Truck Emissions Research Articles

Influences of Crest Vertical Curve Curvature on Exhaust Emissions of Heavy-Duty Diesel Trucks at Grade Change Point Section in Highway

The vertical curve is a major factor affecting vehicle exhaust (CO2, CO, NOx, PM2.5) emissions. This article takes a heavy-duty diesel truck as a typical vehicle, combining instantaneous speed, acceleration and the vehicle-specific power VSP to divide the operating mode, and the exhaust emission of a heavy-duty diesel truck is then calculated by using MOVES. Finally, the environmental modification factors (EMFs) are used to evaluate the influence of curvature change on the exhaust emissions. The results show that CO2 and PM2.5 emissions of heavy-duty diesel trucks are increasing with the curvature of the crest vertical curve changing; with the increase and decrease of curvature K of the crest vertical curve by 50%, CO and NOx emissions showed an increasing trend. Among the four main emissions, CO2 emission amount is the largest, followed by NOx, CO and PM2.5. The emission rates of CO2 and PM2.5 increase with the increase of curvature of the crest vertical curve, and the minimum values of CO2 and PM2.5 emission rates are 46.7 g/s and 0.022 g/s, respectively. The emission rates of CO and NOx increase with the increase of curvature. The minimum point of emission rates of CO and NOx are 0.042 g/s and 0.259 g/s, respectively.

Analyzing Freight Truck Platoon Accessibility with Route Deviations

Truck platooning is emerging as a sustainable means of transporting goods and commodities to destinations. Platooning optimizes road space and contributes to reducing freight truck emissions. However, there has been limited research on freight truck platoon accessibility to the destination that is often necessary to understand the impact of routes on which platoon is deployed. This study develops an analytical formulation for platoon accessibility by considering the possibility of the detour of trucks forming a platoon. Analyses with four prominent interstates in California, CA, USA, show that accessibility with truck platooning on select lengths of the I-5 continues to increase for about an initial 165 miles, attains a maximum there, and starts to drop after. The other three freeways, I-10, I-405, and I-710, show a continuous increase in accessibility for their lengths and the number of ramps considered. The findings can have significant implications in determining the selection of freeway routes that could be regarded as facilitators for a truck platoon.

Improved Benders decomposition for stochastic yard template planning in container terminals

Recent trends in pursuing green ports have had positive effects on terminal operators constructing more eco-friendly decision schemes. A yard template in a container port not only determines the scheduling of the terminal handling equipment by container allocation, but also affects the carbon emissions from yard trucks caused by loading and unloading operations, due to the differing degree of yard road congestion. Moreover, the uncertainty regarding the number of transporting containers makes it challenging to construct a robust yard template for a container terminal. Motivated by research pointing to the need to alleviate the exhaust pollution generated by port handling equipment, this paper presents a two-stage stochastic programming model for yard template planning, with the aim of minimizing the maintenance and manpower costs of activating yard cranes for container handling and of reducing the carbon dioxide emitted from yard trucks. More specifically, the paper considers the impact of road congestion on carbon dioxide emissions from the perspective of the increase in transportation time caused by yard truck interruptions. The uncertainty regarding the number of transport containers is reflected by taking into account numerous scenarios in which different numbers of containers are loaded onto (discharged from) vessels that visit the port periodically. In addition, an improved Benders decomposition-based solution method is designed to solve the proposed model with large-scale problem instances. Several experiments are performed to validate the effectiveness of the model and the efficiency of the method. The numerical results demonstrate that yard template planning that considers road congestion can effectively reduce the carbon emissions of yard trucks in loading and unloading operations.

Developing a Traffic Model to Estimate Vehicle Emissions: An Application in Seoul, Korea

In this study, a traffic demand model was created based on a simulation network, and another model was built to calculate exhaust-gas emissions generated by vehicles based on the emission function. Subsequently, emissions for three scenarios were analyzed based on the traffic restriction policy according to the vehicle grading system implemented in Seoul. According to the results of the analysis, emission reduction under the vehicle restriction policy was the highest among passenger cars in the low-speed range, while the emissions of cargo trucks in the high-speed range were found to be high. The emissions showed a high ratio of carbon monoxide and nitrogen oxides, and high emissions were generated from liquefied petroleum gas and diesel vehicles. Furthermore, the effects of vehicle restriction policy were confirmed to reduce emissions from diesel and other vehicle types. Using the established model, we were able to confirm that the vehicle restriction policy contributed to the improvement of air quality. Furthermore, the diesel vehicle restriction policy also had an impact on reducing the emissions of vehicle types other than those using diesel.

Emission Characteristics of Hazardous Air Pollutants from Medium-Duty Diesel Trucks Based on Driving Cycles

Studies on the characteristics of hazardous air pollutants (HAPs) in the emissions of medium-duty diesel trucks are significantly insufficient compared to those on heavy-duty trucks. This study investigated the characteristics of regulated pollutants and HAPs, such as volatile organic compounds (VOCs), aldehydes, and polycyclic aromatic hydrocarbons (PAHs), and estimated non-methane hydrocarbon (NMHC) speciation in the emissions of medium-duty diesel trucks. Ten medium-duty diesel trucks conforming to Euros 5 and 6 were tested for four various driving cycles (WLTC, NEDC, CVS-75, and NIER-9) using a chassis dynamometer. In an urban area such as Seoul, CO and NMHC emissions were increased because of its longer low-speed driving time. NOx emissions were the highest in the high-speed phase owing to the influence of thermal NOx. PM emissions were almost not emitted because of the DPF installation. Alkanes dominated non-methane volatile organic compound (NMVOC) emissions, 36–63% of which resulted from the low reaction of the diesel oxidation catalyst. Formaldehyde emissions were the highest for 35–53% among aldehydes irrespective of driving cycles. By sampling the particle-phase of PAHs, we detected benzo(k)fluoranthene and benzo(a)pyrene and estimated the concentrations of the gas-phase PAHs with models to obtain the total PAH concentrations. In the particle portion, benzo(k)fluoranthene and benzo(a)pyrene were over 69% and over 91%, respectively. The toxic equivalency quantities of benzo(k)fluoranthene and benzo(a)pyrene from NIER-9 (cold) for both Euro 5 and Euro 6 vehicles were more than five times higher than those of NIER (hot) and NEDC. In the case of NMHC speciation, formaldehyde emissions were the highest for 10–45% in all the driving cycles. Formaldehyde and benzene must be controlled in the emissions of medium-duty diesel trucks to reduce their health threats. The results of this study will aid in establishing a national emission inventory system for HAPs of mobile sources in Korea.

Analysis of the dynamic air conditioning loads, fuel consumption and emissions of heavy-duty trucks with different glazing and paint optical properties

The European transportation sector employs 10 million people and accounts for 4.6% of the European Union GDP. Due to climate change, this workforce is increasingly affected by high temperatures and radiant loads, particularly during summer. They rely on air conditioning (AC) to minimize heat inside the truck cabins, increasing fuel consumption and tailpipe emissions. Because sustainable transportation is crucial for climate change mitigation, we developed a numerical investigation on the dynamic thermal exchanges of cabins of heavy-duty trucks in realistic conditions of a summer workday, to quantify the potential impact of interventions in the glazing and paint optical properties, over the truck AC loads. We observed that the changes in air temperature and solar irradiation throughout the workday imply substantial variations in the truck’s AC loads and, consequently, in its fuel consumption and tailpipe emissions. Furthermore, windshields and side windows with transmissivity of 0.33 instead of typical 0.79 and 0.84, respectively, can reduce AC loads by up to 16%. External paints with reflectivity of 0.70 instead of 0.04 can reduce the AC loads by up to 30%, whereas cumulative changes to glazing and paint can reduce the AC load by up to 40%. These interventions can lower fuel consumption and emissions by up to 0.4%. These results show that important improvements in fuel efficiency and tailpipe emissions are possible, if the research community, policy makers and industry stakeholders successfully promote the adaptation of the European transportation fleet.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 μm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3 Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Research on Low Carbon Distribution Supply Chain of Multi-product of Pure Electric Vehicles

In view of the issue of carbon emissions generated by vehicles in the distribution link of supply chain, a low carbon supply chain distribution model is established considering collaborative optimization of production scheduling and distribution paths, taking the carbon emissions of pure electric trucks at different speeds under the time-varying network as the key variable. In this model, firstly, it is considering constraints such as product type, customer demand time window, vehicle load rate, loading and unloading time and service time. Secondly, an improved ant colony algorithm, a hybrid algorithm combining simulated annealing algorithm with ant colony algorithm, is proposed to improve the ability of searching and jumping out of local optimum. Finally, through the simulation optimization and comparative analysis of a random numerical example, it is verified that it can effectively reduce carbon emissions in the process of distribution using collaborative optimization model of supply chain production and distribution with minimizing optimization of carbon emissions and total travel time under the time-varying network, and the effectiveness of the algorithm is also verified.

Near-road air quality modelling that incorporates input variability and model uncertainty

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM2.5 concentrations at twelve out of the eighteen locations.

Effects of Vehicle Load on Emissions of Heavy-Duty Diesel Trucks: A Study Based on Real-World Data.

Vehicle loads have significant impacts on the emissions of heavy-duty trucks, even in the same traffic conditions. Few studies exist covering the differences in emissions of diesel semi-trailer towing trucks (DSTTTs) with different loads, although these vehicles have a wide load range. In this context, the operating modes and emission rates of DSTTTs were analyzed under varying loads scenarios to understand the effect of vehicle loads on emission factors. First, second-by-second field speed data and emission data of DSTTTs with different loads were collected. Then, the methods for calculating the scaled tractive power (STP) and the emissions model for DSTTTs were proposed to evaluate the effect of different loading scenarios. The STP distributions, emission rate distributions, and emission factor characteristics of different loaded trucks were analyzed and compared. The results indicated that the STP distributions of DSTTTs that under the unloaded state were more narrow than those under fully loaded or overloaded conditions. The emission rates of carbon dioxide (CO2), carbon monoxide (CO) and total hydrocarbon (THC) for DSTTTs under a fully loaded state were significantly higher than those under an unloaded state. However, due to the influence of exhaust temperature, the emission rates of nitrogen oxides (NOx) among fully loaded trucks were lower than those under the unloaded state when STP bin was above 4 kW/ton. The emission factors of CO2, CO, THC, and NOx for fully loaded trucks demonstrated the largest increases at low-speed intervals (0–30 km/h), which rose by 96.2%, 47.9%, 27.8%, and 65.2%, respectively.

Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package

Ningbo is a major coastal city in the Yangtze River Delta region, China, with the largest cargo capacity in the world. We conducted a field campaign in Ningbo to measure the impact of the COVID-19 lockdown on air pollutants including NO2, O3 and CO from 21 January to 23 March 2020, using a home-made low-cost sensor package. The average concentrations of NO2, O3 and CO were observed to be 7.2, 37.5 and 648.5 ppb, respectively, during the lockdown. Compared with the previous year, the concentrations of NO2 and CO decreased by 63.1% and 6.9%, while the concentration of O3 increased by 37.9%. The significant reduction of NO2 concentration may be attributed to the reduced emissions of freighters and heavy trucks with lower port cargo throughput, which led to a decrease of NO concentration. The increase of O3 concentration was probably due to the lower titration of O3 by NO. After the lockdown, the concentrations of O3 and NO2 increased by 15.5% and 143.1%, respectively, compared with those during the lockdown. The temporal variations of the concentrations of NO2, O3 and CO measured by the sensor package were coincident with those obtained by the reference apparatus, which proves the sensor package to be suitable for air quality monitoring in field campaigns. This is the first time that a dramatic decrease in NO2 concentration in a coastal city due to a lockdown has been reported.

Heavy-duty trucks: The challenge of getting to zero

This research considers strategies that will reduce truck emissions and achieve public health and GHG reduction targets. Freight shipments in urban areas are increasing throughout the world as a result of globalization, rising incomes, and shifting patterns of production and consumption. Urban freight shipments are overwhelmingly made by trucks, which generate significant negative impacts on human health and contribute to GHG emissions. We examine the potential of zero emission heavy-duty trucks (ZEHDTs). We use simulation modeling and case studies to explore the impacts of using battery electric heavy-duty trucks (BEHDTs) and natural gas hybrid heavy-duty trucks (hybrid HDTs) in freight operations, taking into account differences in performance and refueling. We estimate impacts for 2020, 2025, and 2030. BEHDT applications are limited in the near term due to range and charging limitations, but as BEHDT performance improves and prices go down, they are viable for a larger segment of the market. Hybrid vehicles are the most cost-effective alternative for reducing air toxics, but BEHDTs reduce air toxics the most by 2025. Subsidies and charging infrastructure investment would be required to promote use of BEHDTs.

Evaluation of Influence Factors in Diesel Truck Pollution Control

Diesel truck pollution emissions are major air polluting sources. Pollution control of diesel trucks is one of the important measures to improve air management. In order to study the influence factors in diesel trucks pollution control, an evaluation system consisting of one target layer, three criterion layers and 15 indicator layers was constructed. Through the comprehensive evaluation software Super Decision, the consistency test is passed, and the weight values of each indicator are obtained. Results show that the most important factor that affects the degree of diesel truck pollution is the number of law enforcement officers, with a weight of 0.24. The sum of weights of number of law enforcement officers, popularizing rate of environmental knowledge, blacklist rate, diesel trucks inspection rate, pollution control device configuration rate, correct use rate of pollution control devices, diesel truck emission standards is up to 0.84, which indicates that the seven influence factors determine diesel truck pollution control. The conclusions provide references in formulating measures to diesel truck.

Comparing the Use of High- to Low-Cost Black Carbon and Carbon Dioxide Sensors for Characterizing On-Road Diesel Truck Emissions.

The exhaust plume capture method is a commonly used approach to measure pollutants emitted by in-use heavy-duty diesel trucks. Lower cost sensors, if used in place of traditional research-grade analyzers, could enable wider application of this method, including use as a monitoring tool to identify high-emitting trucks that may warrant inspection and maintenance. However, low-cost sensors have for the most part only been evaluated under ambient conditions as opposed to source-influenced environments with rapidly changing pollutant concentrations. This study compared black carbon (BC) emission factors determined using different BC and carbon dioxide (CO2) sensors that range in cost from $200 to $20,000. Controlled laboratory experiments show that traditional zero and span steady-state calibration checks are not robust indicators of sensor performance when sampling short duration concentration peaks. Fleet BC emission factor distributions measured at two locations at the Port of Oakland in California with 16 BC/CO2 sensor pairs were similar, but unique sensor pairs identified different high-emitting trucks. At one location, the low-cost PP Systems SBA-5 agreed on the classification of 90% of the high emitters identified by the LI-COR LI-7000 when both were paired with the Magee Scientific AE33. Conversely, lower cost BC sensors when paired with the LI-7000 misclassified more than 50% of high emitters when compared to the AE33/LI-7000. Confidence in emission factor quantification and high-emitter identification improves with larger integrated peak areas of CO2 and especially BC. This work highlights that sensor evaluation should be conducted under application-specific conditions, whether that be for ambient air monitoring or source characterization.

Real-world activity, fuel use, and emissions of heavy-duty compressed natural gas refuse trucks

Over 50% of new refuse truck sales have been compressed natural gas (CNG). Compared to diesel, CNG is less expensive on diesel gallon equivalent (dge) basis. This study quantifies the real-world fuel use and tailpipe exhaust emissions from three front- and three side-loader refuse trucks, each with a spark ignition CNG engine, three-way catalyst, and similar gross weight. Measurements were made at 1 Hz using a portable emissions measurement system (PEMS). Inter-cycle and inter-vehicle variability is quantified. Effect of vehicle weight was analyzed and comparisons were made with MOVES predicted cycle average emission rates. In total, about 220,000 s of data covering 490 miles of operation were recorded. The average fuel economy was 1.9 miles per dge. On average the trucks spent 53% of time in idle, which includes trash collection activity. The average speeds were 10 mph and 5 mph, for front- and side-loader trucks, respectively. Overall, compared to side-loader trucks, front-loader trucks had 55% better fuel economy and 60% lower emission rates. Compared to diesel trucks, CNG truck cycle average NOx and PM emission rates, at 1.2 g/mile and 0.006 g/mile respectively, were substantially lower while CO and HC rates, at 29 g/mile and 6 g/mile respectively, were considerably higher. Fuel use and CO2 emissions rates increased by 10% due to increase in truck weight during trash collection, while CO emissions rates increased by up to 30%. Compared to measured values, MOVES estimated cycle average fuel use and CO2 emissions were 25% lower, CO emissions are 70% lower, and NOx emissions were 200% higher. Results from this study can be used to improve solid waste life cycle and tailpipe emission factor models and, when combined with previous studies on diesel refuse trucks, evaluate the effect on fuel use and emissions from adoption of CNG refuse trucks.

Reducing atmospheric pollutant and greenhouse gas emissions of heavy duty trucks by substituting diesel with hydrogen in Beijing-Tianjin-Hebei-Shandong region, China

The extensive use of diesel heavy duty trucks (diesel HDTs) has led to substantial atmospheric pollutant and greenhouse gas (GHG) emissions in Beijing-Tianjin-Hebei-Shandong (BTHS) region, China. The substitution of hydrogen fuel cell heavy duty trucks (HFC-HDTs) for their diesel counterparts is effective to solve the problems above. Aiming at the BTHS region, a GPERF (GHG emission, Pollutant emission and Economic cost Reduction in Fuel cycle) assessment model was established combining with regional vehicle data to investigate the reductions of GHG emissions, pollutant emissions and economic expenditures by the substitution scheme. Data indicated that population of diesel HDTs and HFC-vehicles in BTHS region both accounted for reaching 25% of total in country. Results showed that the fuel substitution scheme could significantly reduce the GHG and pollutant emissions by vehicles in BTHS region, among which the GHG reduced by 1/3, while the NOx and PM remarkably reduced by around 50%.

Reviewing Truck Logistics: Solutions for Achieving Low Emission Road Freight Transport

Low emission logistics have become an expected and desired goal in all fields of transportation, particularly in the European Union. Heavy-duty trucks (HDTs) are significant producers of emissions and pollution in inland transports. Their role is significant, as in multimodal transport chains truck transportation is, in most cases, the only viable solution to connect hinterlands with ports. Diesel engines are the main power source of trucks and their emission efficiency is the key challenge in environmentally sound freight transportation. This review paper addresses the academic literature focusing on truck emissions. The paper relies on the preliminary hypothesis that simple single solutions are nonexistent and that there will be a collection of suggestions and solutions for improving the emission efficiency in trucks. The paper focuses on the technical properties, emission types, and fuel solutions used in freight logistics. Truck manufacturing, maintenance, and other indirect emissions like construction of road infrastructure have been excluded from this review.

Modelling carbon emissions of diesel trucks on longitudinal slope sections in China.

Carbon emissions are the primary reason that contributes to global warming. The gradient has a significant impact on the carbon dioxide (CO2) emissions produced by trucks. The aim of the current paper is to propose a carbon emission quantification model for diesel trucks on longitudinal slope sections and investigate the influence of gradient on the carbon emissions of trucks for use in the low-carbon highway design. The law of conservation of mechanical energy, the first law of thermodynamics, and the vehicle longitudinal dynamics theory were adopted for deriving the carbon emission model of the trucks on the flat, uphill, downhill and round-trip longitudinal slope segments. Three kinds of common trucks were chosen to conduct the field test. Following the test data, the model demonstrates a high accuracy. The minimum gradient which is expected to impact carbon emissions of trucks on the round-trip longitudinal slope sections was the balance gradient as revealed. The gradient of the longitudinal slope is required to be avoided to be greater in comparison with the balance gradient for the achievement of the two-way traffic low carbon operation on a highway. The results of this study are valuable to researchers interested in low carbon road design and low carbon transportation control.

A big data approach to improving the vehicle emission inventory in China

Estimating truck emissions accurately would benefit atmospheric research and public health protection. Here, we developed a full-sample enumeration approach TrackATruck to bridge low-frequency but full-size vehicles driving big data to high-resolution emission inventories. Based on 19 billion trajectories, we show how big the emission difference could be using different approaches: 99% variation coefficients on regional total (including 31% emissions from non-local trucks), and ± as large as 15 times on individual counties. Even if total amounts are set the same, the emissions on primary cargo routes were underestimated in the former by a multiple of 2–10 using aggregated approaches. Time allocation proxies are generated, indicating the importance of day-to-day estimation because the variation reached 26-fold. Low emission zone policy reduced emissions in the zone, but raised emissions in upwind areas in Beijing's case. Comprehensive measures should be considered, e.g. the demand-side optimization.

Freight truck emissions reductions with connected vehicle technology: A case study with I-710 in California

This research aims to understand the implications of Connected Vehicle Technology (CVT) on freight truck emissions – mainly, carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compound (VOC) and fine particulate matter (PM2.5). CVT is associated with communication between trucks and in this paper, it is represented using route connectivity for trucks along a defined length of a freeway. The route connectivity is based on density of freight trucks with sensors exhibiting communication range as discs of known radii. The model is then applied over a 30-mile length of the interstate-710 (I-710) freeway of the Southern California Region to evaluate reductions in emissions. It is found that there is substantial decrease in emissions with decrease in number of trucks used up to establish communication connectivity through sensor transmission along the studied I-710 stretch. In fact, a decrease in emissions is noted with initial increase in sensor transmission radius from 1000 ft to 8000 ft. However, beyond the radius of 9000 ft, emissions reductions become almost constant. These findings can help determine optimum number of connected trucks needed to cause emissions reductions on I-710 (and other freeways) experiencing considerable heavy-duty truck traffic in the United States.