COPD-like pathophysiologic alterations, consequently leading to the development of specific disease phenotypes. Sprague Dawley rats, representing healthy lungs, were randomly assigned to inhale filtered clean air or DEP at a steady-state concentration of 1.03 mg/m3 (mass concentration), 4 h per day, consecutively for 2, 4, and 8 weeks, respectively. Pulmonary inflammation, morphologies and function were examined. RESULTS: Black carbon (a component of DEP) loading in bronchoalveolar lavage macrophages demonstrated a dose-dependent increase in rats following DEP exposures of different durations, indicating that DEP deposited and accumulated in the peripheral lung. Total wall areas (WAt) of small airways, but not of large airways, were significantly increased following DEP exposures, compared to those following filtered air exposures. Consistently, the expression of α-smooth muscle actin (α-SMA) in peripheral lung was elevated following DEP exposures. Fibrosis areas surrounding the small airways and content of hydroxyproline in lung tissue increased significantly following 4-week and 8-week DEP exposure as compared to the filtered air controls. In addition, goblet cell hyperplasia and mucus hypersecretions were evident in small airways following 4-week and 8-week DEP exposures. Lung resistance and total lung capacity were significantly increased following DEP exposures. Serum levels of two oxidative stress biomarkers (MDA and 8-OHdG) were significantly increased. A dramatical recruitment of eosinophils (14.0-fold increase over the control) and macrophages (3.2-fold increase) to the submucosa area of small airways was observed following DEP exposures. CONCLUSIONS: DEP exposures over the courses of 2 to 8 weeks induced COPD-like pathophysiology in rats, with characteristic small airway remodeling, mucus hypersecretion, and eosinophilic inflammation. The results provide insights on the pathophysiologic mechanisms by which PM2.5 exposures cause COPD especially the eosinophilic phenotype.

Non-allergic eosinophilic inflammation and airway hyperresponsiveness induced by diesel engine exhaust through activating ILCs.

RATIONALE: Diesel engine exhaust (DEE) is associated with the development and exacerbation of asthma. Studies have shown that DEE can aggravate allergen-induced eosinophilic inflammation in lung. However, it remains not clear that whether DEE alone could initiate non-allergic eosinophilic inflammation and airway hyperresponsiveness (AHR) through innate lymphoid cells (ILCs) pathway. OBJECTIVE: This study aims to investigate the airway inflammation and hyperresponsiveness and its relationship with ILC after DEE exposure. METHOD: Non-sensitized BALB/c mice were exposed in the chamber of diesel exhaust or filtered air for 2, 4, and 6 weeks (4 h/day, 6 days/week). Anti-CD4 mAb or anti-Thy1.2 mAb was administered by intraperitoneal injection to inhibit CD4+T or ILCs respectively. AHR、airway inflammation and ILCs were assessed. RESULT: DEE exposure induced significantly elevated level of neutrophils, eosinophils, collagen content at 4, 6 weeks. Importantly, the airway AHR was only significant in the 4weeks-DEE exposure group. No difference of the functional proportions of Th2 cells was found between exposure group and control group. The proportions of IL-5+ILC2, IL-17+ILC significantly increased in 2, 4weeks-DEE exposure group. After depletion of CD4+T cells, both the proportion of IL-5+ILC2 and IL-17A ILCs was higher in the 4weeks-DEE exposure group which induced AHR, neutrophilic and eosinophilic inflammation accompanied by the IL-5, IL-17A levels. CONCLUSION: Diesel engine exhaust alone can imitate asthmatic characteristics in mice model. Lung-resident ILCs are one of the major effectors cells responsible for a mixed Th2/Th17 response and AHR.

The key metabolic signatures and biomarkers of polycyclic aromatic hydrocarbon-induced blood glucose elevation in chinese individuals exposed to diesel engine exhaust.

Due to the complexity of environmental exposure factors and the low levels of exposure in the general population, identifying the key environmental factors associated with diabetes and understanding their potential mechanisms present significant challenges. This study aimed to identify key polycyclic aromatic hydrocarbons (PAHs) contributing to increased fasting blood glucose (FBG) concentrations and to explore their potential metabolic mechanisms. We recruited a highly PAH-exposed diesel engine exhaust testing population and healthy controls. Our findings found a positive association between FBG concentrations and PAH metabolites, identifying 1-OHNa, 2-OHPh, and 9-OHPh as major contributors to the rise in FBG concentrations induced by PAH mixtures. Specifically, each 10 % increase in 1-OHNa, 2-OHPh, and 9-OHPh concentrations led to increases in FBG concentrations of 0.201 %, 0.261 %, and 0.268 %, respectively. Targeted metabolomics analysis revealed significant alterations in metabolic pathways among those exposed to high levels of PAHs, including sirtuin signaling, asparagine metabolism, and proline metabolism pathway. Toxic function analysis highlighted differential metabolites involved in various dysglycemia-related conditions, such as cardiac arrhythmia and renal damage. Mediation analysis revealed that 2-aminooctanoic acid mediated the FBG elevation induced by 2-OHPh, while 2-hydroxyphenylacetic acid and hypoxanthine acted as partial suppressors. Notably, 2-aminooctanoic acid was identified as a crucial intermediary metabolic biomarker, mediating significant portions of the associations between the multiple different structures of OH-PAHs and elevated FBG concentrations, accounting for 16.73 %, 10.84 %, 10.00 %, and 11.90 % of these effects for 1-OHPyr, 2-OHFlu, the sum concentrations of 2- and 9-OHPh, and the sum concentrations of total OH-PAHs, respectively. Overall, our study explored the potential metabolic mechanisms underlying the elevated FBG induced by PAHs and identified 2-aminooctanoic acid as a pivotal metabolic biomarker, presenting a potential target for intervention.

Emission Spectroscopy-Based Sensor System to Correlate the In-Cylinder Combustion Temperature of a Diesel Engine to NOx Emissions.

Due to a rising importance of the reduction of pollutant, produced by conventional energy technologies, the knowledge of pollutant forming processes during a combustion is of great interest. In this study the in-cylinder temperature, of a near series diesel engine, is examined with a minimal invasive emission spectroscopy sensor. The soot, nearly a black body radiator, emits light, which is spectrally detected and evaluated with a modified function of Planck's law. The results show a good correlation between the determined temperatures and the NOx concentration, measured in the exhaust gas of the engine, during a variety of engine operating points. A standard deviation between 25 K and 49 K was obtained for the in-cylinder temperature measurements.

A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis.

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve "Dead ReLU" and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.

Role of chicken fat waste and hydrogen energy ratio as the potential alternate fuel with nano-additives: Insights into resources and atmospheric remediation process.

The main focus of the study was to witness the effects of chicken waste-based biodiesel blends along with constant hydrogen injection in a modified diesel engine. Furthermore, the nanoparticle multiwall carbon nanotubes (MWCNT) effects on the engine efficiency were also examined. A series of tests was conducted in the single cylinder, water cooled engine fuelled with diesel, CB100N, CB10N, CB30N, and CB50N. Throughout the entire run, constant hydrogen injection of 5 LPM has been maintained. The parameters such as brake thermal efficiency, brake specific fuel consumption, heat release rate and the emissions of different pollutants were determined for a variety of engine speeds. ASTM standards were applied to measure the viscosity, density and calorific value. From the reported findings, it was clear that the addition of the chicken waste biodiesel could be a sustainable substitute for the existing fossil fuels. Although the emission of the pollutants was dropped significantly, there was a massive drop in the BTE values. To compensate such shortage of power, the biodiesel was dispersed with MWCNT at the concentration of 80 ppm. Compared to the regular biodiesel, MWCNT inclusion increased the BTE by 14%. Further, the consumption of the fuel was also reduced marginally. Considering the pollutants, the catalytic activity of the MWCNT reduced the emissions of CO, NOx, and HC at various engine speeds. Besides, 10% reduction in NOx had been reported at lower engine speeds and was reduced to 8% at higher speed regimes. Compiling all together, increasing the concentration of the biodiesel blends obviously reduced the performance values and however, there was a great advantage in terms of the emission magnitudes irrespective of the engine operating conditions.

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer.

The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.

Exergoeconomic Analysis of a Mechanical Compression Refrigeration Unit Run by an ORC.

To improve the efficiency of a diesel internal combustion engine (ICE), the waste heat carried out by the combustion gases can be recovered with an organic Rankine cycle (ORC) that further drives a vapor compression refrigeration cycle (VCRC). This work offers an exergoeconomic optimization methodology of the VCRC-ORC group. The exergetic analysis highlights the changes that can be made to the system structure to reduce the exergy destruction associated with internal irreversibilities. Thus, the preheating of the ORC fluid with the help of an internal heat exchanger leads to a decrease in the share of exergy destruction in the ORC boiler by 4.19% and, finally, to an increase in the global exergetic yield by 2.03% and, implicitly, in the COP of the ORC-VCRC installation. Exergoeconomic correlations are built for each individual piece of equipment. The mathematical model for calculating the monetary costs for each flow of substance and energy in the system is presented. Following the evolution of the exergoeconomic performance parameters, the optimization strategy is developed to reduce the exergy consumption in the system by choosing larger or higher-performance equipment. When reducing the temperature differences in the system heat exchangers (ORC boiler, condenser, and VCRC evaporator), the unitary cost of the refrigeration drops by 44%. The increase in the isentropic efficiency of the ORC expander and VCRC compressor further reduces the unitary cost of refrigeration by another 15%. Following the optimization procedure, the cost of the cooling unit drops by half. The cost of diesel fuel has a major influence on the unit cost of cooling. A doubling of the cost of diesel fuel leads to an 80% increase in the cost of the cold unit. The original merit of the work is to present a detailed and comprehensive model of optimization based on exergoeconomic principles that can serve as an example for any thermal system optimization.

A comparative investigation between particle oxidation catalyst (POC) and diesel particulate filter (DPF) coupling aftertreatment system on emission reduction of a non-road diesel engine.

Non-road emission regulations are becoming increasingly rigorous, which makes it necessary for non-road engines to adopt aftertreatment systems. The commonly used aftertreatments mainly include diesel oxidation catalytic (DOC), diesel particulate filter (DPF), particle oxidation catalyst (POC), selective catalytic reduction (SCR) and ammonia purification catalyst (ASC). The purpose of this study is to investigate the effects of using an integrated system (DOC + DPF/POC + SCR + ASC) on non-road diesel engine emissions under steady-state and transient operating conditions, respectively. The major works are the comparison between POC and DPF from the viewpoint of emission reduction. The results show that both POC and DPF can effectively reduce particulate matter (PM) and nitrogen oxide (NOX) emissions under steady-state conditions, and DPF has better purification effect than POC, especially for PM. The PM conversion rate of DPF is up to 87%, while that of POC is only 60% under the non-road steady-state test cycle (NRSC). Both NOX and hydrocarbon (HC) conversion rates are high, exceeding 95%. The conversions of PM, NOX, HC, and carbon monoxide (CO) of DPF in the non-road transient test cycle (NRTC) are 92.83%, 96.99%, 96.86% and 81.45%, respectively, while those of POC are 60.12%, 95.45%, 92.82% and 79.51%, respectively. Both the POC and DPF systems can meet the emission regulation limits. As a result, POC has the potential to substitute DPF in non-road engines due to its lower product and maintenance costs. We hope that the comparison study will provide useful guidance for improving the emissions performance of non-road diesel engines.

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning.

It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method's essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

Characteristics of Resistive PM Sensors for Onboard Diagnostics of Diesel Particulate Filter Failure.

In accordance with the recently reinforced exhaust regulations and onboard diagnostics regulations, it is essential to adopt diesel particulate filter systems in diesel vehicles; a sensor that directly measures particulate matter (PM) in exhaust gas is installed to precisely monitor diesel particulate filter (DPF) failure. Because the reduction of particulate matter in the diesel particulate filter system is greatly influenced by the physical wall structure of the substrate, the presence or absence of damage to the substrate wall (cracks or local melting, etc.) determines the reliability of normal DPF operation. Therefore, an onboard diagnostics sensor for particle matter is being developed with a focus on monitoring damage to the DPF wall. In this study, as a sensor for determining damage to the substrate wall, an accumulation-type sensor whose resistance changes as soot particles are deposited between two electrodes was fabricated. The sensor characteristics were investigated by changing the gap between the sensor electrodes, sensor cap shape, and electrode bias voltage to improve resistive soot sensor sensitivity and response. From the signal characteristics of various sensor configurations, a combination sensor with improved signal stability and response time is manufactured, and they were compared with the characteristics of commercially available sensors in the engine-simulated NEDC mode in terms of the degree of DPF crack. As a result of transient mode, PM monitoring cycle was improved by 1.2~1.5 times during the same vehicle driving time compared to the existing commercial sensor.

Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion.

Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the operation of the diesel engine, this paper proposes an adaptive noise reduction and multi-channel information fusion method for the health status assessment of diesel engine valve clearance. For the problem of missing fault information of single-channel sensors in condition monitoring, we built a diesel engine valve clearance preset simulation test bench and constructed a multi-sensor acquisition system to realize the acquisition of diesel engine multi-dimensional cylinder head signals. At the same time, for the problem of poor adaptability of most signal analysis methods, the improved butterfly optimization algorithm by the bacterial foraging algorithm was adopted to adaptively optimize the key parameter for variational mode decomposition, with discrete entropy as the fitness value. Then, to reduce the uncertainty of artificially selecting fault characteristics, the characteristic parameters with a higher recognition degree of diesel engine signal were selected through characteristic sensitivity analysis. To achieve an effective dimensionality reduction integration of multi-channel features, a stacked sparse autoencoder was used to achieve deep fusion of the multi-dimensional feature values. Finally, the feature samples were entered into the constructed one-dimensional convolutional neural network with a four-layer parameter space for training to realize the health status assessment of the diesel engine. In addition, we verified the effectiveness of the method by carrying out valve degradation simulation experiments on the diesel engine test bench. Experimental results show that, compared with other common evaluation methods, the method used in this paper has a better health state evaluation effect.

Effect of fuel preheating on performance, emission and combustion characteristics of a diesel engine fuelled with Vateria indica methyl ester blends at various loads.

The present study examines the preheated (95 °C) and unheated (35 °C) Vateria indica methyl ester (VIME) blends by studying the engine performance, combustion, and emission characteristics at various loads. A single-cylinder, TV1 Kirloskar direct injection diesel engine is used to carry out the tests. Biodiesel produced from Dhupa fat through the transesterification process is used as a renewable fuel in a diesel engine. In this work, diesel (B0), VIME (B100), and two binary blends (B30 and B50) are used. VIME has a higher viscosity, higher density, and lower calorific value than diesel, resulting in lesser brake thermal efficiency (BTE) and higher brake specific energy consumption (BSEC). Due to high viscosity of the biodiesel, preheating of fuel is done before injecting into cylinder. Preheating reduces the viscosity, and enhances the atomization and vaporization of fuel, resulting in improved engine performance. For a given blend of VIME biodiesel and diesel, the preheated blend has better BTE, decreased BSEC and lesser CO and HC emissions, with a slight increment in NOX emission compared to the unheated blend. The preheated B30 blend has a BTE value of 30.3% which is close to the BTE value of 30.1% of unheated diesel at 100% load condition. CO, HC, and soot emissions are decreased by 16.2%, 34.4%, and 16.5%, respectively, for preheated B100 fuel compared to unheated B100, at full load.

Performance Modulation of S-CO2 Brayton Cycle for Marine Low-Speed Diesel Engine Flue Gas Waste Heat Recovery Based on MOGA.

(1) Background: the shipping industry forced ships to adopt new energy-saving technologies to improve energy efficiency. With the timing modulation for the marine low-speed diesel engine S-CO2 Brayton cycle, the waste heat recovery system is optimized to improve fuel economy. (2) Methods: with the 6EX340EF marine low-speed diesel engine established in AVL Cruise M and verified by the bench test data, the model of the S-CO2 Recompression Brayton Cycle (SCRBC) system for the low-speed engine flue gas waste heat recovery was developed in EBSILON, and verified by SANDIA experimental data. On this basis, the effects of injection timing and valve timing parameters on the comprehensive performance of the main engine and the waste heat recovery system were investigated. By optimizing the timing modulation parameters through multi-objective genetic algorithm (MOGA) and evaluating the flue gas waste heat recovery from the perspective of thermodynamic performance and emission reduction, the research on the performance modulation method of the S-CO2 Brayton Cycle for flue gas waste heat in marine low-speed engines has been completed. (3) Results: the SCRBC with waste heat modulation will further increase the total power and efficiency, which in turn brings about a reduction in the fuel consumption rate. The efficiency of the SCRBC system with the addition of waste heat modulation increases by 2.28%, 1.04% and 2.07% at 50%, 75% and 100%, respectively. After adding the residual heat modulation, the maximum annual CO2 emission reduction of 748.51 × 103 kg·a-1 occurred at 50% load; with the exergy analysis, the cooler has the largest system exergy loss of 165 kW, with the exergy loss efficiency of 2.06% under 100% load. (4) Conclusions: the research on the performance modulation method of S-CO2 Brayton cycle for flue gas waste heat in the marine low-speed engine has been completed, which further improves the efficiency of the system and can be extended to other engines.

Dependable Sensor Fault Reconstruction in Air-Path System of Heavy-Duty Diesel Engines.

This paper addresses the problem of robust sensor faults detection and isolation in the air-path system of heavy-duty diesel engines, which has not been completely considered in the literature. Calibration or the total failure of a sensor can cause sensor faults. In the worst-case scenario, the engines can be totally damaged by the sensor faults. For this purpose, a second-order sliding mode observer is proposed to reconstruct the sensor faults in the presence of unknown external disturbances. To this aim, the concept of the equivalent output error injection method and the linear matrix inequality (LMI) tool are utilized to minimize the effects of uncertainties and disturbances on the reconstructed fault signals. The simulation results verify the performance and robustness of the proposed method. By reconstructing the sensor faults, the whole system can be prevented from failing before the corrupted sensor measurements are used by the controller.

Effects of cordierite particulate filters on diesel engine exhaust emissions in terms of pollution prevention approaches for better environmental management.

In this study, a specific diesel fuel is experimentally tested in a 4-cylindered diesel engine with and without a cordierite-based diesel particulate filter (CPF) to show the prevention of emissions by using an after treatment system (ATS). In this context, engine exhaust emissions, total particle concentration (TPC) and soot concentration are investigated. The diesel engine is firstly evaluated with the data directly measured from the engine output (DEO) (without after treatment option), and then the changes in the exhaust emission are examined by using an ATS which is a cordierite-based diesel particulate filter to prevent pollution. In this regard, total particle concentration of DEO option is found to be 6134041.20 1/cm3 and total particle concentration by using CPF is obtained to be 707.84 1/cm3. 99.99% reduction in TPC is achieved thanks to the use of CPF. The soot concentration of DEO option is calculated to be 2.158 mg/m3. However, the soot concentration is found to be 0.014 mg/m3 by using the CPF. The particulate matters are burned at high temperatures after being filtered at the exhaust output thanks to the regeneration process within the CPF after treatment. CO emissions decreased from 0.7489 g/kWh to 0.7273 g/kWh with the CPF utilization, while HC emissions decreased from 0.0965 g/kWh to 0.0900 g/kWh via CPF. However, an increase in CO2 and NOx emissions are observed due to oxidation in the CPF.

Performance Analysis and Optimization of a Series Heat Exchangers Organic Rankine Cycle Utilizing Multi-Heat Sources from a Marine Diesel Engine.

Organic Rankine Cycle (ORC) is an effective way to recycle waste heat sources of a marine diesel engine. The aim of the present paper is to analyze and optimize the thermoeconomic performance of a Series Heat Exchangers ORC (SHEORC) for recovering energy from jacket water, scavenge air, and exhaust gas. The three sources are combined into three groups of jacket water (JW)→exhaust gas (EG), scavenge air (SA)→exhaust gas, and jacket water→scavenge air→exhaust gas. The influence of fluid mass flow rate, evaporation pressure, and heat source recovery proportion on the thermal performance and economic performance of SHEORC was studied. A single-objective optimization with power output as the objective and multi-objective optimization with exergy efficiency and levelized cost of energy (LCOE) as the objectives are carried out. The analysis results show that in jacket water→exhaust gas and jacket water→scavenge air→exhaust gas source combination, there is an optimal heat recovery proportion through which the SHEORC could obtain the best performance. The optimization results showed that R245ca has the best performance in thermoeconomic performance in all three source combinations. With scavenge air→exhaust, the power output, exergy efficiency, and LCOE are 354.19 kW, 59.02%, and 0.1150 $/kWh, respectively. Integrating the jacket water into the SA→EG group would not increase the power output, but would decrease the LCOE.

Global and regional burden of cancer in 2016 arising from occupational exposure to selected carcinogens: a systematic analysis for the Global Burden of Disease Study 2016.

OBJECTIVES: This study provides a detailed analysis of the global and regional burden of cancer due to occupational carcinogens from the Global Burden of Disease 2016 study. METHODS: The burden of cancer due to 14 International Agency for Research on Cancer Group 1 occupational carcinogens was estimated using the population attributable fraction, based on past population exposure prevalence and relative risks from the literature. The results were used to calculate attributable deaths and disability-adjusted life years (DALYs). RESULTS: There were an estimated 349 000 (95% Uncertainty Interval 269 000 to 427 000) deaths and 7.2 (5.8 to 8.6) million DALYs in 2016 due to exposure to the included occupational carcinogens-3.9% (3.2% to 4.6%) of all cancer deaths and 3.4% (2.7% to 4.0%) of all cancer DALYs; 79% of deaths were of males and 88% were of people aged 55 -79 years. Lung cancer accounted for 86% of the deaths, mesothelioma for 7.9% and laryngeal cancer for 2.1%. Asbestos was responsible for the largest number of deaths due to occupational carcinogens (63%); other important risk factors were secondhand smoke (14%), silica (14%) and diesel engine exhaust (5%). The highest mortality rates were in high-income regions, largely due to asbestos-related cancers, whereas in other regions cancer deaths from secondhand smoke, silica and diesel engine exhaust were more prominent. From 1990 to 2016, there was a decrease in the rate for deaths (-10%) and DALYs (-15%) due to exposure to occupational carcinogens. CONCLUSIONS: Work-related carcinogens are responsible for considerable disease burden worldwide. The results provide guidance for prevention and control initiatives.

Excess Air Ratio Management in a Diesel Engine with Exhaust Backpressure Compensation.

The paper investigates the operation of a wideband universal exhaust gas oxygen (UEGO) sensor in a diesel engine under elevated exhaust backpressure. Although UEGO sensors provide the excess air ratio feedback signal primarily in spark ignition engines, they are also used in diesel engines to facilitate low-emission combustion. The excess air signal is used as an input for the fuel mass observer, as well as to run the engine in the low-emission regime and enable smokeless acceleration. To ensure a short response time and individual cylinder control, the UEGO sensor can be installed upstream of a turbocharger; however, this means that the exhaust gas pressure affects the measured oxygen concentration. Therefore, this study determines the sensor's sensitivity to the exhaust pressure under typical conditions for lean burn low-emission diesel engines. Identification experiments are carried out on a supercharged single-cylinder diesel engine with an exhaust system mimicking the operation of the turbocharger. The apparent excess air measured with the UEGO sensor is compared to that obtained in a detailed exhaust gas analysis. The comparison of reference and apparent signals shows that the pressure compensation correlations used in gasoline engines do not provide the correct values for diesel engine conditions. Therefore, based on the data analysis, a new empirical formula is proposed, for which the suitability for lean burn diesel engines is verified.

An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis.

This study proposes an unsupervised anomaly detection method using sensor streams from the marine engine to detect the anomalous system behavior, which may be a possible sign of system failure. Previous works on marine engine anomaly detection proposed a clustering-based or statistical control chart-based approach that is unstable according to the choice of hyperparameters, or cannot fit well to the high-dimensional dataset. As a remedy to this limitation, this study adopts an ensemble-based approach to anomaly detection. The idea is to train several anomaly detectors with varying hyperparameters in parallel and then combine its result in the anomaly detection phase. Because the anomaly is detected by the combination of different detectors, it is robust to the choice of hyperparameters without loss of accuracy. To demonstrate our methodology, an actual dataset obtained from a 200,000-ton cargo vessel from a Korean shipping company that uses two-stroke diesel engine is analyzed. As a result, anomalies were successfully detected from the high-dimensional and large-scale dataset. After detecting the anomaly, clustering analysis was conducted to the anomalous observation to examine anomaly patterns. By investigating each cluster's feature distribution, several common patterns of abnormal behavior were successfully visualized. Although we analyzed the data from two-stroke diesel engine, our method can be applied to various types of marine engine.