Evaluating real-world CO2 and NOX emissions for public transit buses using a remote wireless on-board diagnostic (OBD) approach

Evaluating traffic emission control policies based on large-scale and real-time data: A case study in central China

The traffic control policies, including "Odd and Even" (OAE) and "One Day Per Week" (ODPW), were adopted in Zhengzhou, China. In this study, we use the bottom-up policy evaluation framework to capture the temporal-spatial characteristics of traffic conditions and vehicle emissions under various traffic restriction scenarios. Moreover, we use the street-scale simulation model to evaluate the effectiveness of improving air quality. Results showed that the improvements in traffic conditions led to the emission decrease by about 28.3 % for carbon monoxide (CO), 16.2 % for nitrogen oxide (NOx), 21.3 % for particulate matter (PM_2.5), and 23.2 % for total hydrocarbon (THC) under OAE. During ODPW, total vehicle emissions decreased by 14.1 % for CO, 10.2 % for NOx, 13.7 % for PM_2.5, and 12.4 % for THC. However, the spatial analysis indicates traffic restrictions could not significantly reduce those emissions caused by high traffic volume; besides, buses, middle-duty trucks, and heavy-duty trucks have partly offset the reduction benefit from restrictions. The air quality simulation results reveal no significant concentration decrease of CO and nitrogen dioxide (NO₂) in most areas. With the update of vehicles, stricter management of high-emission vehicles, and limited coverage for implementation of policies, the traffic control policies were not as effective as before. The limitations of the restriction policies are gradually prominent, and upgrade policies are urgently needed to continuously improve urban air quality in the future.

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data

Light-duty gasoline vehicles (LDGVs) have made up >90 % of vehicle fleets in China since 2019, moreover, with a high annual growth rate (> 10 %) since 2017. Hence, accurate estimates of air pollutant emissions of these fast-changing LDGVs are vital for air quality management, human healthcare, and ecological protection. However, this issue is poorly quantified due to insufficient reserves of timely updated LDGV emission factors, which are dependent on real-world activity levels. Here we constructed a big dataset of explicit emission profiles (e.g., emission factors and accumulated mileages) for 159,051 LDGVs based on an official I/M database by matching real-time traffic dynamics via real-world traffic monitoring (e.g., traffic volumes and speeds). Consequently, we provide robust evidence that the emission factors of these LDGVs follow a clear heavy-tailed distribution. The top 10 % emitters contributed >60 % to the total fleet emissions, while the bottom 50 % contributed <10 %. Such emission factors were effectively reduced by 75.7-86.2 % as official emission standards upgraded gradually (i.e., from China 2 to China 5) within 13 years from 2004 to 2017. Nevertheless, such achievements would be offset once traffic congestion occurred. In the real world, the typical traffic congestions (i.e., vehicle speed <5 km/h) can lead to emissions 5- 9 times higher than those on non-congested roads (i.e., vehicle speed >50 km/h). These empirical analyses enabled us to propose future traffic scenarios that could harmonize emission standards and traffic congestion. Practical approaches on vehicle emission controls under realistic conditions are proposed, which would provide new insights for future urban vehicle emission management.

Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired

It has been recognized that emission control for heavy diesel trucks should be given priority, as a massive amount of pollutants (e.g., NOx) are emitted from heavy diesel trucks. Although pollutants can be filtered to a considerable extent by after-treatment devices equipment, emissions can still exceed the designated standards when after-treatment devices function improperly. To timely identify excessive emissions, we propose a general and systematic framework, including a data quality assessment and a data repairing and excessive emission determination process, based on the data sensed from the on-board diagnostics (OBD) monitoring system. To overcome the adverse effects of poor data quality, a set of approaches have been developed for the different statuses of data quality. When all variables contain missing or abnormal values, data repairing algorithms can be employed to improve data quality. Two strategies have been developed for the situation where only the NOx data is problematic. One is to improve data quality by using the other variables before identifying excessive emissions, and the other is to directly predict whether the emissions exceed recommendations by using other variables without the data quality problem. To reduce the impact of noise and extreme values, three methods based on the moving average principle have been developed to generate an aggregated emission level for the determination of excessive emissions. In the experimental study, we employed a number of machine learning algorithms to achieve data repairing and prediction. The support vector machine (SVM) algorithm slightly outperforms the random forests (RF) and gradient boosting decision tree (GBDT) in the prediction of the excessive emission possibility in terms of prediction accuracy. The experimental results indicate that the most accurate data repairing can be achieved by probabilistic principal component analysis (PPCA), as compared to non-negative matrix factorization (NNMF) and k-nearest neighbor (KNN). However, the proposed approach does not restrict other algorithms from achieving the functions of data repairing and prediction.

Evaluating the ammonia emission from in-use vehicles using on-road remote sensing test

The on-road remote sensing test was conducted in Zhengzhou to obtain a large dataset of ammonia emissions from in-use vehicles. The ammonia emission characteristics and high-emitter vehicles of different manufacture years, vehicles with different emission standards, and vehicles with different types of other fuel vehicles were analysed. The results show that the average ammonia emission concentration obtained through remote sensing tests fluctuated after the initial reduction. The ammonia emission factors generally range from 0.30 to 0.47 g/kg, 0.34-0.50 g/kg and 0.29-0.60 g/kg for gasoline vehicles, diesel vehicles and other fuel vehicles respectively. Improving the emission standards of new vehicles has a direct role in reducing exhaust pollution from in-use vehicles. However, after the China III emission standard, the ammonia emission level showed a stable trend and no obvious downward trend. The distributions of ammonia emission rates were highly skewed as the dirtiest 10% of vehicles emitted much higher emissions than other vehicles. In the group with the highest emissions, the emissions from other fuel vehicles were lower than those from gasoline and diesel vehicles. However, the percentage of high-emitters decreased with newer emission standards for vehicles. The results indicate that remote sensing test technology will be very effective in screening vehicles with high ammonia emissions. However, some clean vehicles can be exempted from annual inspection through remote sensing test technology. Finally, based on the comprehensive analysis of big data from remote sensing, the ammonia emissions of diesel vehicles and other fuel vehicles cannot be ignored.

Social Cost Benefit Analysis of Operating Compressed Biomethane (CBM) Transit Buses in Cities of Developing Nations: A Case Study

Cities in developing nations have to deal with two significant sustainability challenges amidst rampant urbanization. First, consumer-generated food waste is increasing monumentally since open dumping is still followed as a predominant practice, the negative environmental externalities associated with food waste disposal are growing beyond manageable proportions. Second, the dependency on conventional fuels like diesel to operate transit buses, which is one of the major causes for deteriorating urban air quality. A nexus established between food waste management and operation of transit buses can improve the sustainable performance of cities in developing nations. In this study, a Life Cycle Assessment (LCA) supported Social Cost-Benefit Analysis (SCBA) is performed by considering a hypothetical scenario of establishing a large food waste treating biomethanation plant in Mumbai, India. The food waste from the city is transported to a biomethanation plant where it is subjected to an anaerobic digestion (AD) process. The biogas produced as a byproduct is upgraded to compressed biomethane (CBM) and used as a vehicle fuel to operate transit buses within the city. The LCA results suggest that CBM buses can reduce greenhouse gas and particulate matter emissions by 60% compared to diesel or compressed natural gas (CNG) buses. Fossil depletion potential of CBM buses is 98% lower than diesel, suggesting CBM’s importance in decoupling developing nations dependency on imported crude oil. The SCBA considers: (a) costs to stakeholders, i.e., fees for open dumping of food waste and cost of fuel for operating transit buses; and (b) social costs incurred by negative environmental externalities (obtained by monetizing LCA results) resulting from both, open dumping as well as fuel combustion. SCBA results indicate that the food waste-based CBM model can save 6.86 billion Indian rupees (USD 99.4 million) annually for Mumbai. The savings are made due to a reduction in stakeholder’s costs (fuel) coupled with societal, i.e., environmental externality costs if entire transit bus fleet operates on CBM fuel instead of conventional fuel mix (33:67 diesel to CNG) currently used. Although the study is performed for Mumbai, the results will be replicable to any city of developing nations facing similar issues.

Fine-grained vehicle emission management using intelligent transportation system data

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km²) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%-40% of vehicular emissions (e.g., CO₂ and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NO_X). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future.

On-road vehicle emissions and their control in China: A review and outlook

The large (26-fold over the past 25years) increase in the on-road vehicle fleet in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. China has established integrated emission control policies and measures since the 1990s, including implementation of emission standards for new vehicles, inspection and maintenance programs for in-use vehicles, improvement in fuel quality, promotion of sustainable transportation and alternative fuel vehicles, and traffic management programs. As a result, emissions of major air pollutants from on-road vehicles in China have peaked and are now declining despite increasing vehicle population. As might be expected, progress in addressing vehicle emissions has not always been smooth and challenges such as the lack of low sulfur fuels, frauds over production conformity and in-use inspection tests, and unreliable retrofit programs have been encountered. Considering the high emission density from vehicles in East China, enhanced vehicle, fuel and transportation strategies will be required to address vehicle emissions in China. We project the total vehicle population in China to reach 400-500 million by 2030. Serious air pollution problems in many cities of China, in particular high ambient PM_2.5 concentration, have led to pressure to accelerate the progress on vehicle emission reduction. A notable example is the draft China 6 emission standard released in May 2016, which contains more stringent emission limits than those in the Euro 6 regulations, and adds a real world emission testing protocol and a 48-h evaporation testing procedure including diurnal and hot soak emissions. A scenario (PC[1]) considered in this study suggests that increasingly stringent standards for vehicle emissions could mitigate total vehicle emissions of HC, CO, NO_X and PM_2.5 in 2030 by approximately 39%, 57%, 59% and 79%, respectively, compared with 2013 levels. With additional actions to control the future light-duty passenger vehicle population growth and use, and introduce alternative fuels and new energy vehicles, the China total vehicle emissions of HC, CO, NO_X and PM_2.5 in 2030 could be reduced by approximately 57%, 71%, 67% and 84%, respectively, (the PC[2] scenario) relative to 2013. This paper provides detailed policy roadmaps and technical options related to these future emission reductions for governmental stakeholders.