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One of the traditional fuels for power generation in the Philippines is the petroleum diesel (PD). However, its extensive usage contributes to environmental degradation, health risks and climate change concerns. Alternative fuels such as petroleum nut biodiesel (PNB) may address the increasing consumption of PD amidst depleting fossil reserves and related issues. This study aimed to produce, characterize, and observe the behavior of PNB as a fuel in a compression ignition (CI) engine-power generation system at various loads of 0 %, 25 %, 50 %, 75 % and 100 %. Petroleum nut fruits were collected, extracted of oil then transesterified to produce PNB. The performance and emission profiles of the latter were determined. Degumming increased the PNB yield by 24.28 %. Additional refining decreased colorants and impurities. Majority of the chemical and physical properties of the PNB showed comparable values with those of PD. Various blends of PNB-PD were prepared and tested in terms of their performance and emissions. The 20 % PNB mixed with 80 % PD (B20) showed the most efficient performance after 100 % PD with at least 3.95 % decrease, whereas PNB for specific fuel consumption (SFC) showed at most 30.78 % higher than all fuels for all loads. The heat release rate (HRR) increases with increasing %PNB in the PNB-PD blend. PNB generally showed the highest CO2 and NOx emissions with at least 16.67 % and 80.52 % lower with PD respectively, but the lowest for CO emission with at least 13.42 % difference compared with PD. Finally, the study confirms that CI engine-generator can be operated with 100 % PNB and its blends without engine modification.

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For the first time, an energy-efficient and eco-friendly technology for the conversion of abundantly available kitchen waste, specifically waste cooked rice water (WCRW) to drop-in- biofuels, namely, butyl levulinate (BL), has been explored. The synthesis of BL was accomplished employing butyl alcohol (BA) and WCRW in an energy-efficient UV (5W each UVA and UVB)-near-infrared (100W) irradiation assisted spinning (120 rpm) batch reactor (UVNIRSR) in the presence of TiO2-Amberlyst 15 (TA15) photo-acidic catalyst system (PACS). The optimal 95.81% yield of BL (YBL) could be achieved at 10 wt% catalyst concentration, 60 °C reaction temperature, 80 min time, and 1:10 WCRW: BA concentration as per Taguchi statistical design. Moreover, additional combination of different PACS such as TiO2-Amberlyst 16, TiO2-Amberlyst 36, and TiO2-Amberlite IRC120 H rendered 86.72% YBL, 90.04% YBL, and 93.47% YBL, respectively, proving superior efficacy compared to individual activity of the acidic catalysts and photocatalysts. The heterogeneous reaction kinetics study for TA15 PACS suggested Langmuir-Hinshelwood model to be the best fitted model. A significant 63.33% energy could be saved by UVNIRSR as compared to conventional heated reactor at the optimized experimental condition using PACS TA15. An overall alleviation in environmental pollution with 59.259% reduction in GWP, 15.254% decline in terrestrial ecotoxicity, 18.238% diminution in marine ecotoxicity, 17.25% decrease in ozone formation affecting human health, 5.865% reduction in human non-carcinogenic toxicity, 18.65% diminution in ozone formation affecting terrestrial ecosystem, 55.17% significant decrease in terrestrial acidification, and 25.619% mitigation in fresh water ecotoxicity could be observed. Furthermore, BL-biodiesel-diesel blends (3% BL, 7% biodiesel, and 90% diesel) exhibited significant reduction (25.45% and 36%, respectively, for CO and HC) in harmful engine exhaust emissions demonstrating environmental sustainability of the overall process.

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The sustainable utilization of waste cooking oil (WCO) as an alternative to fossil fuels has gained considerable attention due to its potential for delivering substantial environmental and economic benefits. This research attempts to explore the impact of incorporating aluminum oxide nanoparticles (AONP) into WCO on the emissions, combustion characteristics, and overall performance of a single-cylinder compression ignition (CI) engine. Comparative analyses were conducted against conventional commercial diesel fuel and pure WCO, as well as varying blends of WCO with AONP at 25 ppm, 50 ppm, and 75 ppm concentrations. The experimental results demonstrate a notable enhancement in brake thermal efficiency (BTE), with a 13.2% increase observed in the WCO + 75 AONP fuel blend compared to neat WCO. Engines fueled by WCO nanoparticle blends showed significant augmentation in-cylinder pressure and heat release rates. Furthermore, these blends exhibited a substantial reduction in carbon monoxide (CO), hydrocarbons (HC), and soot emissions by 44%, 31%, and 48%, respectively, while nitrogen oxide (NO) emissions increased by 7% compared to neat WCO. Among the assessed fuel mixtures, the WCO + 75 AONP blend demonstrated higher engine performance. This study underscores the potential of aluminum oxide nanoparticle-enhanced WCO blends as viable and environmentally responsible options for sustainable energy solutions. However, challenges such as production costs and long-term fuel stability must be addressed to establish nano-fuels as financially viable alternatives.

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Nowadays, mankind is very particular about the usage of energy in the most effective manner by keeping the view of less adulterating the atmosphere, which are the key aspects of many scientists all around the world. In this particular study, the aloevera diesel has been chosen as the primary fuel, and studies have been conducted on emission pollutant characteristics by choosing an appropriate diesel engine. Furthermore, stable emulsions have been produced by using aloevera, and the same was mixed with diesel at the ratio of 5% and 10% as the compound. Moreover, span 80 and tween 80 are used as the surfactant with an HLB balance of 9.95. Similarly, the emulsions are prepared with the help of a mechanical stirrer for exact duration of 30 min. In order to carry out the experimental investigation process, a single-cylinder diesel engine was used with a data acquisition system. The entire analyses are carried out with two sets of methods such as no load and full load. The performance and combustion characteristics such as heat release, combustion pressure, thermal efficiency (B5 - 6.303%↑, B10 - 3.789%↑), and specific fuel consumption of the brake (B5 - 4.2%↑, B10 - 5%↑) were measured. Likewise, emission parameters such as CO (B5 - 0.02%↓, B10 - 0.04%↓), HC (B5 - 1 PPM↓, B10 - 5 PPM↓), NOx (B5 - 20 PPM↓, B10 - 89 PPM↓), and CO2 (B5 - 0.3%↑, B10 - 0.4%↑) are measured by using AVL Di-gas analyzer. It was noticed that increased peak cylinder pressure and greater heat release rate were on account of a longer ignition delay period. Additionally, an increase in engine performance and the corresponding reduction in exhaust emission have also been observed upon using aloevera-emulsified diesel fuel.

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To gain better understanding of how the transition to electric vehicles affects road dust (RD) composition, and potential health and environmental risks, it is crucial to analyze the chemical composition of RD and identify its sources. Sources of RD include wear of tire tread (TT), brake wear (BW) and road wear (RW). A relevant component of RD are tire and road wear particles (TRWPs). This literature review compiles data on the chemical bulk composition of RD sources, RD in Asia, Europe and North America and TRWP as a RD component. The focus is on elements such as Cd, Co, Cr, Cu, Ni, Pb, V, and Zn. Although the comparability of global RD data is limited due to differences in sampling and analytical methods, no significant differences in the composition from Asia, Europe, and North America were found for most of the investigated elements studied, except for Cd, Co, and V. Sources of RD were analyzed using elemental markers. On average TT, BW, and RW contributed 3 %, 1 %, and 96 %, respectively. The highest concentrations of TT (9 %) and BW (2 %) were observed in the particle size fraction of RD ≤ 10 µm. It is recommended that these results be verified using additional marker compounds. The chemical composition of TRWPs from different sources revealed that (i) TRWPs isolated from a tunnel dust sample are composed of 31 % TT, 6 % BW, and 62 % RW, and (ii) test material from tire test stands show a similar TT content but different chemical bulk composition likely because e.g., of missing BW. Therefore, TRWPs from test stands need to be chemically characterized prior to their use in hazard testing to validate their representativeness.

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The generation of tire wear is an inevitable outcome of the friction between the road and the tire which is necessary for the safe operation of vehicles on roadways. Tire wear particles form agglomerates with road surface material. These agglomerates are called tire and road wear particles (TRWP). Due to their persistence in the environmental compartments and their potentially harmful effects, research on preventative and end-of-pipe mitigation strategies for TRWP is essential. The major goal of this study is to summarize and assess the state of the art in science and technology of mitigation measures for TRWP as the basis for further research activities. Approximately 500 literature sources were found and analyzed in terms of the efficiency, maturity, implementation, and impact of the mitigation measures. Generally, technological and management mitigation measures to reduce the generation of TRWP are beneficial since they prevent TRWP from entering the environment. Once released into environmental compartments, their mobility and dispersion would increase, making removing the particles more challenging. Technological and management mitigation measures after the release of TRWP into the environment are mainly well established in industrialized countries. Street cleaning and wastewater technologies show good removal efficiencies for TRWP and microplastics. In any case, no individual measure can solely solve the TRWP issue, but a set of combined measures could potentially be more effective. The absence of fully-developed and standardized methods for tire abrasion testing and measuring TRWP in the environment makes it impossible to reliably compare the tire abrasion behavior of different tire types, determine thresholds, and control mitigation actions. Field tests and pilot studies are highly needed to demonstrate the effectiveness of the abatement measures under real conditions.

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Non-exhaust emissions are now recognized as a significant source of atmospheric particulate matter and the trend towards a reduction of conventionally fueled internal combustion engine vehicles on the road is increasing their contribution to air pollution due to lower exhaust emissions. These particles include brake wear particles (BWP) and tire-road contact particles (TRCP), which are composed of tire wear particles (TWP), road wear particles (RWP) and resuspended road dust (RRD). The goal of this study has therefore been to design an original experimental approach to provide insight into the chemical composition of particles emitted at the tire-road contact, focusing on the micron (PM10-1µm) and submicron (PM1-0.1µm) fractions. Through this characterization, an examination of the different TRCP generated by different materials (tire, road surface, brake system) was conducted. To achieve this, TRCP were collected at the rear of the wheel of an instrumented vehicle during road and track tests, and a SEM-EDX analysis was performed. Our experimental conditions have allowed us to demonstrate that, at the individual particle scale, TRCP are consistently associated with road dust materials and particles solely composed of tire or road materials are practically non-existent. The contribution of BWP to TRCP is marked by the emission of Fe-rich particles, including heavy metals like Ba, Mn and Cr. TWP, which result from rubber abrasion, consist of C-rich particles abundant in Si, Zn, and S. RWP, mainly composed of Al, Si, Fe, and Ca, can be either part of RRD or internally mixed with emitted TWP. The findings of this study highlight the substantial role of RRD to TRCP emissions under real driving conditions. Consequently, it underscores the importance of examining them simultaneously to achieve a more accurate estimation of on-road traffic emissions beyond the vehicle exhaust.

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Road dust pollution is still an important environmental problem in the cities of northwest China. To better understand the risk exposure and sources of unhealthy metals in road dust and foliar dust, the dust samples were collected in Xi'an city, Northwest China. The sampling period was during December 2019 and 53 metals in the dust were analyzed using Inductive Coupled Plasma Emission Spectrometer (ICPA-RQ). Compared to road dust, most metals are found in relatively higher concentrations in foliar dust, especially water-soluble metals, with Mn being 3710 times more abundant in foliar dust. However, the regional characteristics of road dust are more pronounced, i.e., the concentrations of Co and Ni are six times higher in industrial manufacturing areas than in residential areas. The results of the non-negative matrix factorization and principal component analysis source analyses demonstrate this difference, the dust in Xi'an is mainly from transportation (63 %) and natural sources (35 %). From the emission characteristics of the traffic source dust, brake wear is the main cause of traffic source, accounting for 43 %. However, the metal sources in each principal component of foliar dust show a more mixed state, which is consistent with the results of regional characterization. The health risk evaluation shows that traffic sources are the main risk source and contribute 67 % to the total risk. Among them, Pb from tire wear is the main contribution to the total non-carcinogenic risk for children, which is close to the risk threshold. In addition, Cr and Mn are also worthy of attention. The above results all emphasize the contribution of traffic emissions, especially the non-tailpipe emission component, to dust emissions and health risks. Therefore, controlling vehicle wear and tear and exhaust emissions should be the main way to improve air quality, such as traffic control and improvement of vehicle component materials.

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The maritime sector plays a key role in transportation in the world, and over 90% of world trade is carried by ocean shipping. However, ships are large contributors to global emissions. Hence, a vast majority of research publications have focused on different emission monitoring techniques, which are essential to establishing required policies and regulations that reduce maritime transport emissions. Various documents have been published on monitoring maritime transport emissions affecting air quality since 1977. This paper presents a bibliometric analysis to explore evolution trends, gaps, challenges, and productive countries, as well as the most cited publications with high scholarly impacts. The annual growth of 9.64% in publications demonstrates an increasing interest in reducing maritime vessel emissions. Journal articles constitute 69% of publications, followed by conference papers (25%). China and the USA play a leading role in this field of research. Regarding active resources, the "Atmospheric Environment" journal accounts for the highest relevant publications, H-index, and total citations. Eventually, the temporal evolution of keywords shows the increasing trend towards sustainable maritime transport.

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Brake wear is an important but unregulated vehicle-related source of atmospheric particulate matter (PM). The single-particle spectral fingerprints of brake wear particles (BWPs) provide essential information for understanding their formation mechanism and atmospheric contributions. Herein, we obtained the single-particle mass spectra of BWPs by combining a brake dynamometer with an online single particle aerosol mass spectrometer and quantified real-world BWP emissions through a tunnel observation in Tianjin, China. The pure BWPs mainly include three distinct types of particles, namely, Ba-containing particles, mineral particles, and carbon-containing particles, accounting for 44.2%, 43.4%, and 10.3% of the total BWP number concentration, respectively. The diversified mass spectra indicate complex BWP formation pathways, such as mechanical, phase transition, and chemical processes. Notably, the mass spectra of Ba-containing particles are unique, which allows them to serve as an excellent indicator for estimating ambient BWP concentrations. By evaluating this indicator, we find that approximately 4.0% of the PM in the tunnel could be attributable to brake wear; the real-world fleet-average emission factor of 0.28 mg km-1 veh-1 is consistent with the estimation obtained using the receptor model. The results presented herein can be used to inform assessments of the environmental and health impacts of BWPs to formulate effective emissions control policies.

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This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses 9 static testing points (defined by constant engine load and constant engine speed), volume of engine combustion chamber, EURO emission standard category, engine condition state coefficient and actual mileage is applied. An example for dysfunction detection using exhaust emission analyses is described in detail. The test setup is also described, along with the procedure for data collection using a Mindsphere cloud data processing platform. Mindsphere is a core of the new Platform as a Service (Paas) for data processing from multiple testing facilities. An evaluation on a fleet level which used quantile regression method is implemented. In this phase of the research, real data was used, as well as data defined on the basis of knowledge of the manifestation of internal combustion engine defects. As a result of the application of the platform and the evaluation method, it is possible to classify combustion engine dysfunctions. These are defects that cannot be detected by self-diagnostic procedures for cars up to the EURO 6 level.

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The increasing number of vehicles is one main cause of atmospheric environment pollution problems. Timely and accurate long- and short-term (LST) prediction of the on-road vehicle exhaust emission could contribute to atmospheric pollution prevention, public health protection, and government decision-making for environmental management. Vehicle exhaust emission has strong non-stationary and nonlinear characteristics due to the inherent randomness and imbalance nature of meteorological factors and traffic flow. Therefore accurate LST vehicle exhaust emission prediction encounters many challenges, such as the LST temporal dependencies and complicated nonlinear correlation on various emission gases, including carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO), and external influence factors. To resolve these challenging issues, we propose a novel hybrid deep learning framework, namely Dual Attention-based Fusion Network (DAFNet), to effectively predict LST multivariate vehicle exhaust emission with the temporal convolutional network, convolutional neural network, long short term memory (LSTM)-skip based on recurrent neural network, dual attention mechanism, and autoregressive decomposition model. The proposed DAFNet consists of three major parts: 1) a nonlinear component to effectively capture the dynamic LST temporal dependency of multivariate gas by the temporal convolutional network, convolutional neural network, and LSTM-skip. Moreover, the above two networks employ an attention mechanism to model the internal relevance of the LST temporal patterns and multivariate gas, respectively. 2) a linear component to tackle the scale-insensitive problem of the neural network model by an autoregressive decomposition model. 3) the external components are taken to compensate the impact of external factors on vehicle exhaust emission by the multilayer perceptron model. Finally, the proposed DAFNet is evaluated on two real-world vehicle emission datasets in Zibo and Hefei, China. Experimental results demonstrate that the proposed DAFNet is a powerful tool to provide highly accurate prediction for LST multivariate vehicle exhaust emission in the field of vehicle environmental management.

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Traffic contributes to fine particulate matter (PM2.5) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM2.5 emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM2.5 over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM2.5, increasing at 6.1% year-1, more than twice that of EC (2.9% year-1). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

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The exposure to diesel exhaust emissions (DEE) contributes to negative health outcomes and premature mortality. At the same time, the health effects of the exposure to biodiesel exhaust emission are still in scientific debate. The aim of presented study was to investigate in an animal study the effects of exposure to DEE from two types of biodiesel fuels, 1st generation B7 biodiesel containing 7% of fatty acid methyl esters (FAME) or 2nd generation biodiesel (SHB20) containing 7% of FAME and 13% of hydrotreated vegetable oil (HVO), on the oxidative stress in testes and possible protective effects of dietary intervention with blackcurrant pomace (BC). Adult Fisher344/DuCrl rats were exposed by inhalation (6 h/day, 5 days/week for 4 weeks) to 2% of DEE from B7 or SHB20 fuel mixed with air. The animals from B7 (n = 14) and SHB20 (n = 14) groups subjected to filtered by a diesel particulate filter (DPF) or unfiltered DEE were maintained on standard feed. The rats from B7+BC (n = 12) or SHB20+BC (n = 12), exposed to DEE in the same way, were fed with feed supplemented containing 2% (m/m) of BC. The exposure to exhaust emissions from 1st and 2nd generation biodiesel resulted in induction of oxidative stress in the testes. Higher concentration of the oxidative stress markers thiobarbituric acid-reactive substances (TBARS), lipid hydroperoxides (LOOHs), 25-dihydroxycholesterols (25(OH)2Ch), and 7-ketocholesterol (7-KCh) level), as well as decreased level of antioxidant defense systems such as reduced glutathione (GSH), GSH/GSSG ratio, and increased level of oxidized glutathione (GSSG)) were found. Dietary intervention reduced the concentration of TBARS, 7-KCh, LOOHs, and the GSSG level, and elevated the GSH level in testes. In conclusion, DEE-induced oxidative stress in the testes was related to the biodiesel feedstock and the application of DPF. The SHB20 DEE without DPF technology exerted the most pronounced toxic effects. Dietary intervention with BC in rats exposed to DEE reduced oxidative stress in testes and improved antioxidative defense parameters, however the redox balance in the testes was not completely restored.

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Switching diesel buses (DBs) to electric buses (EBs) has been a global trend to reduce the use of fossil fuels and improve air quality. However, buses electrification may lead to additional vehicle weight, which may emit more non-exhaust particulate matter (PM) emissions. It remains debatable whether buses' electrification will successfully improve air quality as excepted. To assess the effect of the buses' electrification on the levels of PM emissions, PM emission factors (EFs) were evaluated from EBs and equivalent DBs. In addition, the total mass of PM emissions from EBs and equivalent DBs in 2021 was calculated in Xi'an using the real-world number and mileage of EBs. The non-exhaust PM EFs from EBs were larger than total exhaust and non-exhaust PM EFs from DBs, indicating that the electrification of buses would cause an increase in the level of PM emissions. The total annual mass of PM emissions from EBs was apparently higher than that from DBs. Moreover, a sensitivity analysis showed that tire wear, brake wear, and road wear PM emissions were more reliant on vehicle mileage, whereas resuspension of road dust was more dependent on vehicle weight. This finding can serve as a guideline for policymakers to design mitigation strategies for reducing extra PM emissions due to the electrification of buses by reasonably reducing vehicle weight and annual mileage.

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In this paper, environmental impact analysis is applied to the various auxiliary power units (APUs) used for commercial aircraft in air transportation sector. The exhaust emissions of different auxiliary power units used in commercial aircraft are investigated. The emission index (EI), global warming potential (GWP) rate, global warming potential index (GWPI), environmental impact (EnI) rate, environmental impact index (EnII), environmental damage cost (EDC) rate, and environmental damage cost index (EDCI) of the exhaust emissions of APUs are computed. The GTCP36-300 model APU has the lowest total emission rate (TER) with 1.333 kg/h, the GTC85-129 model APU has the maximum total environmental index (TEI) by 24.719 g/kg-fuel, the GTCP36-300 model APU has the best total global warming potential value with 2709.176 kg/h CO2_eqv, the TSCP700 model APU has the worst global warming potential index rate as 52.481 kg/kWh CO2_eqv, the best total environmental damage cost rate is calculated to be 3.717 €/h for GTC85-72 model APU, the TSCP700 model APU has the highest environmental damage cost index with 0.130 €/kWh, the maximum total environmental impact is computed to be 5656.378 mPts/h for GTCP660 model APU, and the best total environmental impact index is determined for the GTC85-72 model APU.

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Demand for energy is one of the crises that the whole world is now facing as a direct result of the rapid depletion of fossil resources. Because of the many positive effects that biodiesel may have on both the economy and the environment, a significant amount of study has been conducted on the topic in recent years. In order to improve the physiochemical qualities, a number of researchers have been conducting studies to determine whether or not biodiesel can be used effectively as a renewable fuel in diesel engines. This research report presents the findings of an experimental investigation into the use of aluminium oxide nanoparticles as an additive in alternative fuel made from palm oil biodiesel. The investigation was carried out in the context of a nanoparticle mix. The method of transesterification is used in the manufacturing of biodiesel. The properties of the tested using American Society of Testing Methods (ASTM). The results showed that there is a significant increase in the brake thermal efficiency and a reduction of the brake-specific fuel consumption from the engine using biodiesel blends. When compared to the diesel fuel in the engine, the brake thermal efficiency of the engine fuelled using POBD20 with 50 ppm Al2O3 nanoadditive and POBD20 is found to be 11.78 and 4.76% respectively, while the engine is operated at peak load. However, the BTE is improved by about 14.16, 15.69, 20.55 and 18.39% using POBD20 and POBD20 with 25, 50 and 75 ppm Al2O3 nanoadditive respectively compared to neat palm oil biodiesel. The improvement in the BTE of the engine would be completely due to the existence of higher thermal conductivity nanoparticle which enhanced the surface to volume ratio with in the fuel. This acts as a chemical catalyst during the combustion and thereby increases the burning rate of fuel inside the combustion chamber. Furthermore, the analysis revealed that the NOx formation increased with other emissions such as carbon monoxide (CO) and unburnt hydrocarbons (UBHC) which are reduced.

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Due to the reduction of fossil fuels' resources and their contribution to environmental problems, biodiesel fuels have attracted significant attention as substitutes for diesel fuels. However, since their NOx emissions are higher than that of diesel fuels in most cases and also because of their higher viscosity than diesel, fuel additives are used to enhance their properties and reduce emissions. In this study, the effect of n-hexane and n-hexadecane addition to biodiesel and diesel fuels on exhaust emissions and performance of a single-cylinder diesel engine was investigated by using grey-based Taguchi method. Fuel additive, the additive amount, and fuel type were considered as the operating parameters. Three fuel types including diesel, rapeseed oil biodiesel, and cottonseed oil biodiesel were used in this investigation, while n-hexane and n-hexadecane were considered as the two fuel additives. As well as, three levels were assigned to the additive amount which were 4, 8, and 12%. Based on the operating parameters and their levels, the plan of experiments was generated according to L18 orthogonal array. Using grey relational analysis, this multi-response optimization problem was first transformed into a single response optimization. Then, this single system response, which is known as grey relational grade, was utilized in Taguchi approach for statistical evaluations. The results demonstrated that rapeseed was the best selection for fuel type compared to cottonseed and diesel in order to have the optimum system responses and hexadecane gave better results for system optimization in comparison with hexane additive. As well as, the analysis of variance showed that fuel type was the predominant operating factor influencing the grey relational grade which means fuel type was the most important parameter in the simultaneous optimization of exhaust emissions and engine performance. The Taguchi results also revealed that the optimum condition of engine performance and exhaust emissions happened when engine was fueled with rapeseed biodiesel containing 12% hexadecane as an additive. The confirmation test result validated the reliability of Taguchi approach in this investigation.

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This paper describes a multi-phase investigation into the direct effect of diesel exhaust emission on seed germination traits and biochemical changes responsible for observed effects in seeds belongs to the Brassica family. Diesel exhaust emissions were collected in germination boxes and seeds were exposed to diesel exhaust pollutants for durations of 30 to 120 min with 30 min intervals. Observed effects include seed germination inhibition, changes in seeds' antioxidants activity, and protein content. The lowest seed germination of canola (71 %) and arugula (84 %) was observed when seeds were exposed to 120 min of diesel exhaust pollution. Seed exposure to diesel exhaust emission for 60 min, caused a 23 % and 8 % decline of germination index of canola and arugula, respectively. The maximum seed soluble protein for canola (3.72 mg/g FW) was observed in seeds exposed to 120 min diesel exhaust pollution declined to 1.65 mg/g FW, and 0.60 mg/g FW after 60 and 30 min exposure to diesel exhaust, respectively. The maximum protein content of arugula seeds (0.95 mg/g FW) was observed in the control treatment and it was reduced to 0.72 mg/g FW and 0.53 mg/g FW after 60 and 90 min exposure to diesel exhaust pollution. Catalase activity was significantly reduced as canola seed exposure to diesel exhausted was increased while there were no statistically significant changes for catalase activity of arugula seeds. All evidence suggested that time of exposure was the key phytotoxic component of exhaust emissions, and highlights the potential for detrimental effects of vehicle emissions on agro-ecosystems.

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This paper evaluates the effect of the electrification of the small, medium, and large internal combustion engine (ICE) passenger cars on the levels of total particulate matter (PM). The total mean PM10 and PM2.5 emission factors (EFs) on urban, rural, and motorway roads are in the range of 26.13 - 39.57 mg km-1 veh-1 and 13.39 - 18.44 mg km-1 veh-1, respectively, from small to large ICE passenger cars. Correspondingly, the total mean PM10 and PM2.5 non-exhaust EFs on urban, rural, and motorway roads range from 27.76 to 43.43 mg km-1 veh-1 and 13.17 -19.24 mg km-1 veh-1 from equivalent small to large electric vehicles (EVs) without regenerative braking. These results show that the total non-exhaust PM from the equivalent EVs may exceed all PM from ICE passenger cars, including exhaust particle emissions, which are dependent mainly on the extent of regenerative braking, followed by passenger car type and road type. PM10 EFs for equivalent EVs without regenerative braking on urban, rural, and motorway roads are all higher than those from ICE cars. As for PM2.5, most of the equivalent EVs require different extents of regenerative braking to reduce brake emissions to be in line with all particle emissions from relative ICE cars.

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