Vehicle Emission Model Research Articles

Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade

Past research showed on-road emissions patterns unique to hybrid electric vehicles (HEVs), indicating the need to account for them in emissions models as projected HEV sales increase over the coming decades. This work defines and characterizes a variable that quantifies HEV operating behavior to inform future development of new HEV emissions models based on current knowledge of conventional vehicle (CV) emissions patterns. Instantaneous hybridization factor (IHF), was quantified using on-road data collected from a 2010 Toyota Camry HEV. IHF is the ratio of electric system power to total system power and accounts for energy storage in the high voltage battery (IHF ranges from −1 to +1). Relationships between IHF and vehicle specific power (VSP), road type and road grade were examined. Negative VSP resulted in regenerative braking operation (IHF = −0.01 to −1) 90% of the time. IHF identified the VSP range where HEV operation was highly variable (VSP = −1 to 8 kW/ton) when driving at speeds below the ICE-off threshold (42 mph). VSP and IHF together account for 76–86% of the variability in HEV CO2 emissions in this study. CO2 model results using VSP computed with the measured real-world road grade (R2 = 0.86) gave improved fits over the no-grade VSP model (R2 = 0.69). This study establishes one framework for calculating the instantaneous HEV power split, confirms the need to include road grade in VSP for accurate modeling of vehicle emissions, and identified the need for significant improvements in on-board diagnostic (OBD) scantool measurement requirements for HEVs in three areas: (1) temporal resolution (sub-second to capture transient events such as ICE restarts); (2) simultaneous data logging capability for multiple controller area networks (i.e., engine and HEV parameters together); and (3) improved data precision.

Modelling of instantaneous emissions from diesel vehicles with a special focus on NOx: Insights from machine learning techniques

Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, we apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to: (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. The application of dynamic time warping and clustering analysis by NOx emissions resulted in 17 clusters capturing 88% of trips in the dataset. We show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, we evaluate three instantaneous emissions models: a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOx predictions, on par with the MLP: relative errors in prediction of emission factors are below 20% for both models, average fractional biases are −0.01 (s.d. 0.02) and −0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that we present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOx emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.

Speed-guided intelligent transportation system helps achieve low-carbon and green traffic: Evidence from real-world measurements

Intelligent transportation systems (ITS) have emerged as essential measures to manage traffic congestion and to reduce vehicular emission and energy consumption, while their effectiveness has not been directly quantified under real-world driving conditions. By conducting real-world driving and emission measurements with a portable emission measurement system, we found that the usage of the speed guided-ITS (SG-ITS) significantly reduces fuel consumption and pollutant emissions (i.e., carbon monoxide, nitrogen monoxide, nitrogen dioxide, and particle number) of both tested vehicles (a light-duty gasoline car and a heavy-duty diesel truck) through decreasing the frequency of starts and stops, the strength of acceleration, and the time proportions of acceleration and idle. These reductions obtained from real-world measurements are higher than those predicted by vehicle emission models such as Motor Vehicle Emission Simulator (MOVES) and Computer Programme to calculate Emissions from Road Transport (COPERT). Reductions in fuel consumption and pollutant emissions due to the usage of the SG-ITS are most efficient on urban roads (low traffic) among all the three road types, while the most time saving can be obtained on urban roads (high traffic). By combining a high-resolution vehicle emission inventory, we estimate that the implementation of the SG-ITS may significantly reduce the total vehicular exhaust emissions of nitrogen oxides, carbon monoxide, total hydrocarbons, and particulate matter in Beijing by 15.9%, 20.5%, 23.9%, and 22.5%, respectively. Our study directly evaluates the benefits of using SG-ITS based on real-world measurements, highlighting that implementation of the SG-ITS can be a practical policy option in terms of efficiently reducing energy consumption, mitigating urban air pollution, and alleviating urban traffic congestion.

Traffic characteristics and pollutant emission from road transport in urban area

In this work, the traffic characteristics and emissions of air pollutants were predicted for two vehicle classifications (passenger cars and trucks) at the national highway in Kyoto City, Japan. Traffic characteristic information (traffic volume, travel speed, and degree of congestion) was estimated based on the digitised data collected by the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. A vehicle emission model, known as the computer program to calculate emissions from road transport (COPERT), was utilised to compute the emission factors (EFs) and total emissions of air pollutants in terms of exhaust particulate matter (PMExh), benzene (C6H6), carbon monoxide (CO), and nitrogen oxide (NOx). Input variables, such as fuel data, activity data, driving conditions, and meteorological conditions, are needed. The findings revealed that the pollutant emissions reached the higher values over the slower travel speed phase. Road no. 1 with the most congested road segment has intensified vehicle numbers, and the slowest traffic flow movement exposed a greater magnitude of pollutant emissions. C6H6 and CO emissions are obviously more emitted from the passenger cars whereas the trucks are responsible for the greater emission of NOx and PMExh. The EFs of pollutants were compared with the Japanese Emission Standards through JE05 and JC08 chassis dynamometer test cycles. The estimated EFs showed inconsistency with the EFs derived from the test cycles. These results may be deployed as the input in air quality dispersion modelling in urban areas for designing the air pollution abatement strategy.

Vehicle emissions inventory in high spatial–temporal resolution and emission reduction strategy in Harbin-Changchun Megalopolis

Harbin-Changchun Megalopolis (HCM), as one of the vehicle production centers in China, has rapidly increasing vehicles on road supported by people’s increasing purchasing power, having directly and indirectly led to air pollution. This study systematically analyzes tempo-spatial characteristics of vehicle emissions by combining International Vehicle Emissions (IVE) model with the Technical guidelines for road motor vehicle emission inventory of air pollutants (Guideline) in HCM. This research further analyzes emissions from various emissions sources and projected emissions based on scenarios. The results show that the CO and HC emissions from mini passenger cars (MiniPC) and ordinary motorcycles using gasoline account for 86.4 % and 82.3 % of total emissions. Diesel-fueled heavy-duty trucks (HDT) are the main sources of NOX and PM, accounting for 86.5 % and 89.7 % of the total emissions, respectively. Three emission reduction scenarios are developed to analyze the vehicle emissions in HCM in 2020. This study concluded that phasing out the old vehicles is an effective strategy to mitigate air pollutions in HCM, with reduction rates of pollutants CO, HC, NOX, PM2.5 and PM10 being 19.8 %, 19.6 %, 8.6 %, 18.3 % and 18.8 %, respectively.

Intelligent Control Algorithm for Vehicle Exhaust Emission

In order to effectively control the emission of vehicle exhaust, it is necessary to analyse and study the control algorithm of vehicle exhaust emission. When using the current algorithm to control the vehicle exhaust emission, it cannot accurately calculate the ambient air capacity of nitric oxide, hydrocarbons, particulate matter and do not have synergistic control effect. On the basis of mathematical analysis, an intelligent control algorithm for vehicle exhaust emission is proposed. International Vehicle Emissions (IVE) model is used to calculate the emission of pollutants in the running and starting stages of the vehicle, so as to improve the precision of the vehicle exhaust emission. The box model and road traffic model are used to accurately calculate the atmospheric environmental capacity and analyse the pollutant emission reduction based on the calculation results. Through the PID control algorithm, the control of vehicle exhaust is completed and the synergistic control effect of the method is improved.

Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region

Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.

Methodology of motor transport air pollution monitoring of large city taking into account residential development type

A complex method of motor transport air pollution monitoring of a large city was developed, taking into account meteorological conditions and types of residential development, combining traditional computational methods, the results of field research, data obtained from the experiments on dispersion modeling of motor vehicle emissions under various conditions and mathematical modeling. The methodology allows for the operation of a monitoring system for a part of the city’s territory (within the given boundaries). It’s able to determine areas with negative ecological condition in need of management decisions to improve the urban environment; identify favorable areas where it is possible to ensure complete ecological safety when placing construction sites; predict changes in the atmospheric air quality in a changing residential development.

MOVES-Matrix for high-performance on-road energy and running emission rate modeling applications

ABSTRACTThe MOVES model was developed by the U.S. Environmental Protection Agency (U.S. EPA) to estimate emissions from on-road mobile sources and nonroad sources in the United States. Coupling high-resolution on-road vehicle activity data with appropriate MOVES emission rates further advances research efforts designed to assess the environmental impacts of transportation design and operation strategies. However, the complicated MOVES interface and slow performance makes it difficult to assess large, regional scale transportation networks and to undertake analyses of large-scale systems that are dynamic in nature. The MOVES-Matrix system develops an initial Large Matrix of MOVES outputs by running MOVES 146,853 times on the PACE high performance computing cluster to generate more than 90 billion emission rates to populate the matrix for a single area with one fuel regime and one inspection and maintenance program. A total of 117 such Large Matrices would be needed for the entire United States. The MOVES-Matrix system developed can be used to conduct the emissions modeling 200-times faster than using MOVES. The hypothetical case study shows that MOVES-Matrix is able to generate the exact same emission results as the MOVES model to ensure the validity for regulatory analysis. The resulting matrix allows users to link emission rates to big data projects and to evaluate changes in emissions for dynamic transportation systems in near-real-time. MOVES-Matrix does not currently estimate emissions from starts, hoteling or evaporative emissions, and the research team is working on MOVES-Matrix version 2 that supports incorporating off-network modeling.Implications: MOVES-Matrix should be of interest to a broad readership including those interested in vehicle emission modeling, near-road air quality modeling, transportation conformityanalysis. The paper should also interest engineers who are involved in transportation regulatory and conformity analysis, state implementation plan, and who are seeking an efficient way of conducting regulatory emission modeling and air quality analysis in the United States.

Study on minimum emission control strategy on arterial road based on improved simulated annealing genetic algorithm

In this paper, an optimal signal control model was presented to minimize the traffic emissions on an arterial road. In order to solve the optimal model, an improved simulated annealing genetic algorithm (ISAGA) was utilized by integrating microscopic traffic flow model with vehicle emission model. For simplicity, three intersections on a signalized arterial road were taken into account. During the optimizing process, the traffic flow model and the emission model were embedded into ISAGA as a fitness-calculating module. Through the experimental simulation, the results indicate that the optimal signal timing of the multi-intersection was realized by minimizing the emissions of vehicles on the arterial road. Moreover, it is found that the improved simulated annealing genetic algorithm is effective in solving the optimization model.

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

In order to realize the simulation and evaluation of road traffic noise among urban buildings, a spatial subdivision-based beam-tracing method is proposed in this study. First, the road traffic source is divided into sets of point sources and described with the help of vehicle emission model. Next, for each pair of source and receiver, spatial subdivision-based beam-tracing method is used in noise paths generation. At last, noise distribution can be got by noise calculation of all receivers considering the complex transmission among urban buildings. A measurement experiment with a point source is carried out to validate the accuracy of the method; the 0.8 m height and 2.5-m height average errors are about 0.9 dB and 1.2 dB, respectively. Moreover, traffic noise analysis under different building layouts and heights are presented by case applications and conclusions can be reached: (1) Different patterns result in different noise distributions and patterns designed as self-protective can lead to an obvious noise abatement for rear buildings. Noise differences between the front and rear buildings are about 7–12 dB with different patterns. (2) Noise value might not show a linear variation along with the height as shielding of different layers is various in reality.

Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal

Abstract. Air pollution is one of the most pressing environmental issues in the Kathmandu Valley, where the capital city of Nepal is located. We estimated emissions from two of the major source types in the valley (vehicles and brick kilns) and analyzed the corresponding impacts on regional air quality. First, we estimated the on-road vehicle emissions in the valley using the International Vehicle Emissions (IVE) model with local emissions factors and the latest available data for vehicle registration. We also identified the locations of the brick kilns in the Kathmandu Valley and developed an emissions inventory for these kilns using emissions factors measured during the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) field campaign in April 2015. Our results indicate that the commonly used global emissions inventory, the Hemispheric Transport of Air Pollution (HTAP_v2.2), underestimates particulate matter emissions from vehicles in the Kathmandu Valley by a factor greater than 100. HTAP_v2.2 does not include the brick sector and we found that our sulfur dioxide (SO2) emissions estimates from brick kilns are comparable to 70 % of the total SO2 emissions considered in HTAP_v2.2. Next, we simulated air quality using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for April 2015 based on three different emissions scenarios: HTAP only, HTAP with updated vehicle emissions, and HTAP with both updated vehicle and brick kilns emissions. Comparisons between simulated results and observations indicate that the model underestimates observed surface elemental carbon (EC) and SO2 concentrations under all emissions scenarios. However, our updated estimates of vehicle emissions significantly reduced model bias for EC, while updated emissions from brick kilns improved model performance in simulating SO2. These results highlight the importance of improving local emissions estimates for air quality modeling. We further find that model overestimation of surface wind leads to underestimated air pollutant concentrations in the Kathmandu Valley. Future work should focus on improving local emissions estimates for other major and underrepresented sources (e.g., crop residue burning and garbage burning) with a high spatial resolution, as well as the model's boundary-layer representation, to capture strong spatial gradients of air pollutant concentrations.

Climate co-benefits of alternate strategies for tourist transportation: The case of Murree Hills in Pakistan.

This study explores the climate impacts of on-road tourist transportation with alternate mitigation strategies. To this end, greenhouse gas (GHG) emissions for 2016 and emissions under four "what-if" scenarios were estimated for a popular tourist site in Pakistan, i.e., Murree Hills, using the international vehicle emissions model. Alternate scenarios included occupancy optimization, bus transit system, and Euro II and Euro IV implementation. The emissions were further decomposed using the log mean Divisia index method to study the drivers of global warming potential (GWP) mitigation. As per the results, the total 20-year GWP for 2016 was equal to 51,262 tons CO2 equivalent, and maximum reduction was achieved under the bus transit system scenario having a 20-year GWP of 25,736 tons CO2 equivalent, i.e., 49.8% reduction. Relative to the base year, GWP reductions were also quite significant for Euro IV (46.8%), Euro II (45.8%), and occupancy optimization (32.3%) scenarios. For the base year, CO2 held a share of 87.3% in total emissions; however, its share in the 20-year GWP was 39.7% indicating its reduced impact on total GWP as compared to N2O, CO, NOx, VOC, and CH4. Based on the decomposition results for alternate scenarios, GWP mitigation was mainly driven by CO, CH4, NOx, VOCs, and partially by CO2, while N2O negatively affected GWP mitigation. These results provide several policy-level instruments for developing countries to design a transition to an eco-friendly tourist transport management system. The policy implications from this study can be used to promote an eco-tourism industry.

NO2 hotspots: Are we measuring in the right places?

Abstract Nitrogen Dioxide (NO2) is a major atmospheric pollutant which is produced from a variety of anthropogenic sources, notably vehicular emissions. High NO2 concentrations are found in and around the urban environment and as such pose a significant threat to human health. Diffusion tube surveys of NO2 concentrations were carried out at a variety of intersections and road layouts over a 3-month period in 2017. This study identified that ‘hotspots’ in NO2 concentrations frequently occurred in sections of road where vehicles are accelerating or queueing. Under these conditions, concentrations were elevated by 58.6% and 52.6% respectively compared to when vehicles were cruising. Analysis of environmental factors which influence NO2 concentrations show that meteorology, topography, traffic volume and driver behaviour all contribute. The results highlight the complexity of monitoring hotspot locations and show the need for traffic and driver behaviour to be included in vehicle emission models. Additionally, the investigation suggests need for caution when relying on in-situ monitors to determine pollutant exceedances as there is shown to be substantial variation along a street and the location of the monitor is critical.

Vehicle Energy/Emissions Estimation Based on Vehicle Trajectory Reconstruction Using Sparse Mobile Sensor Data

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation’s dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.

Modeling Traffic Congestion Based on Air Quality for Greener Environment: An Empirical Study

The primary focus of this paper is to govern traffic congestion on urban road networks based upon a cumulative approach comprising of traffic flow modeling, vehicle emission modeling, and air quality modeling. Based upon the traffic conditions, a simulation model is proposed and further tested for performance metrics, which is relative to three main aspects, namely, the waiting time of the vehicles at the junctions/intersections/signals, the type of pollutant emitted by a vehicle, and the traveling time. The experimental analysis and validation are carried out for different case studies in Malaysia, such as Petaling Jaya, Shah Alam, Mont Kiara, and Jalan Tun Razak. Three different scenarios (morning, afternoon, and evening) are analyzed and tested to explore the traffic usage parameter. The results showed that when traffic is modeled and governed based upon traffic flow, vehicle emission, and air quality index (AQI), nearly 75% of traffic congestion is mitigated, hence making the atmosphere pollution free as well as avoiding Urban Heat Island (UHI) effect due to the heat generated from vehicles. The experimental results are tested, validated, and compared with existing solutions for performance analysis. The proposed model is aimed toward overcoming the major drawbacks of existing approaches, such as single-path suggestions, traffic delay during peak hours/emergencies, non-recurring congestion consideration, congestion avoidance instead of recovering from it, improper reporting of road accidents, and notifications about traffic jam ahead to the users and high vehicle usage rate.

Vehicle Emission Computation Through Microscopic Traffic Simulation Calibrated Using Genetic Algorithm

Abstract Vehicle emission calculation is critical for evaluating motor vehicle related environmental protection policies. Currently, many studies calculate vehicle emissions from integrating the microscopic traffic simulation model and the vehicle emission model. However, conventionally vehicle emission models are presented as a stand-alone software, requiring a laborious processing of the simulated second-by-second vehicle activity data. This is inefficient, in particular, when multiple runs of vehicle emission calculations are needed. Therefore, an integrated vehicle emission computation system is proposed around a microscopic traffic simulation model. In doing so, the relational database technique is used to store the simulated traffic activity data, and these data are used in emission computation through a built-in emission computation module developed based on the IVE model. In order to ensure the validity of the simulated vehicle activity data, the simulation model is calibrated using the genetic algorithm. The proposed system was implemented for a central urban region of Nanjing city. Hourly vehicle emissions of three types of vehicles were computed using the proposed system for the afternoon peak period, and the results were compared with those computed directly from the IVE software with a trivial difference in the results from the proposed system and the IVE software, indicating the validity of the proposed system. In addition, it was found for the study region that passenger cars are critical for controlling CO, buses are critical for controlling CO and VOC, and trucks are critical for controlling NOx and CO2. Future work is to test the proposed system in more traffic management and control strategies, and more vehicle emission models are to be incorporated in the system.

Assessment of urban passenger fleet emissions to quantify climate and air quality co-benefits resulting from potential interventions

Lack of robust data to assess the effects of different policies may hamper the design and implementation of effective solutions to reduce traffic emissions. This study analyzed the emissions of passenger fleets in Bandung, Indonesia, illustrated with three emission scenarios against the 2015 baseline. Local surveys were conducted to get traffic activity data for International Vehicle Emissions (IVE) modeling to generate emission factors (EFs) relevant to actual fleets and driving conditions in the city. EFs obtained for gasoline fleets that could have been affected by the leaded gasoline used prior 2006 were adjusted for the catalyst deactivation effect. Annual emissions (Gg/year) for CO, VOC, NOx, PM, BC, OC, NH3, air toxics and SO2 were 168, 36, 16, 1.8, 0.68, 0.72, 0.68, 4.7 and 0.42, respectively. Emissions of CO2, N2O and CH4 were 2679; 0.07 and 5.8 Gg/year, respectively. Collectively, the 20-year global warming potential (GWP) of the emissions was 6770 Gg/year CO2 eq. Analysis of potential interventions of technology intrusion of Euro3 (S1) and Euro4 (S2), and catalyst revamping (S3) showed potential co-benefits to air quality improvement and climate forcing mitigation. S1 would reduce the toxic pollutants emissions by 62% and GWP by 29%; corresponding reductions would be 68% and 45% for S2 and 47% and 16% for S3. Actual measurements are required to validate the potential emission reductions by catalyst revamping.

Full-chain health impact assessment of traffic-related air pollution and childhood asthma

BackgroundAsthma is the most common chronic disease in children. Traffic-related air pollution (TRAP) may be an important exposure contributing to its development. In the UK, Bradford is a deprived city suffering from childhood asthma rates higher than national and regional averages and TRAP is of particular concern to the local communities. AimsWe estimated the burden of childhood asthma attributable to air pollution and specifically TRAP in Bradford. Air pollution exposures were estimated using a newly developed full-chain exposure assessment model and an existing land-use regression model (LUR). MethodsWe estimated childhood population exposure to NOx and, by conversion, NO2 at the smallest census area level using a newly developed full-chain model knitting together distinct traffic (SATURN), vehicle emission (COPERT) and atmospheric dispersion (ADMS-Urban) models. We compared these estimates with measurements and estimates from ESCAPE's LUR model. Using the UK incidence rate for childhood asthma, meta-analytical exposure-response functions, and estimates from the two exposure models, we estimated annual number of asthma cases attributable to NO2 and NOx in Bradford, and annual number of asthma cases specifically attributable to traffic. ResultsThe annual average census tract levels of NO2 and NOx estimated using the full-chain model were 15.41 and 25.68 μg/m3, respectively. On average, 2.75 μg/m3 NO2 and 4.59 μg/m3 NOx were specifically contributed by traffic, without minor roads and cold starts. The annual average census tract levels of NO2 and NOx estimated using the LUR model were 21.93 and 35.60 μg/m3, respectively. The results indicated that up to 687 (or 38% of all) annual childhood asthma cases in Bradford may be attributable to air pollution. Up to 109 cases (6%) and 219 cases (12%) may be specifically attributable to TRAP, with and without minor roads and cold starts, respectively. ConclusionsThis is the first study undertaking full-chain health impact assessment of TRAP and childhood asthma in a disadvantaged population with public concern about TRAP. It further adds to scarce literature exploring the impact of different exposure assessments. In conservative estimates, air pollution and TRAP are estimated to cause a large, but largely preventable, childhood asthma burden. Future progress with childhood asthma requires a move beyond the prevalent disease control-based approach toward asthma prevention.

Influences of accumulated mileage and technological changes on emissions of regulated pollutants from gasoline passenger vehicles

In this study, the influences of accumulated mileage (deterioration) and technological changes (emission standards) on emission factors (EFs) of regulated pollutants (CO, HC, and NOx) from gasoline passenger vehicles were investigated based on Inspection and Maintenance (I/M) data using the chassis dynamometer method. The accumulated mileage of passenger vehicles was significantly linearly correlated with vehicle age. For most cases, the average EFs of CO, HC and NOx were significantly linearly correlated with accumulated mileage, indicating that emission deterioration had a significant impact on pollutant EFs. Implemented emission standards markedly influenced the EFs of regulated pollutants, and EFs markedly decreased with progressing emission standards. The present study also compared EFs of regulated pollutants between this study and the International vehicle emission (IVE) model, and marked differences in EFs were seen with variations in emission standards, vehicle types and accumulated mileage; NOx EFs in this study were higher than in the IVE model. The results provide new insight into estimating regulated pollutant emissions using the IVE model.