Development of a road transport emission inventory for Greece and the Greater Athens Area: Effects of important parameters

Fameli, Kyriaki-Maria; Assimakopoulos, Vasiliki D.

doi:10.1016/j.scitotenv.2014.10.015

Cited by 85 publications

(38 citation statements)

References 51 publications

(59 reference statements)

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“…This model is widely used in Europe to calculate air pollutant and greenhouse gas emissions from road transport. [21][22][23] The IVE model is another model for the calculation of average emission rates for different vehicle categories and facility types. The IVE model is specifically designed to have the flexibility needed by developing nations in their efforts to address mobile source emissions [24].…”

Section: Introductionmentioning

confidence: 99%

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

Shahbazi

Reyhanian

Hosseini

et al. 2016

Emiss. Control Sci. Technol.

113

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Tehran, a city with 8.5 million inhabitants, has suffered from rapid and unplanned urbanization in recent years resulting in substantial environmental impacts perhaps foremost of which is poor air quality. A major source of air pollution is emissions from mobile vehicles; therefore, having an accurate and comprehensive mobile source emission inventory is essential for decision-makers to develop mitigating strategies. The aim of this study is to determine the relative contributions of specific mobile sources to key air pollutants through the development of an emissions inventory for mobile sources in the city of Tehran using the International Vehicle Emissions (IVE) model. Tehran traffic data were acquired to obtain link level emission rates, using IVE emission rates. The developed emission inventory was evaluated using Tehran gasoline sales data. The results indicate that the sources of carbon monoxide (CO), volatile organic compound (VOC), nitrogen oxide (NO x ), and sulfur oxide (SO x ) emissions are mainly passenger cars. The contribution of emissions of CO, VOCs, and particulate matter (PM) from motorcycles to the total traffic emissions is more than 15, 31, and 12 %, respectively. Despite the fact that medium and heavy-duty vehicles (minibuses, buses, and trucks) only comprise 2.4 % of the Tehran fleet, they contribute more than 41, 64, and 85 % of the NO x , SO x , and PM emissions, respectively. Analyzing the distribution of the aggregated emission of pollutants shows that emissions are mostly higher in central zones due to the high traffic rate of passenger cars, taxis, motorcycles, and buses.

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Section: Introductionmentioning

confidence: 99%

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

Shahbazi

Reyhanian

Hosseini

et al. 2016

Emiss. Control Sci. Technol.

113

View full text Add to dashboard Cite

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“…Inventories for vehicular CO, NMVOC, NO x , PM 2.5 , PM 10 , CO 2 , CH 4 , N 2 O, NH 3 , and SO 2 emissions in the CCUA in 2020, under the different emission reduction scenarios described above, were calculated and compared with the vehicular emissions under the BAU scenario. The predicted vehicle population data, emission factors, and annual VKT for the different vehicle types will produce CO, NMVOC, NO x , PM 2.5 , PM 10 Emissions of CO will be lower in 2020 under each of the different scenarios than under the BAU scenario except for the CAER and RAER scenarios (Figure 7). The CAER and RAER scenarios will increase CO emissions by 4.39% and 4.29%, relative to the BAU scenario.…”

Section: Reduction Effects Of Different Control Scenariosmentioning

confidence: 99%

“…There is, thus, an urgent need to study regional vehicular emissions and emission reduction strategies in China. Many studies research on the vehicular emissions estimation and control strategies using different methods for some areas [8][9][10]. In China, Che (2010) studied the vehicular pollutant reduction control strategy and evaluated the emission reduction effects of five control measures, including "eliminating the yellow label vehicle (eliminating gasoline vehicles where emission level is lower than the State I emission standard, and diesel vehicles where emission level is lower than the State III emission standard in China)" and "implementing new emission standards" in the Pearl River Delta region.…”

Section: Introductionmentioning

confidence: 99%

“…Li (2015) [12] estimated vehicular emission inventory (CO, NO x , HC, and PM) for the Beijing-Tianjin-Hebei region between 2007 and 2011 based on COPERT model and analyzed its emission characteristics, then set up five reduction scenarios and assessed their emission reductions effects. Guo et al (2016) [13] used the scenario analysis method to estimate vehicular emissions of CO, NO x , hydrocarbons, and PM 10 in Beijing for 2011-2020 for three pollutant control measures. However, as researchers have paid more attention to vehicular emissions on the developed regions and cities of China, few studies have focused on the vehicular emissions and emission reduction strategies of the Chinese central and western regions, such as the CCUA.…”

Section: Introductionmentioning

confidence: 99%

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Air Pollutant Emissions from Vehicles and Their Abatement Scenarios: A Case Study of Chengdu-Chongqing Urban Agglomeration, China

2019

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Vehicular emissions have become one of the important sources of air pollution, and their effective control is essential to protect the environment. The Chengdu-Chongqing Urban Agglomeration (CCUA), a less developed area located in the southwest of China with higher vehicle population and special topographic features, was selected as the research area. The aims of this study were to establish multi-year vehicular emission inventories for ten important air pollutants in this area and to analyze emission control policy scenarios based on the inventories. The results showed that the ten vehicular pollutant emissions had differences during the past decade, and CO2 and NH3 increased markedly between 1999 and 2015. Chengdu and Chongqing were the dominant contributors of vehicular emissions in the CCUA. Eight scenarios based on these inventories were designed and the alternative energy replacement scenario was studied from the life-cycle perspective. Compared with the business as usual scenario, elimination of substandard vehicles scenario is the most effective policy to control NOx, PM2.5, PM10, and CH4 emissions; the radical alternative energy replacement scenario could decrease the vehicular NMVOC, CO2, N2O, and NH3 emissions; the elimination of motorcycles scenario could decrease the vehicular CO emissions; and the raising fuel standards scenario could reduce vehicular SO2 emissions significantly (by 94.81%). The radical integrated scenario (combining all of the reduction control measures mentioned above) would achieve the maximum emission reduction of vehicular pollutants CO, NMVOC, NOx, PM2.5, PM10, CO2, N2O, and NH3 compared with any scenario alone.

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“…One of the important models is GIS modelling. Fameli et al [16] developed a model for predicting traffic emissions by using mathematical equations that depend on statistical information such as fuel consumption and fleet composition. The predicted emissions were graphically represented using GIS techniques (6 × 6 km, 2 × 2 km).…”

Section: Previous Workmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

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Development of a road transport emission inventory for Greece and the Greater Athens Area: Effects of important parameters

Cited by 85 publications

References 51 publications

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

The Relative Contributions of Mobile Sources to Air Pollutant Emissions in Tehran, Iran: an Emission Inventory Approach

Air Pollutant Emissions from Vehicles and Their Abatement Scenarios: A Case Study of Chengdu-Chongqing Urban Agglomeration, China

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

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