Vehicle Emission Data Research Articles

Developing a system for the real-time collection and analysis of mobile vehicle emission data

In this study, an integrated system for remotely monitoring air pollution data was developed. This system combines vehicle exhaust emission data with driving information. It comprises an exhaust extraction device (EED), an improved On-Board Diagnostic II (OBDII) device, a mobile app, and a backend server database. The EED detects exhaust gases such as NOx, COx, and PM2.5 and transmits this information to the OBDII device through 2.4G communication. The OBDII device gathers vehicle driving information through a CAN 2.0 interface, integrates this information with vehicle exhaust emission data, and transmits the integrated information to the app through Bluetooth. The app forwards the integrated data to the backend database through 4G/5G communication for storage. Overall, this system provides a platform for the comprehensive collection, analysis, and application of big data pertaining to vehicle pollution sources and driving behavior. Analysis of actual driving experiments involving three vehicles revealed a correlation of over 80 % between vehicles’ revolutions per minute and CO2 and PM2.5 emissions, indicating that driving behaviors affect vehicle exhaust emissions. In summary, the proposed system not only stores relevant driving information in real time but also provides a platform for the future analysis of driving behavior and vehicle exhaust emissions.

A combined framework of Biplots and Machine Learning for real-world driving volatility and emissions data interpretation

Advanced visualization techniques can be useful for a better understanding of driving behavior and vehicle emissions in real-time. This study used classic and sparse HJ-biplots to examine the relationship between driving behavior, vehicle engine, exhaust emissions, and route type variables. Different Machine Learning classifiers were applied. Second-by-second vehicle dynamic, engine, and emissions data were collected from three light-duty vehicles (hybrid, diesel, and gasoline) and along three different routes (urban, rural, and highway). The dataset included a sample of 12,150 s of speed, acceleration, vehicular jerk, engine speed, engine load, fuel flow rate, vehicular specific power mode, carbon dioxide and nitrogen oxides emissions. The proposed methodology not only enables the distinction of driving styles, road types, and emissions profiles but also allows for revealing the correlation of variables in a single plot. The Random Forest algorithm showed to present the highest accuracy. This study can be useful in the context of road traffic emissions monitoring since it identifies hidden relationships in input data, and it reduces the redundancy in input parameters without compromising information.

Attention-based global and local spatial-temporal graph convolutional network for vehicle emission prediction

Nowadays the number of vehicles is increasing day by day and vehicle emission becomes a major pollution source. To wisely control vehicle emission, accurate vehicle emission prediction is of critical importance. However, accurate vehicle emission prediction suffers from many challenges, such as the strong nonlinearity of emission data and the temporal correlation and spatial interaction between different road segments, which become more complicated for mid- and long-term prediction. To resolve these challenging issues, we propose an attention-based global and local spatial-temporal graph convolutional network (AGLGCN) to effectively predict mid- and long-term vehicle emission through a graph structural network. The proposed AGLGCN consists of two major parts: 1) a spatial-temporal attention mechanism to effectively capture the dynamic spatial-temporal correlation of vehicle emission data by merging hourly, daily, and weekly sequences, 2) a global and local spatial graph convolution network to capture the hidden global and local spatial dependencies based on graph convolution. AGLGCN can capture the dynamic temporal correlation as well as the global and local spatial information variation of vehicle emission, and effectively predict mid- and long-term time series. Two real-world vehicle emission datasets are taken to evaluate AGLGCN. Experimental results demonstrate that our proposed AGLGCN can outperform some state-of-the-art methods.

A complex network approach for the model of vehicle emission propagation and intelligently mine the interaction rules

With the increasing number of motor vehicles, exhaust emission has become a major source of urban pollution. Most studies are limited to the prediction of pollutant concentration, which cannot clearly indicate the change of pollution emissions and regional relationship. In this paper, we propose an emission propagation model of vehicle source pollution based on complex network in order to intelligently mine the interaction and propagation rules hidden behind dynamic spatiotemporal data. First, aiming at the problems of low resolution and insufficient data volume of vehicle emission data, a high-resolution pollution emission data is generated based on the COPERT (Computer Program to Calculate Emissions from Road Transport). For study the influence of causality between regions, a propagation model is designed based on the convergent cross mapping method to transform the emission time series into a complex network. In addition, we propose a novel key node mining algorithm using hybrid local and global information to identify areas of heavy pollution. Experimental results on real datasets demonstrate that the spread of pollution follows certain rules and is also affected by regional influences. Moreover, the proposed algorithm is superior to the state-of-the-art methods.

EV Adoption Influence on Air Quality and Associated Infrastructure Costs

Exploring the system-level interactions within the modern urban transportation system, factors such as human health, vehicle exhaust pollution, air quality, emerging personal transportation technologies, and local weather events, are increasingly expedient considering the growth of human population centers projected in the 21st century. Pollutants often accumulate to unhealthy concentrations during winter inversion events such as those that commonly occur in Utah’s Salt Lake valley and other mountainous regions. This work examines the degree to which replacing conventionally powered vehicles with electric vehicles (EV) could reduce the near-road accumulation of criteria pollutants under various degrees of inversion depth and wind speed. Vehicle emissions data are combined with inversion and wind factors to determine changes in the Air Quality Index, and a first-order estimate of the cost required to build an EV charging infrastructure to support a given EV adoption scenario is also derived. Results are presented in the form of multiple Pareto frontiers and a simplified cost–benefit formula that inform potential public and private EV charging infrastructure investments to drive the EV adoption that would result in optimal air quality improvements during average weather and winter inversion events.

Study of Air Pollutions Caused by Exhaust Gases Emitted from Gasoline Vehicles in Erbil City

The environmental pollution caused by exhaust gas during parking in Erbil City’s Periodic Vehicle Inspection (PVI) was studied. The purpose of this research is to measure vehicle exhaust emissions from Erbil (PVI) gasoline vehicles. Tested the carbon monoxide (CO), carbon dioxide (CO2), oxygen (O2) and hydrocarbon (HC) emissions of gasoline vehicles. The statistical results show that there are significant differences between emission rates based on vehicle characteristics (such as fuel type, model year, engine fuel supply system, and regular maintenance). In addition, these factors will also affect the emission level test results based on the Iraqi vehicle emission standards. In addition, the emissions of these exhaust gases also depend on factors such as engine combustion design and operating conditions, fuel grade, lubricants, local road conditions and other factors, and continue to affect the concentration of pollutants emitted by vehicles (such as carbon dioxide and unburned hydrocarbons) These pollutants are very toxic to the human body and cause environmental pollution. Accurate estimates of pollutant emissions are needed to ensure proper design and implementation of air quality plans. Emission factors (EFs) are empirical functions between pollutant emissions and their activities. In this review article, in connection with the development of EFs found in emission models used to generate emission inventories, techniques for measuring emissions from road vehicles are studied. The emission measurement technologies covered include those most widely used for road vehicle emission data collection, namely chassis and engine dynamometer measurements, remote sensing, road tunnel research, and portable emission measurement systems (PEMS). The main advantages and disadvantages of each method with regard to emission modeling are presented. It also reviews ways in which EF can be derived from test data, with a clear distinction between data obtained under controlled conditions (engine and chassis dynamometer measurements using standard driving cycles) and measurements under real conditions.

Spatiotemporal Graph Convolution Multifusion Network for Urban Vehicle Emission Prediction.

Urban vehicle emission prediction can help the regulation of vehicle pollution and traffic control. However, it is hard to predict the spatiotemporal variation of vehicle emission because of the spatial interactions and temporal correlations between different road segments as well as the high nonlinearity and complexity of vehicle emission variation. The existing methods solve the problem by splitting the region into standard segments or grids based on conventional deep learning methods, without considering that urban vehicle emission varies by graph-structured traffic road network and depends on many complex external environment factors. To address these issues, a spatiotemporal graph convolution multifusion network (ST-MFGCN) is proposed to leverage the graph structural properties as the inherent connectivity of road network for urban vehicle emission prediction, which can capture the vehicle emission spatiotemporal variation patterns and learn the effects of complex environmental factors. The proposed model consists of three parts: 1) a spatiotemporal graph convolution module to capture spatiotemporal dependencies by merging closeness, period, and trend sequences with temporal convolution as well as graph convolution is introduced to model the spatial dependencies; 2) an external factor component to divide multisource external factors into global and individual external features; and 3) a general fusion component to merge the spatiotemporal patterns and the external features as well as fit the mutation of emission measurement data by multifusion strategy. Finally, the proposed model is evaluated on the practical monitoring data of vehicle emission data in Hefei, and the results demonstrate that our proposed model can predict regional vehicle emissions effectively.

Vehicle emission control on road with temporal traffic information using deep reinforcement learning

The increased vehicle usage significantly aggravate the urban air pollution, which have great impact on the public health. Therefore, it is necessary to make proper traffic control policies and reduce traffic emissions. However, it is difficult to establish control strategies based on modeling methods, and carry out online control based on historical traffic information for the complex time-varying characteristics of emissions. In this paper, we present a deep reinforcement learning emission control strategy, which automatically learns the optimal traffic flow and speed limits to reduce traffic emission on the target road segment based on the temporal traffic information. The proposed approach is evaluated on real world vehicle emission data in Hefei. And the results demonstrate the effectiveness of the proposed approach against baseline methods.

A NOx Emission Model Incorporating Temperature for Heavy-Duty Diesel Vehicles with Urea-SCR Systems Based on Field Operating Modes

The selective catalytic reduction (SCR) is the most commonly used technique for decreasing the emissions of nitrogen oxides (NOx) from heavy-duty diesel vehicles (HDDVs). However, the same injection strategy in the SCR system shows significant variations in NOx emissions even at the same operating mode. This kind of heterogeneity poses challenges to the development of emission inventories and to the assessment of emission reductions. Existing studies indicate that these differences are related to the exhaust temperature. In this study, an emission model is established for different source types of HDDVs based on the real-time data of operating modes. Firstly, the initial NOx emission rates (ERs) model is established using the field vehicle emission data. Secondly, a temperature model of the vehicle exhaust based on the vehicle specific power (VSP) and the heat loss coefficient is established by analyzing the influencing factors of the NOx conversion efficiency. Thirdly, the models of NOx emissions and the urea consumption are developed based on the chemical reaction in the SCR system. Finally, the NOx emissions are compared with the real-world emissions and the estimations by the proposed model and the Motor Vehicle Emission Simulator (MOVES). This indicates that the relative error by the proposed method is 12.5% lower than those calculated by MOVES. The characteristics of NOx emissions under different operating modes are analyzed through the proposed model. The results indicate that the NOx conversion rate of heavy-duty diesel trucks (HDDTs) is 39.2% higher than that of urban diesel transit buses (UDTBs).

Deep spatiotemporal residual early-late fusion network for city region vehicle emission pollution prediction

Regulation on the urban vehicle emission has great impact on our daily lives and can protect public health. However, there are sparse emission remote sensing stations in city, and vehicle emission data is both spatial and temporal non-stationary, which is influenced by various internal and external factors, such as spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), environments (road network, meteorology, events, traffic flow and POIs). In this paper, we introduce a semi-supervised learning approach with co-training geographical weighted regression model, which aims to construct the historical emission observations with the insufficient stations records. And then we formulate the region emission prediction as a spatiotemporal sequence forecasting problem and propose a deep spatiotemporal residual early-late fusion network based on unique properties of spatiotemporal data, to predict vehicle emissions in each region of the given city. And the residual convolution network is employed to model the temporal properties of region vehicle emissions. Finally, we present experiments with the remote sensing records of Hefei, where the proposed model outperforms the other baselines. This result demonstrates that combining deep spatiotemporal residual early-late fusion network with the semi-supervised geographical weighted regression can predict vehicle emission in each region of city effectively.

Vehicle Emission Forecasting Based on Wavelet Transform and Long Short-Term Memory Network

This paper proposes a time series model based on wavelet transform and long short-term memory (LSTM) network to forecast vehicle emission. It implements the semi-supervised collaborative training regression to compensate missing emissions data. The accumulated carbon monoxide (CO), hydrocarbons (HC), and nitric oxide (NO) concentrations emitted by vehicles in different lanes per hour were taken to quantitatively characterize the vehicle emissions. The original time series of vehicle emission data, which may be highly variable, is decomposed into several lowly variable sub-series by wavelet transform. For each sub-series, an LSTM time series model is proposed to forecast vehicle emissions. More specifically, the inputs of that LSTM model are the weather variables, the driving variables of the concerned vehicle and historical emissions records while its output is the predicted accumulated concentrations of CO, HC, and NO. The three types of predicted concentrations of all sub-series are summed up, respectively, and produce the desired prediction of the total emission of each type. The proposed model is verified through real data which was collected between May 2017 and December 2017 at the multi-lane monitoring station of Baimiao South Road, Daxing District, Beijing, China. It confirms that our model based on wavelet transform and LSTM can efficiently improve the correlation coefficient (R) and the index of agreement (IA) against conventional models, such as ARIMA and wavelet-ARIMA model.

Application of Instantaneous Emission Modeling for Rural Vehicle Emission Estimation

Rural vehicles (RVs) contribute a large proportion to total air pollutant emissions in China. In order to develop and evaluate the RV emission control legislation and policy, accurate emission factors and emission inventories for RVs need to be investigated. In this paper, based on the tri-wheel rural vehicle (3-W RV) emission data measured using portable emission measurement system (PEMS) on real roads, an instantaneous emission model was developed and used to estimate the hot stabilized 3-W RV emissions and fuel consumption under real driving conditions. The modeling approach of the instantaneous emission model was presented, and the instantaneous emission model was validated using the experimental results. The predicted emissions and fuel consumption of Foton Lovol 3-W RV met well with the measured results over the selected driving trip, and the prediction errors were in the range of 3-9%.

Evaluation of diesel fleet emissions and control policies from plume chasing measurements of on-road vehicles

Abstract Vehicle emissions are an important source of urban air pollution. Diesel fuelled vehicles, although constituting a relatively small fraction of fleet population in many cities, are significant contributors to the emission inventory due to their often long mileage for goods and public transport. Recent classification of diesel exhaust as carcinogenic by the World Health Organization also raises attention to more stringent control of diesel emissions to protect public health. Although various mandatory and voluntary based emission control measures have been implemented in Hong Kong, there have been few investigations to evaluate if the fleet emission characteristics have met desired emission reduction objectives and if adoption of an Inspection/Maintenance (I/M) programme has been effective in achieving these objectives. The limitations are partially due to the lack of cost-effective approaches for the large scale characterisation of fleet based emissions to assess the effectiveness of control measures and policy. This study has used a plume chasing method to collect a large amount of on-road vehicle emission data of Hong Kong highways and a detailed analysis was carried out to provide a quantitative evaluation of the emission characteristics in terms of the role of high and super-emitters in total emission reduction, impact of after-treatment on the multi-pollutants reduction strategy and the trend of NO2 emissions with newer emission standards. The study revealed that not all the high-emitters are from those vehicles of older Euro emission standards. Meanwhile, there is clear evidence that high-emitters for one pollutant may not be a high-emitter for another pollutant. Multi-pollutant control strategy needs to be considered in the enactment of the emission control policy which requires more comprehensive retrofitting technological solutions and matching I/M programme to ensure the proper maintenance of fleets. The plume chasing approach used in this study also shows to be a useful approach for assessing city wide vehicle emission characteristics.

Trends in on-road vehicle emissions and ambient air quality in Atlanta, Georgia, USA, from the late 1990s through 2009

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NOx), and volatile organic compounds (VOCs) during 1995–2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NOx, and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995–2009 period despite an increase in total vehicle distance traveled. The CO and NOx emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NOx in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001–2009. Although this trend coexists with the declining trends in on-road NOx, VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors.Implications:Large reductions in on-road vehicle emissions of CO and NOx in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NOx during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.

Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact

The transportation sector's carbon footprint and dependence on oil are of deep concern to policy makers in many countries. Use of all-electric drive trains is arguably the most realistic medium-term solution to address these concerns. However, motorist anxiety induced by an electric vehicle's limited range and high battery cost have constrained consumer adoption. A novel switching-station-based solution is touted as a promising remedy. Vehicles use standardized batteries that, when depleted, can be switched for fully charged batteries at switching stations, and motorists only pay for battery use. We build a model that highlights the key mechanisms driving adoption and use of electric vehicles in this new switching-station-based electric vehicle system and contrast it with conventional electric vehicles. Our model employs results from repairable item inventory theory to capture switching-station operation; we embed this model in a behavioral model of motorist use and adoption. Switching-station systems effectively transfer range risk from motorists to the station operator, who, through statistical economies of scale, can better manage it. We find that this transfer of risk can lead to higher electric vehicle adoption than in a conventional system, but it also encourages more driving than a conventional system does. We calibrate our models with motorist behavior data, electric vehicle technology data, operation costs, and emissions data to estimate the relative effectiveness of the two systems under the status quo and other plausible future scenarios. We find that the system that is more effective at reducing emissions is often less effective at reducing oil dependence, and the misalignment between the two objectives is most severe when the energy mix is coal heavy and has advanced battery technology. Increases in gasoline prices (by imposition of taxes, for instance) are much more effective in reducing carbon emissions, whereas battery-price-reducing policy interventions are more effective for reducing oil dependence. Taken together, our results help a policy maker identify the superior system for achieving the desired objectives. They also highlight that policy makers should not conflate the dual objectives of oil dependence and emissions reductions as the preferred system, and the policy interventions that further that system may be different for the two objectives. This paper was accepted by Yossi Aviv, operations management.

A GIS approach to modelling CO2 emissions associated with the pupil-school commute

Concerns have been raised in numerous countries over declining rates of active transport to school. In a UK context, the pupil-school commute is estimated to contribute around 658 kilotonnes of CO2 per year; however, tackling this issue effectively requires an improved understanding of how emissions can be modelled and mapped over a variety of scales. This paper implements a new estimation technique for the modelling of CO2 emissions linked with the school commute that integrates both transport network-level routing and geographically disaggregate vehicle emissions data. The model is then applied to a national cohort of pupils in England. Areas demonstrating the highest emissions were typically more rural and/or comprising more affluent resident populations. Emissions were also shown to increase with school year, with larger step changes between educational stages reflecting the different geography of school locations. Furthermore, where secondary school entry policies were selective or based on a religious domination, average emissions were typically higher than in non-selective schools.

Emissions and Fuel Consumption Modeling for Evaluating Environmental Effectiveness of ITS Strategies

Road transportation is a major fuel consumer and greenhouse gas emitter. Recently, the intelligent transportation systems (ITSs) technologies, which can improve traffic flow and safety, have been developed to reduce the fuel consumption and vehicle emissions. Emission and fuel consumption estimation models play a key role in the evaluation of ITS technologies. Based on the influence analysis of driving parameters on vehicle emissions, this paper establishes a set of mesoscopic vehicle emission and fuel consumption models using the real-world vehicle operation and emission data. The results demonstrate that these models are more appropriate to evaluate the environmental effectiveness of ITS strategies with enough estimation accuracy.

Study on Eco-Route Planning Algorithm and Environmental Impact Assessment

The transportation sector has faced increased environmental pressure due to the rapid motorization in China over recent years. In addition to energy policies and the use of fuel-efficient vehicles, CO2 emissions and fuel consumption can be lowered through the application of intelligent transportation systems (ITS) technologies that can affect drivers’ route choice decision-making process and reduce congestion. Ecological route (eco-route) planning is one such strategy that provides the least CO2 emission and fuel consumption routes for vehicles. In this article, a set of mesoscopic vehicle emission and fuel consumption models are established based on locally collected vehicle operation and emission data, and these models are more appropriate for evaluating the effectiveness of ITS strategies with sufficient estimation accuracy. Based on the proposed models and a dynamic traffic information database, an eco-route planning algorithm is proposed as well, which is expected to be consistent with the road network characteristics of China cities. Furthermore, the environmental impact of the eco-route is fully evaluated with tests undertaken in Beijing urban areas. The results show that the proposed eco-route algorithm significantly reduces fuel consumption and has good environmental performance.

Post-processing of vehicle emission test data for use in exhaust after-treatment modelling and analysis

The accurate measurement of transient engine-out exhaust compositions, flow rate and temperature is critical for analysing and understanding the performance of exhaust system after-treatment components. This paper describes procedures for processing the transient emission data measured at vehicle test sites. These procedures include estimation of the hydrogen, oxygen and water concentrations and correction of the engine-out exhaust gas flow for the first few seconds of the drive cycle for non-oxygenated and oxygenated fuels. Both secondary air injection and non-secondary air injection applications are discussed. A procedure for accurately measuring the exhaust gas temperature during transient operations is also described with a specific thermocouple recommended for best results. A checklist for handling and processing the measured data is also provided. In addition to providing better data for further analysis and understanding of exhaust after-treatment components, the procedures listed in this work allow consistent and reliable input data to be generated for the mathematical model and model-based application studies using the measured vehicle emission data.

Modeling air pollutant emissions from Indian auto-rickshaws: Model development and implications for fleet emission rate estimates

Abstract Chassis dynamometer tests were conducted on 40 Indian auto-rickshaws with 3 different fuel–engine combinations operating on the Indian Drive Cycle (IDC). Second-by-second (1 Hz) data were collected and used to develop velocity-acceleration look-up table models for fuel consumption and emissions of CO 2 , CO, total hydrocarbons (THC), oxides of nitrogen (NO x ) and fine particulate matter (PM2.5) for each fuel–engine combination. Models were constructed based on group-average vehicle activity and emissions data in order to represent the performance of a ‘typical’ vehicle. The models accurately estimated full-cycle emissions for most species, though pollutants with more variable emission rates (e.g., PM 2.5 ) were associated with larger errors. Vehicle emissions data showed large variability for single vehicles (‘intra-vehicle variability’) and within the test group (‘inter-vehicle variability’), complicating the development of a single model to represent a vehicle population. To evaluate the impact of this variability, sensitivity analyses were conducted using vehicle activity data other than the IDC as model input. Inter-vehicle variability dominated the uncertainty in vehicle emission modeling. ‘Leave-one-out’ analyses indicated that the model outputs were relatively insensitive to the specific sample of vehicles and that the vehicle samples were likely a reasonable representation of the Delhi fleet. Intra-vehicle variability in emissions was also substantial, though had a relatively minor impact on model performance. The models were used to assess whether the IDC, used for emission factor development in India, accurately represents emissions from on-road driving. Modeling based on Global Positioning System (GPS) activity data from real-world auto-rickshaws suggests that, relative to on-road vehicles in Delhi, the IDC systematically under-estimates fuel use and emissions; real-word auto-rickshaws consume 15% more fuel and emit 49% more THC and 16% more PM 2.5 . The models developed in this study can be used to further explore the impact of varying vehicle activity patterns on emissions in efforts to manage air quality and mitigate air pollution exposure and air pollution related health impacts.