5 resultados para vehicle emission

em Universidad Politécnica de Madrid


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Emission inventories are databases that aim to describe the polluting activities that occur across a certain geographic domain. According to the spatial scale, the availability of information will vary as well as the applied assumptions, which will strongly influence its quality, accuracy and representativeness. This study compared and contrasted two emission inventories describing the Greater Madrid Region (GMR) under an air quality simulation approach. The chosen inventories were the National Emissions Inventory (NEI) and the Regional Emissions Inventory of the Greater Madrid Region (REI). Both of them were used to feed air quality simulations with the CMAQ modelling system, and the results were compared with observations from the air quality monitoring network in the modelled domain. Through the application of statistical tools, the analysis of emissions at cell level and cell – expansion procedures, it was observed that the National Inventory showed better results for describing on – road traffic activities and agriculture, SNAP07 and SNAP10. The accurate description of activities, the good characterization of the vehicle fleet and the correct use of traffic emission factors were the main causes of such a good correlation. On the other hand, the Regional Inventory showed better descriptions for non – industrial combustion (SNAP02) and industrial activities (SNAP03). It incorporated realistic emission factors, a reasonable fuel mix and it drew upon local information sources to describe these activities, while NEI relied on surrogation and national datasets which leaded to a poorer representation. Off – road transportation (SNAP08) was similarly described by both inventories, while the rest of the SNAP activities showed a marginal contribution to the overall emissions.

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streets in local residential areas in large cities, real traffic tests for pollutant emissions and fuel consumption have been carried out in Madrid city centre. Emission concentration and car activity were simultaneously measured by a Portable Emissions Measurement System. Real life tests carried out at different times and on different days were performed with a turbo-diesel engine light vehicle equipped with an oxidizer catalyst and using different driving styles with a previously trained driver. The results show that by reducing the speed limit from 50 km h-1 to 30 km h-1, using a normal driving style, the time taken for a given trip does not increase, but fuel consumption and NOx, CO and PM emissions are clearly reduced. Therefore, the main conclusion of this work is that reducing the speed limit in some narrow streets in residential and commercial areas or in a city not only increases pedestrian safety, but also contributes to reducing the environmental impact of motor vehicles and reducing fuel consumption. In addition, there is also a reduction in the greenhouse gas emissions resulting from the combustion of the fuel.

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Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

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Particulate matter emissions from paved roads are currently one of the main challenges for a sustainable transport in Europe. Emissions are scarcely estimated due to the lack of knowledge about the resuspension process severely hampering a reliable simulation of PM and heavy metals concentrations in large cities and evaluation of population exposure. In this study the Emission Factors from road dust resuspension on a Mediterranean freeway were estimated per single vehicle category and PM component (OC, EC, mineral dust and metals) by means of the deployment of vertical profiles of passive samplers and terminal concentration estimate. The estimated PM10 emission factors varied from 12 to 47 mg VKT?1 (VKT: Vehicle Kilometer Traveled) with an average value of 22.7 ? 14.2 mg VKT?1. Emission Factors for heavy and light duty vehicles, passenger cars and motorbikes were estimated, based on average fleet composition and EPA ratios, in 187e733 mg VKT?1, 33e131 VKT?1, 9.4e36.9 VKT?1 and 0.8e3.3 VKT?1, respectively. These range of values are lower than previous estimates in Mediterranean urban roads, probably due to the lower dust reservoir on freeways. PM emitted material was dominated by mineral dust (9e10 mg VKT?1), but also OC and EC were found to be major components and approximately 14 e25% and 2e9% of average PM exhaust emissions from diesel passenger cars on highways respectively.

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Atmospheric emissions from road transport have increased all around the world during the last decades more rapidly than from other pollution sources. For instance, they contribute to more than 25% of total CO, CO2, NOx, and fine particle emissions in most of the European countries. This situation shows the importance of road transport when complying with emission ceilings and air quality standards applied to these pollutants. This paper presents a modelling system to perform atmospheric emission projections (simultaneously both air quality pollutants and greenhouse gases) from road transport including the development of a tailored software tool (EmiTRANS) as a planning tool. The methodology has been developed with two purposes: 1) to obtain outputs used as inputs to the COPERT4 software to calculate emission projections and 2) to summarize outputs for policy making evaluating the effect of emission abatement measures for a vehicle fleet. This methodology has been applied to the calculation of emission projections in Spain up to 2020 under several scenarios, including a sensitivity analysis useful for a better interpretation and confidence building on the results. This case study demonstrates the EmiTRANS applicability to a country, and points out the need for combining both technical and non-technical measures (such as behavioural changes or demand management) to reduce emissions, indirectly improving air quality and contributing to mitigate climate change.