Assessing total cost of driving competitiveness of zero-emission trucks
Ontology highlight
ABSTRACT:
SUBMITTER: Ledna C
PROVIDER: S-EPMC10951982 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The adoption of zero-emission vehicles (ZEVs) offers multiple benefits for the climate, air quality, and public health by reducing tailpipe emissions. However, the environmental justice implications of the nonexhaust emissions from future ZEV fleets for near-roadway communities remain unclear. Here, we model the on-road fine particulate matter (PM2.5) emissions across all California counties and assess the near-roadway exposure disparities at the census block group level in the Los Angeles County in 2050, when almost all passenger vehicles are projected to be ZEVs. We found that promoting zero-emission heavy-duty trucks generates more air quality benefits for disadvantaged communities than light-duty passenger vehicles. Persistent disparities in near-roadway PM2.5 levels, however, exist due to the remaining brake and tire wear emissions and increased truck traffic in disadvantaged communities. We recommend implementing fleet-specific ZEV policies to address brake and tire wear emissions and optimizing freight structures to address these persistent environmental justice issues in California.
| S-EPMC11526366 | biostudies-literature
Project description:High indoor humidity/temperature pose serious public health threat and hinder industrial productivity, thus adversely impairing the wellness and economy of the entire society. Traditional air conditioning systems for dehumidification and cooling involve significant energy consumption and have accelerated the greenhouse effect. Here, this work demonstrates an asymmetric bilayer cellulose-based fabric that enables solar-driven continuous indoor dehumidification, transpiration-driven power generation, and passive radiative cooling using the same textile without any energy input. The multimode fabric (ABMTF) consists of a cellulose moisture absorption-evaporation layer (ADF) and a cellulose acetate (CA) radiation layer. The ABMTF exhibits a high moisture absorption capacity and water evaporation rate, which quickly reduces the indoor relative humidity (RH) to a comfortable level (40-60% RH) under 1 sun illumination. The evaporation-driven continuous capillary flow generates a maximum open-circuit voltage (Voc ) of 0.82 V, and a power density (P) up to 1.13 µW cm-3 . When a CA layer with high solar reflection and mid-infrared (mid-IR) emissivity faces outward, it realizes subambient cooling of ≈12 °C with average cooling power of ≈106 W m-2 at midday under radiation of 900 W m-2 . This work brings a new perspective to develop the next-generation, high performance environmentally friendly materials for sustainable moisture/thermal management and self-powered applications.
| S-EPMC10104666 | biostudies-literature
Project description:During a period of 7 months, 54 class N3 trucks from 4 fleets of German fleet operators were equipped with high resolution GPS data loggers. A total of 1.26 million km of driving data has been recorded and constitutes one of the most comprehensive open datasets to date for high-resolution data of heavy commercial vehicles. This dataset provides metadata of recorded tracks as well as high-resolution time series data of the vehicle speed. Its applications include simulation of electrification for heavy commercial vehicles, modeling logistics processes or driving cycle construction.
| S-EPMC10293978 | biostudies-literature
Project description:The present study examines the effects of fuel [an ultralow sulfur diesel (ULSD) versus a 20% v/v soy-based biodiesel-80% v/v petroleum blend (B20)], temperature, load, vehicle, driving cycle, and active regeneration technology on gas- and particle-phase carbon emissions from light and medium heavy-duty diesel vehicles (L/MHDDV). The study is performed using chassis dynamometer facilities that support low-temperature operation (-6.7 °C versus 21.7 °C) and heavy loads up to 12 000 kg. Organic and elemental carbon (OC-EC) composition of aerosol particles is determined using a thermal-optical technique. Gas- and particle-phase semivolatile organic compound (SVOC) emissions collected using traditional filter and polyurethane foam sampling media are analyzed using advanced gas chromatograpy/mass spectrometry methods. Study-wide OC and EC emissions are 0.735 and 0.733 mg/km, on average. The emissions factors for diesel vehicles vary widely, and use of a catalyzed diesel particle filter (CDPF) device generally mutes the carbon particle emissions in the exhaust, which contains ~90% w/w gas-phase matter. Interestingly, replacing ULSD with B20 did not significantly influence SVOC emissions, for which sums range from 0.030 to 9.4 mg/km for the L/MHDDVs. However, both low temperature and vehicle cold-starts significantly increase SVOCs in the exhaust. Real-time particle measurements indicate vehicle regeneration technology did influence emissions, although regeneration effects went unresolved using bulk chemistry techniques. A multistudy comparison of the toxic particle-phase polycyclic aromatic hydrocarbons (PAHs; molecular weight (MW) ≥ 252 amu) in diesel exhaust indicates emission factors that span up to 8 orders of magnitude over the past several decades. This study observes conditions under which PAH compounds with MW ≥ 252 amu appear in diesel particles downstream of the CDPF and can even reach low-end concentrations reported earlier for much larger HDDVs with poorly controlled exhaust streams. This rare observation suggests that analysis of PAHs in particles emitted from modern L/MHDDVs may be more complex than recognized previously.
| S-EPMC7252512 | biostudies-literature
Project description:This study addresses the challenges of measuring regional competitiveness using traditional methods, due to the inherent complexity and non-linearity of its determinants'. The development of new Machine Learning (ML) models allows the creation of predictive models capable of handling this type of data, providing actionable insights. The objective of the study was to develop and test the use of non-linear Machine Learning models to measure the regional competitiveness in Peru, at the sub-national level. The research uses the ODD (Overview, Design Concepts, and Details) protocol to ensure a transparent and replicable methodology. The impact of ML on the Peruvian Regional Competitiveness Index (IRCI) is examined across 25 regions from 2016 to 2023, focusing on five key pillars: economy, government, infrastructure, businesses, and people. A suitability index (IoI) was developed to assess how well the pillar components align with ML. Data provided by CENTRUM PUCP was subjected to exploratory data analysis (EDA) to address variability among pillar scores and their effects on competitiveness. Six nonlinear machine learning models (Gradient Boosting, Random Forest, XGBoost, AdaBoost, Neural Networks, and Decision Trees) were applied, and the machine learning models with the highest predictive accuracy were Gradient Boosting and Random Forest. Performance metrics include MSE values of 1.1399 and 1.3469, RMSE values of 1.0677 and 1.1606, and R2 values of 0.9768 and 0.9729, respectively. These results demonstrate the effectiveness of machine learning in analyzing the complexity of regional competitiveness data, identifying influential variables, and reducing score distortions. The findings provide a data-driven framework for policymakers to improve regional competitiveness, which promotes academic knowledge and practical applications for sustainable development.
| S-EPMC11856397 | biostudies-literature
Project description:Competitiveness of seaports is a matter of interest not only to the economists, but also businesses, governments and international organizations. This data article provides quantitative data from the survey research on factors of competitiveness of container ports as perceived by shipping lines. The data was collected from around the world using an online questionnaire distributed through LinkedIn. The spatial dispersion of respondents corresponds approximately to the structure of global maritime container trade. The data provides full responses from 120 respondents. Each respondent assessed the importance of 20 predefined competitiveness factors on a scale of 1 (least important) to 10 (most important). For each respondent two additional characteristics are known, the location (continent) and the size of the company for which he/she works, measured by the number of employees. The data were used for a research article to determine the ranking of competitiveness factors for container ports entitled "Key factors of container port competitiveness: A global shipping lines perspective" [1]. The data can be used for another research to uncover relationships between factors of competitiveness (through e.g. factor analysis, cluster analysis), both for the whole world and for groups by continents or the size of the company.
| S-EPMC7118293 | biostudies-literature
Project description:The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.
| S-EPMC7248787 | biostudies-literature
Project description:We replicate and extend the adversarial expert based learning approach of Györfi et al to the situation of zero-cost portfolio selection implemented with a quadratic approximation derived from the mutual fund separation theorems. The algorithm is applied to daily sampled sequential Open-High-Low-Close data and sequential intraday 5-minute bar-data from the Johannesburg Stock Exchange (JSE). Statistical tests of the algorithms are considered. The algorithms are directly compared to standard NYSE test cases from prior literature. The learning algorithm is used to select parameters for experts generated by pattern matching past dynamics using a simple nearest-neighbour search algorithm. It is shown that there is a speed advantage associated with using an analytic solution of the mutual fund separation theorems. We argue that the strategies are on the boundary of profitability when considered in the context of their application to intraday quantitative trading but demonstrate that patterns in financial time-series on the JSE could be systematically exploited in collective and that they are persistent in the data investigated. We do not suggest that the strategies can be profitably implemented but argue that these types of patterns may exists for either structural of implementation cost reasons.
| S-EPMC6155486 | biostudies-literature
Project description:A certain class of photonic crystals with conical dispersion is known to behave as isotropic zero-refractive-index medium. However, the discrete building blocks in such photonic crystals are limited to construct multidirectional devices, even for high-symmetric photonic crystals. Here, we show multidirectional emission from low-symmetric photonic crystals with semi-Dirac dispersion at the zone center. We demonstrate that such low-symmetric photonic crystal can be considered as an effective anisotropic zero-refractive-index medium, as long as there is only one propagation mode near Dirac frequency. Four kinds of Dirac multidirectional emitters are achieved with the channel numbers of five, seven, eleven, and thirteen, respectively. Spatial power combination for such kind of Dirac directional emitter is also verified even when multiple sources are randomly placed in the anisotropic zero-refractive-index photonic crystal.
| S-EPMC4536485 | biostudies-other