Heavy-duty Diesel Trucks Research Articles

Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network.

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO2 concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO2 concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO2 suggested a gradual oxidation of NO into NO2, with local emissions contributing to 32-70% of the total NO2 concentration. These findings provide valuable insights into the NO and NO2 emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.

Characterizing hydrogen-diesel dual-fuel performance and emissions in a commercial heavy-duty diesel truck

This study investigates hydrogen (H2) as a supplementary fuel in heavy-duty diesel engines using pre-manifold injection. A H2-diesel dual-fuel (H2DF) system was implemented on a commercial class-8 heavy-duty diesel truck without modifying the original diesel injection system and engine control unit (ECU). Tests were conducted on a chassis dynamometer at engine speeds between 1000 and 1400 rpm with driver-demanded torques from 10 to 75%. The hydrogen energy fraction (HEF) was strategically controlled in the range between 10 and 30%. Overall, CO2 reduction (comparable to the HEF level) was achieved with similar brake-specific energy consumption (BSEC) at all loads and speeds. To maintain the same shaft torque, the driver-demanded torque was reduced in H2DF operation, which resulted in a lower boost pressure. At higher loads, engine-out BSNOx slightly decreased, while BSCO and black carbon (BC) increased significantly due to lower oxygen concentration resulting from the lower boost pressure. At lower loads, engine-out BSCO and BSBC decreased moderately, while NO2/NO ratio increased substantially in H2DF operation. Deliberate air path and diesel injection control are expected to enable higher HEF and GHG reductions.

Real-World Emission Characteristics of Diesel Pallet Trucks under Varying Loads: Using the Example of China

Diesel pallet trucks, a type of heavy-duty diesel trucks (HDDTs), have historically been a vital component in logistics and transport due to their high payload capacity. However, they also present significant challenges, particularly in terms of emissions which contribute substantially to urban air pollution. Traditional HDDTs emission measurement methods, such as engine bench tests and those used in laboratory settings, often fail to capture real-world emission behaviors accurately. This study specifically examines the real-world emission characteristics of diesel pallet trucks exceeding 30 t under varying loads (unloaded, half loaded, and fully loaded) and different road conditions (urban, suburban, and high-speed). Considering that data quality is the key to the accuracy of the scheme, this research utilized a portable emission measurement system (PEMS) to capture real-time emissions data of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOX), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO2, CO, and NOX increasing by 39.5%, 105.4%, and 22.7%, respectively, from unloaded to fully loaded states under comprehensive operating conditions. Regression analyses further provide an emission factor prediction model for HDDPTs, underscoring the continuous relationship between speed, load, and emission rates. These findings provide a scientific basis for pollution control strategies for diesel trucks.

E-Heater Performance for Aftertreatment Warm-Up in a 48V Mild-Hybrid Heavy-Duty Truck over Real Driving Cycles

High-efficiency and low-emissions heavy-duty (HD) internal combustion engines (ICEs) offer significant GHG reduction potential. Mild hybridization via regenerative braking and enabling the use of an electric heater component (EHC) for the aftertreatment system (ATS) warm-up extends these benefits, which can mitigate tailpipe GHG and NOx emissions simultaneously. Understanding such integrated hybrid powertrains is essential for the system optimization of real-world driving conditions. In the present work, the potential of a low engine-out NOx (1.5–2.5 g/kWh range) ‘Low-NOx’ HD diesel engine and EHCs were analyzed in a 48V P1 mild-hybrid system for a class 8 commercial vehicle concept and compared with those in an EPA-2010-certified HD diesel truck as a baseline under real-world driving cycles, including those from the US, Europe, India, China, as well as the world harmonized vehicle cycle (WHVC). For analysis, an integrated 1-D vehicle model was utilized that consisted of models of the ‘Low-NOx’ HD engine, the stock ATS, and a production EHC. For the real driving cycles, ‘GT-RealDrive’-based vehicle speed profiles were generated for busy trucking routes for different markets. For each cycle, the effects of the Low-NOx and EHC performances were quantified in terms of the ATS warm-up time, engine-out NOx emissions, and net fuel consumption. Depending on the driving route, the regenerative braking fully or partly neutralized the EHC power penalty without a significant impact on the ATS thermal performance. For a two-EHC system, the fueling penalty associated with every second reduction in the warm-up time FCEHC (g/s) was several-fold higher for the real driving routes compared with the WHVC. Overall, while a multi-EHC setup accelerated the ATS warm-up, a single EHC integrated at the SCR inlet showed minimized EHC heating power, leading to a minimized fueling penalty. Finally, for the India and China routes, being highly transient, the P1 hybridization proved inadequate for GHG reduction due to the limited energy recuperation. A stronger hybridization was desirable for such driving cycles.

Assessment major NOx sources to nitrate of TSP around the Danjiangkou reservoir using isotopes and a Bayesian isotope mixing model

Nitrate (NO3−) is a key component of atmospheric particulate matter. However, studies quantifying NO3− formation and sources in water source areas remain scarce. This study employs NO3− concentration, dual isotopic analyses (δ15N, δ18O), and SIAR and PSCF models to elucidate NO3− formation mechanisms and apportion sources in the Danjiangkou Reservoir area. The result showed that the concentration of NO3− (0.52 μg m−3-2.39 μg m−3), δ15N–NO3- (−2.3‰-4.2‰) and δ18O–NO3- (64.2‰–96.6‰) values varied seasonally in total suspended particulate matter (TSP). The contribution of the NO2 + ·OH pathway was the highest in summer (84%), while the main contributions came from the N2O5 + H2O and NO3 + VOCs pathways in winter (75%). The SIAR model revealed that the contributions of coal combustion and traffic emissions were 34% and 23%, respectively, indicating that fossil fuel release contributed most. Moreover, the contribution of biomass combustion sources was 31%, and soil emission sources had the least (12%). Considering liquefied petroleum gas, gasoline, and diesel vehicles as traffic emission sources, their contribution to NO3− in TSP increased to 50%. The PSCF analysis indicated that external input areas were predominantly from central Henan Province, northern Hubei Province, and central Shaanxi Province. This study quantitatively analyzed the sources of NO3− in atmospheric particulate matter in water sources for the first time, indicating that controlling the emissions of nitrogen oxides from gasoline cars, heavy-duty diesel trucks, and ships could help alleviate haze pollution and enhance air quality in the Danjiangkou reservoir area.

On-road evaluation and regulatory recommendations for NOx and particle number emissions of China VI heavy-duty diesel trucks: A case study in Shenzhen

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Database of emission factors of volatile organic compound (VOC) species in motor vehicle exhaust in China

The synergistic strategy for fine particulate matter (PM2.5) and O3 pollution prevention and control has emerged as a pivotal approach in combating air pollution. Volatile organic compounds (VOCs) serve as crucial precursors to both O3 and secondary organic aerosols (SOAs), with motor vehicles representing one of their significant sources. In this study, a standard for establishing a database of VOC species emission factors for motor vehicles was developed, and a database containing 134 VOC species was constructed through field tests and literature surveys. The VOC emissions of light-duty gasoline passenger vehicles (LDGPVs) comprised primarily alkanes and aromatics. The VOC emissions of light-duty diesel trucks (LDDTs) comprised mostly alkanes. Regarding low-speed trucks, 3-wheel vehicles, medium-duty diesel trucks (MDDTs) and heavy-duty diesel trucks (HDDTs), their VOC emissions comprised mainly oxygenated volatile organic compounds (OVOCs). The update of emission standards resulted in a reduction in VOC species emission factors while altering the composition of VOCs. Attention should be directed toward isopentane, benzene and dichloromethane emitted by LDGPVs, dodecane, undecane, ethene and propene emitted by LDDTs, and acetaldehyde emitted by HDDTs. VOC species originating from LDGPVs were more dispersed than those originating from LDDTs and HDDTs. In addition, variations in VOC species were observed among motor vehicles with different fuel types. Toluene, ethene, benzene, m,p-xylene, isopentane, hexanal, ethyne and 1,2,4-trimethylbenzene were the predominant VOC species emitted by gasoline vehicles. Diesel vehicles emitted mainly dodecane, formaldehyde, propene, undecane, acetaldehyde, ethene, decane and benzene. The results could enhance our comprehension of the emission characteristics of VOC species originating from motor vehicles and provide data support and a scientific foundation for achieving synergistic PM2.5 and O3 pollution prevention and control.

Health impacts of spatiotemporal variation in PM2.5 concentrations from heavy-duty diesel trucks in Beijing

PM2.5 emissions from heavy-duty diesel vehicles (HDDTs) pose a serious threat to human health. Existing researches difficult to capture the spatiotemporal evolution patterns of pollutant concentrations, resulting in the health impacts of pollutant emissions under different spatiotemporal patterns remain unclear. To fill this gap, this study explores the spatiotemporal evolution patterns of PM2.5 concentrations emitted by HDDTs from a spatiotemporal integration perspective, utilizing a three-dimensional spatiotemporal cube model. On this basis, the disparities in health impacts and economic losses under various spatiotemporal patterns are quantified. The results show that: 1) Differences in spatiotemporal patterns of PM2.5 emissions from HDDTs have obvious public health impacts. 55% of the areas exhibit the Oscillating Hot Spot pattern, with health impacts and economic losses accounting for over 90% of the total. High-High Cluster pattern accounts for 26% of all spatiotemporal cluster patterns, but their health and economic consequences attribute over 80% of the total. 2) Short-term exposure to PM2.5 emissions from HDDTs have resulted in substantial human health impacts and economic losses that cannot be ignored. The results of this study can assist decision-makers in identifying spatiotemporal patterns with high health impacts and economic losses of HDDT emissions, and providing decision support for the formulation of health-oriented environmentally sustainable policies.

Towards sustainable transport: quantifying and mitigating pollutant emissions from heavy-duty diesel trucks in Northeast China.

Heavy-duty diesel trucks (HDDTs) have caused serious environmental pollution in China. Accurate estimation of their pollutant emission characteristics is essential to reduce emissions and associated environmental and public health impacts. To achieve sustainable development for transport emissions in Northeast China, we developed localized emission factors and a high-resolution emission inventory of HDDTs, based on on-board test, Guidebook and international vehicle emission (IVE) model. The results show that the total emissions of CO, NO, NO2, and PM from HDDTs in Northeast China in 2020 were 172.2 kt, 531.5 kt, 11.2 kt, and 921.4 t, respectively. In terms of spatial distribution, emissions decreased from the city center to the city fringe. Temporally, the NOx emission variation curves of different types of roads presented a "single-peak" emission characteristic, which was different from the peak of traffic flow. Three emission reduction scenarios are further developed in the paper. Scenario analysis shows that elimination of HDDTs that follow the old China III emission standard and installing tailpipe treatment devices are the most effective pollutant reduction measure. The reduction percentages for CO, NO, NO2, and PM ranged from 62.9 to 83.89%. The results of our study could inform policymakers to devise feasible strategies to reduce vehicle pollution in Northeast China.

Speciated VOCs emissions and ozone formation potential from on-road vehicles in North China Plain based on detailed and localized source profiles

One of China's most heavily ozone (O3) contaminated regions is the North China Plain (NCP). On-road vehicles are major contributors to volatile organic compound (VOC) emissions. We established localized speciated VOC emission inventories for 153 sub-sources using detailed and comprehensive source profiles based on local measurements. The survival ratio of the cars was used to perform a more accurate investigation of vehicle populations. The O3 formation potential (OFP) was calculated based on the speciated VOCs emissions, and key reactive species and sources were identified. Total VOC emissions and OFPs from the on-road vehicles in the NCP were 319.49 kt and 1480 kt, respectively. The emissions of alkanes, alkenes, alkynes, aromatics, halohydrocarbons, and oxygenated VOCs (OVOCs) were 130.53, 50.60, 14.21, 63.07, 3.42, and 57.67 kt, respectively. Formaldehyde, isopentane, and acetylene dominated emissions. Alkenes contributed the most to the OFPs, accounting for 34.47%, followed by aromatics and OVOCs. Formaldehyde, ethylene, and propene were the most common contributors to the OFPs. The emissions and OFPs varied by city, vehicle type, fuel type, and emission standards. Beijing and Xuzhou had the highest emissions, primarily because of their high vehicle populations, while Beijing and Tianjin had the highest OFPs. In addition, high emissions and OFP were detected in Jiangsu, Anhui, Shandong, and Henan (JASH) border areas, as well as in cities with developed industries such as Zhengzhou, Baoding, and Shijiazhuang. For both VOCs emissions and OFP, passenger car was the dominant source among the different vehicle types; petrol vehicle was the dominant source among various fuel types; vehicle under China 3 standard was the dominant source among different standards. The primary contributors to VOC emissions were 4-stroke petrol motorcycle (4-stroke < 250 cm3) under China 0 while the major sources to O3 pollution were heavy-duty diesel trucks with rigidities of 40–50 t under China 3 standard.

On-road NOx and NH3 emissions measurements from in-use heavy-duty diesel and natural gas trucks in the South Coast air Basin of California

This study characterized nitrogen oxides (NOx) and ammonia (NH3) emissions from five in-use goods movement vehicles, including one diesel vehicle without selective catalytic reduction (SCR), two diesel vehicles with SCR, and two ultra-low NOx natural gas vehicles equipped with three-way catalysts (TWCs). Emissions testing was performed under real-world driving conditions in typical goods movement routes in the South Coast Air Basin (SCAB) of California. NOx emissions varied depending on the vehicle and the route. The no-SCR diesel vehicle showed the highest NOx emissions over all the routes. One of the SCR-equipped vehicles showed NOx emission rates that were two times higher than the other SCR-equipped vehicle with similar engine model year and mileage, which may be attributed to catalyst deterioration. The SCR-equipped diesel vehicles were within two to three times 0.2 g/bhp-hr certification standard over the different routes. The natural gas vehicles showed average NOx emissions around or below the optional Low NOx emissions standard of 0.02 g/bhp-hr. The three-bin moving average window (MAW) method was utilized to show the NOx emissions across various modes of operation, including idle, low load, and medium/high load. The highest fraction of vehicle operation was found in either the low load or medium/high load bins, depending on the route. NOx emissions for each of the bins varied between different vehicles, and are further analyzed in this paper. NH3 emissions formation was favored for the TWC-equipped natural gas vehicles, which produced about 20 times more NH3 emissions than the SCR-equipped diesel vehicles.

Analysis of the Actual Usage and Emission Reduction Potential of Electric Heavy-Duty Trucks: A Case Study of a Steel Plant

In order to understand the driving characteristics of electric heavy-duty trucks in practical application scenarios and promote their usage to replace diesel trucks, this study analyzed the actual usage of electric and diesel heavy-duty trucks in a steel factory based on vehicle-monitoring data and remote online monitoring data and estimated the emission reduction potential of the application of electric trucks by using a mileage-based method and the greenhouse gas emission model. The results showed that the electric heavy-duty trucks in the steel factory mostly operated for over 14 h, with a vehicle kilometers traveled (VKT) of 50–300 km each day, which could meet most of the demands of the transportation of the steel industry. The average daily energy consumption for most trucks falls within the range of 210–230 kWh/100 km, with higher consumption in winter than in summer, which can save approximately 18–26% in operating costs compared with diesel trucks. It is estimated that the usage of these electric heavy-duty trucks can achieve an annual reduction of 115.8 tons of NOx emissions, 0.7 tons of PM emissions, and 18,000 tons of CO2 emissions. To further promote the application of electric heavy-duty trucks in China, several policy suggestions, such as introducing priority road-right policies, promoting vehicle and battery leasing markets, and exempting zero-emission vehicles during heavy pollution days, were proposed.

Emission Measurements on a Large Sample of Heavy-Duty Diesel Trucks in China by Using Mobile Plume Chasing.

Real-world heavy-duty diesel trucks (HDTs) were found to emit far more excess nitrogen oxides (NOX) and black carbon (BC) pollutants than regulation limits. It is essential to systematically evaluate on-road NOX and BC emission levels for mitigating HDT emissions. This study launched 2109 plume chasing campaigns for NOX and BC emissions of HDTs across several regions in China from 2017 to 2020. It was found that NOX emissions had limited reductions from China III to China V, while BC emissions of HDTs exhibited high reductions with stricter emission standard implementation. This paper showed that previous studies underestimated 18% of NOX emissions in China in 2019 and nearly half of the real-world NOX emissions from HDTs (determined by updating the emission trends of HDTs) exceeded the regulation limits. Furthermore, the ambient temperature was identified as a primary driver of NOX emissions for HDTs, and the low-temperature penalty has caused a 9-29% increase in NOX emissions in winter in major regions of China. These results would provide important data support for the precise control of the NOX and BC emissions from HDTs.

Green light optimized speed advisory achieves fuel savings and CO2 emission reduction by profoundly impacting driving behavior

Green Light Optimized Speed Advisory (GLOSA) is an emerging smart transportation technology that guides drivers through signalized intersections during green light phases, showing promise in mobile source pollution control. However, there is still a lack of comprehensive understanding regarding its impact on driving behavior, fuel consumption, and CO2 emissions. In this study, we used heavy-duty diesel truck (HDDT) as an example and conducted a comprehensive path study from the changes in driving behavior to fuel consumption and CO2 emission reduction after the application of GLOSA, using a portable emission measurement system for real-world driving tests at two research scales: signal-controlled road section and signalized intersections. The study was innovatively conducted in three scenarios: Without GLOSA, With GLOSA, and GW GLOSA, which considers the simultaneous application of Green Wave (GW) traffic control system. Results show that applying GLOSA, the passing rate of vehicles at signalized intersections significantly improves, the driving cycle curve tends to be smoother, the cruise time ratio increases to 67.60%, and the travel time is reduced by 5%–13%. HDDT's average speed on the road segments (Seg) decreases by 25.15%, and the aggressive acceleration and deceleration behavior near signalized intersections are significantly reduced. The trip fuel consumption and CO2 emissions of HDDT decrease by 16.63%–23.51% and 16.06%–24.12%, respectively. These fuel savings and CO2 emission reductions mainly occur in the Seg and Signalized intersection-after sections. After the simultaneous application of GLOSA and GW, the trip average speed increases by 60.90%, and traffic efficiency is greatly improved. Although the fuel consumption and CO2 emissions are slightly higher compared to GLOSA alone due to the higher driving speed, GW GLOSA still offers relatively considerable fuel and environmental benefits. This study deepens people's understanding of GLOSA and GW and provides support for the formulation of transportation efficiency improvement and environmental protection strategies.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the “business as usual” scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Co-Powering Solutions to Truck Pollution in South Stockton

Despite decades of literature and practice with community-engaged research along with advancements in the recognition of environmental injustices, the application of equity-/justice-based and collaborative approaches between government agencies and community-based organizations has been limited. The toxic legacies of environmental racism, redlining, displacement, and segregation combined with the accelerating human-caused climate crisis warrant an increased need for consultation and collaboration between frontline communities and power brokers to markedly improve quality of life and health outcomes in environmental justice neighborhoods. This paper describes the processes and progress to date from a community-led collaboration between local community-based organizations and the Enforcement Division of the California Air Resources Board to assess and address air pollution in South Stockton, particularly from heavy-duty diesel trucks. South Stockton is one of the most polluted neighborhoods in California’s San Joaquin Valley, one of the most disparate and polluted regions in the United States. Some of the most significant components integrated into this project thus far include taking an equity-, justice-, and youth-oriented approach to community development that intentionally emphasizes a historical understanding of root causes of social and environmental injustices and provides pathways to workforce development. Including these elements has been essential in building the trust necessary to transform disparate power relations between the state and environmental justice communities, and to put multiple ways of knowing into conversation with each other to co-learn and co-power solutions to air pollution in South Stockton.

Modeling the Impacts of Low Emission Zone on Route Diversion and Emissions of Heavy-Duty Trucks: A Southern California Case Study

Heavy-duty diesel trucks (HDDTs), the majority of which are used for freight movement, are major contributors of nitrogen oxides (NO x) and fine particulate matter (PM2.5) emissions. As a result, communities close to freight hubs such as ports, railyards, and distribution centers are disproportionately affected by diesel-related air pollution. This study evaluates the potential for emission-based access restriction and pricing strategy to reduce HDDT emissions in one such community in Southern California, U.S. Using a regional travel demand model with a dedicated heavy-duty truck submodel, it is assumed that a $10 emission fee is collected from HDDTs that do not meet the 2007 emission standards (i.e., model years 2009 and older) when they enter the community, which is considered to be a low emission zone (LEZ). The modeling results show that the emission fee diverts 60% of the pass-through trips made by HDDTs that do not meet the required emission standards away from the LEZ. The emission fee is found to reduce NO x, PM2.5, and carbon dioxide (CO2) emissions from HDDTs inside the LEZ by 37%, 38%, and 25%, respectively. Also, it is found that the emission fee has minimal impacts on the total emissions in the modeling area. The total NO x and CO2 emissions remain unchanged, while the total PM2.5 emission increases by 1%. These results demonstrate that emission-based access restriction and pricing can be an effective strategy for reducing truck emissions in communities heavily affected by truck traffic without resulting in adverse impacts on the regional emission inventory.

Measurement report: Diurnal variations of brown carbon during two distinct seasons in a megacity in northeast China

Abstract. Brown carbon (BrC) represents an important target for the “win-win” strategy of mitigating climate change and improving air quality. However, estimating co-benefits of BrC control remains difficult for China, partially because current measurement results are insufficient to represent the highly variable emission sources and meteorological conditions across different regions. In this study, we investigated, for the first time, the diurnal variations of BrC during two distinct seasons in a megacity in northeast China. The winter campaign conducted in January 2021 was characterized by low temperatures rarely seen in other Chinese megacities (down to about −20 ∘C). The mass absorption efficiencies of BrC at 365 nm (MAE365) were found to be ∼ 10 % higher at night. The variations of MAE365 could not be explained by the influence of residential biomass burning emissions or secondary aerosol formation but were strongly associated with the changes of a diagnostic ratio for the relative importance of coal combustion and vehicle emissions (RS/N). Given that most coal combustion activities were uninterruptible, the higher nighttime MAE365 in winter were attributed primarily to increased emissions from heavy-duty diesel trucks. The spring campaign conducted in April 2021 was characterized by frequent occurrences of agricultural fires, as supported by the intensive fire hotspots detected around Harbin and the more-than-doubled ratios of levoglucosan to organic carbon (LG/OC) compared to the winter campaign. In spring, the MAE365 depended little on RS/N but exhibited a strong positive correlation with LG/OC, suggesting open burning emissions as the dominant influencing factor for BrC's light absorption capacity. MAE365 were ∼ 70 % higher at night for the spring campaign, pointing to the prevalence of nighttime agricultural fires, which were presumably in response to local bans on open burning. It is noteworthy that the agricultural-fire emissions resulted in a distinct peak at ∼ 365 nm for the light absorption spectra of BrC, and candidates for the compounds at play were inferred to be aromatic species with nitro-functional groups. The presence of the ∼ 365 nm peak complicated the determination of absorption Ångström exponents for the agricultural-fire-impacted samples. In addition, the ∼ 365 nm peak became much less significant during the day, likely due to photobleaching of the relevant chromophores.

Quantifying the Coupling Relationship of Road Grade Impacting Heavy-Duty Diesel Truck Operation Emissions in a Mountainous City in China

To reveal the complex impact of the road grade on heavy-duty diesel truck (HDDT) emissions, the dynamic coupling relationships among the road grade, vehicle operation, and emissions were quantified based on portable emission measurement system data from 24 HDDTs in Chongqing, China. The results showed that the average emission factors of nitrogen oxides (NO x) and carbon dioxide (CO2) of China IV HDDTs were the highest among all tested HDDTs, and NO x emissions often exceeded their corresponding standard limit values. Next, the coupling relationships of road grade, speed, and emissions were studied. Graded roads caused a 4%–44% decrease in the average vehicle speed, resulting in a coupling effect on emissions. For all pollutants and vehicle emission standards, the HDDT emissions on uphill roads were much higher, while those on downhill roads did not deteriorate significantly owing to the increased acceleration operation. Overall, ignoring the road grade led to estimation errors of between −99.14% and 291.30% for CO2 and between −99.21% and 247.73% for NO x. Speed was less correlated with emissions on downhill roads (≤−3%). For uphill roads (&gt;0%), the joint effect of road grade and speed caused a significant increase in CO2 and NO x emissions up to 21.26 and 17.29 times compared to idling emissions. A Sigmoid function successfully modeled the coupling relationship (average R2 = 0.95), and grouped S-shaped mapping curves were revealed for between −8% and 7% road grade and emissions. The model can estimate the influence of road grades on emissions at different speeds, thus providing support for emission reduction control.

Just Transition on air quality governance: a case study of heavy-duty diesel truck protests in Taiwan

Just Transitions are gaining attention in environmental research, and most studies have focused on climate change; however, the insights from this work may be usefully applied to the rarely discussed area in just transition studies. This article uses traditional dimensions of environmental and social justice, such as distributive, procedural, recognition, and restorative justice, to understand why heavy-duty diesel truck drivers fought back against stricter air pollution regulations while demanding destigmatization. The protest resulted in policy failure, and Taiwan’s transition to cleaner, newer diesel trucks were halted. This study finds that the key social contextual factor in Taiwan’s transportation industry was the labor relations of license-leasing. The drivers’ protest began with a lack of procedural justice, and communication occurred only after the law was passed. There was insufficient regard for procedural justice, and although the drivers were concerned, the new rule would significantly impact their right to work and life. Furthermore, the drivers felt disrespected and even carried the stigma of creating environmental pollution. The article assumes that the results should be different if the governance mechanism can handle the key factor in a social context and make appropriate arrangements for the four dimensions of Just Transition. This argument may be relevant for other countries looking to transition from older diesel vehicles to cleaner vehicles through Just Transition.