Model for Traffic Emissions Estimation (original) (raw)
Related papers
A Specific Approach for Estimating Traffic-Induced Urban Pollution
Polish Journal of Environmental Studies, 2015
Most air pollution originates from combustion processes, so it is important to make quantitative as well as qualitative analysis, as the sources of pollution can be stochastic-especially for road traffic. The point of our study is to better determine road traffic as one of the largest pollution sources in the city of Niš, Serbia. The research was conducted at several locations in the city that represent all of the main roads across the city. Parameters considered in the study were traffic frequency, vehicle type, annual vehicle mileage in the city, and average vehicle velocity. Besides these parameters, roadside CO 2 concentration was measured for evaluation. These data were used as the input for the COPERT methodology of air pollutant emissions calculation from road transport. On the basis of these data, annual CO 2 emissions and major pollutants were calculated, as well as the specific average emissions on roads. According to this, a classification of roads in respect to their specific average emissivity is given at the end of this paper.
Influence of Traffic Emissions Estimation Variability on Urban Air Quality Modelling
2002
The main objective of this work is to analyse how uncertainties in emission data of nitrogen oxides (NO x ) and volatile organic compounds (VOC), originated from road traffic, influence the model prediction of ozone (O 3 ) concentration fields. Different methods to estimate emissions were applied and results were compared in order to obtain their variability. Based on these data, different emission scenarios were compiled for each pollutant considering the minimum and the maximum values of the estimated emission range. These scenarios were used as input to the MAR-IV mesoscale modelling system. Simulations have been performed for a summer day in the Northern Region of Portugal. The different approaches to estimate NO x and VOC traffic emissions show a significant variability of absolute values and of their spatial distribution. Comparison of modelling results obtained from the two scenarios presents a dissimilarity of 37% for ozone concentration fields as a response of the system to a variation in the input emission data of 63% for NO x and 59% for VOC. Far beyond all difficulties and approximations, the developed methodology to build up an emission data base shows to be consistent and an useful tool in order to turn applicable an air quality model. Nevertheless, the sensitivity of the model to input data should be considered when it is used as a decision support tool.
Traffic pollution modelling and emission data
Environmental Modelling & Software, 2006
Evaluation of traffic pollution in streets requires basically information on three main factors: traffic emissions, the meteorological conditions and the street surroundings. Dispersion models exist with various degree of sophistication, which are able to properly describe the dispersion conditions, and thus to predict the relationships between emissions and the concentration levels in the street. However, for real-world applications, the model calculations must be based on ''true'' emission data, and their estimation is not trivial. Significant uncertainty is still connected with emission data. Examining the relationships between model predictions and measurements with respect to the meteorological conditions and inter-relationships between different pollution components allows quantitative evaluation of the traffic emissions. This methodology is illustrated using the Danish Operational Street Pollution Model e OSPM, and time series of traffic related pollutants. Street level concentrations of NO x and CO are calculated using OSPM as the dispersion model and emission data estimated by the widely used COPERT methodology. Comparison with measurements shows significant underestimation of the pollution concentrations and especially the CO/NO x ratio. An alternative set of traffic emission factors, using a more simplified vehicle classification methodology, provides better agreement with the measured concentrations. Evaluation of these results provides some guidance on the necessary modifications of the ''real-world'' traffic emission factors.
Estimation of vehicular emissions by capturing traffic variations
Atmospheric Environment, 2007
Increase in traffic volumes and changes in travel-related characteristics increase vehicular emissions significantly. It is difficult, however, to accurately estimate emissions with current practice because of the reliance on travel forecasting models that are based on steady state hourly averages and, thus, are incapable of capturing the effects of traffic variations in the transportation network. This paper proposes an intermediate model component that can provide better estimates of link speeds by considering a set of Emission Specific Characteristics (ESC) for each link. The intermediate model is developed using multiple linear regression; it is then calibrated, validated, and evaluated using a microscopic traffic simulation model. The improved link speed data can then be used to provide better estimates of emissions. The evaluation results show that the proposed emission estimation method performs better than current practice and is capable of estimating time-dependent emissions if traffic sensor data are available as model input.
2007
Air quality in urban environments can today be modelled by a large number of computer models (empirical, box models, CFD). OSPM (Operational Street Pollution Model) is one of the most widely used empirical-box models due to its simplicity and its very good performance. However, in most cases the necessary data, even for such simple computations, are not all available, leading to large errors, especially when employed for future planning. The City of Thessaloniki (Greece) was studied as an example, as although few input-data are available, there are enough measurements to validate the model's results. The current work proposes a methodology for dealing with this lack of data, confirmed by comparison with measured values. Following that, a sensitivity analysis for the most common input parameters is presented. Finally, OSPM is employed to predict the air quality in some highly-possible future scenarios.
A coupled macroscopic traffic and pollutant emission modelling system for Barcelona
Transportation Research Part D: Transport and Environment, 2021
We present a coupled macroscopic traffic and emission modelling system tailored to the Barcelona metropolitan area that allows estimating hourly road transport emissions at road link level. We use the developed system to perform an emission sensitivity analysis of typically high uncertainty emission features and assess their impact. We also explore the uncertainties of our system compared to a microscopic approach in a representative area of Barcelona. The developed macroscopic system shows a high sensitivity to spatially-resolved vehicle fleet composition inputs, meteorological effects on diesel engines (+19% in NO) and non-exhaust sources (80% of total PM emissions). The comparison with the microscopic system shows that discrepancies grow as a function of the congestion level, up to +65% in NO. The resulting coupled system will be used in further steps of the research to evaluate the impact of traffic management strategies upon urban emissions and air quality levels in Barcelona.
Emission estimation based on traffic models and measurements
Linköping Studies in Science and Technology. Licentiate Thesis, 2019
Contents 3.2 Limitations associated with cross-sectional data 3.3 Comparison of the traffic estimation methods 3.4 Computational results 3.5 Discussion 4 Emission estimation based on static traffic models 4.1 Definition and categorisation of the problems 4.
Atmospheric Environment, 2007
Traffic emission estimation in developing countries is a key-issue for air pollution management. In most cases, comprehensive bottom-up methodologies cannot be applied in mid-sized cities because of the resource cost related to their application. In this paper, a simplified emission estimation model (SEEM) is evaluated. The model is based on a top-down approach and gives annual global hot emission. Particular attention is paid to the quality of the input traffic data. The quality of results is assessed by application of the SEEM model in the Chilean Gran Concepcio´n urban area and by comparison with a bottom-up approach that has been led for the year 2000. The SEEM model estimates emissions with an accuracy of about 20% and is related to important resource savings. The results of the SEEM model are then distributed in space with a disaggregation approach and using GIS techniques. The relevancy of the disaggregation approach is evaluated among several possibilities through statistical methods. A spatial disaggregation using principal roads density gives the best results in terms of emissions repartition and gives a globally accurate image of the distribution of hot emissions in a mid-sized city. r
A methodology for modelling traffic related emissions in suburban areas
Transport, 2013
A methodology that integrates a computer program COPERT III for calculation of traffic emissions estimates, and a transportation modelling software CUBE VOYAGER was used to assess pollutant emissions for a suburban area, as a support for future transport planning strategies to be applied for any developing road network. COPERT III is used to obtain the carbon monoxide emission factors by accounting for the car fleet composition, characteristics and average speed. An aggregated emission parametric equation was determined and used further on for estimating network carbon monoxide emissions based upon the output of macroscopic traffic characteristics enabled by traffic simulation software, CUBE VOYAGER. The methodology and modelling results are applied here for Floreşti, a satellite town of Cluj-Napoca, Romania.
Transportation Research Part D: Transport and Environment, 2018
The development of accurate emission inventories at an urban scale is of utmost importance for cities in light of climate change commitments and the need to identify the emission reduction potential of various strategies. Emission inventories for on-road transportation are sensitive to the network models used to generate traffic activity data. For large networks (cities or regions), average-speed models have been relied upon extensively in research and practice, primarily due to their computational attractiveness. Nevertheless, these models are myopic to traffic states and driving cycles and therefore lack in accuracy. The aim of this study is to improve the quality of regional on-road emission inventories without resorting to computationally-intensive traffic microsimulation of an entire region. For this purpose, macroscopic, mesoscopic, and microscopic emission models are applied and compared, using average speed, average speed and its standard deviation, and instantaneous speeds. We also propose a hybrid approach called the CLustEr-based Validated Emission Re-calculation (CLEVER), which bridges between the microscopic and mesoscopic approaches. CLEVER defines unsupervised traffic conditions using a combination of mesoscopic traffic characteristics for selected road segments, and identifies a representative emission factor (EF) for each condition based on the microscopic driving cycle of the sample. Regional emissions can then be estimated by classifying segments in the regional network into these conditions, and applying corresponding EFs. The results of the CLEVER method are compared with the results of microsimulation and of mesoscopic approaches revealing a robust methodology that improves the emission inventory while reducing computational burden.