scholarly journals Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

2016 ◽  
Vol 16 (5) ◽  
pp. 3161-3170 ◽  
Author(s):  
Boyu Jing ◽  
Lin Wu ◽  
Hongjun Mao ◽  
Sunning Gong ◽  
Jianjun He ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vehicle Emissions ◽  
Emission Inventory ◽  
Road Transport ◽  
Companion Paper ◽  
Emission Factors ◽  
Vehicle Emission ◽  
Traffic Data ◽  
Bottom Up ◽  
Macro Scale

Abstract. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission inventory with high temporal–spatial resolution (HTSVE) for the Beijing urban area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NOx, CO, HC and PM were 10.54  ×  104, 42.51  ×  104 and 2.13  ×  104 and 0.41  ×  104 Mg respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Based on the results of this study, improved air quality simulation and the contribution of vehicle emissions to ambient pollutant concentration in Beijing have been investigated in a companion paper (He et al., 2016).

scholarly journals Development of a high temporal-spatial resolution vehicle emission inventory based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

2015 ◽  
Vol 15 (19) ◽  
pp. 26711-26744 ◽  
Author(s):  
B. Y. Jing ◽  
L. Wu ◽  
H. J. Mao ◽  
S. L. Gong ◽  
J. J. He ◽  
...  
Keyword(s):  
Air Pollution ◽  
Spatial Resolution ◽  
Vehicle Emissions ◽  
Emission Inventory ◽  
Emission Factors ◽  
Inventory Model ◽  
Vehicle Emission ◽  
Traffic Data ◽  
Bottom Up ◽  
Macro Scale

Abstract. As the ownership of vehicles and frequency of utilization increase, vehicle emissions have become an important source of air pollution in Chinese cities. An accurate emission inventory for on-road vehicles is necessary for numerical air quality simulation and the assessment of implementation strategies. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near real time (NRT) traffic data on road segments to develop a high temporal-spatial resolution vehicle emission inventory (HTSVE) for the urban Beijing area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NOx, CO, HC and PM were 10.54 × 104, 42.51 × 104 and 2.13 × 104 and 0.41 × 104 Mg, respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Additionally, the on-road vehicle emission inventory model and control effect assessment system in Beijing, a vehicle emission inventory model, was established based on this study in a companion paper (He et al., 2015).

scholarly journals Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality

2016 ◽  
Vol 16 (5) ◽  
pp. 3171-3184 ◽  
Author(s):  
Jianjun He ◽  
Lin Wu ◽  
Hongjun Mao ◽  
Hongli Liu ◽  
Boyu Jing ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Resolution ◽  
Emission Inventory ◽  
Emission Factors ◽  
Pollutant Emissions ◽  
Vehicle Emission ◽  
Traffic Data ◽  
Near Surface ◽  
The Impact

Abstract. A companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment for 2013 in urban Beijing (Jing et al., 2016), which is used to investigate the impact of vehicle pollution on air pollution in this study. Based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modelling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing's main urban areas in the periods of summer (July) and winter (December) 2013. Generally, the CUACE model had good performance of the concentration simulation of pollutants. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes with time. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO2 and 5.4 and 10.5 % for PM2.5 in July and December 2013 respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO2 is 59.2 and 57.8 % in July and December 2013, while it is 8.7 and 13.9 % for PM2.5. The RVEC to PM2.5 is lower than the PM2.5 contribution rate for vehicle emission in total emission, which may be due to dry deposition of PM2.5 from vehicle emission in the near-surface layer occuring more easily than from elevated source emission.

scholarly journals Development of a high temporal–spatial resolution vehicle emission inventory based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality

2015 ◽  
Vol 15 (13) ◽  
pp. 19239-19273 ◽  
Author(s):  
J. J. He ◽  
L. Wu ◽  
H. J. Mao ◽  
H. L. Liu ◽  
B. Y. Jing ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Emission Inventory ◽  
Model Simulation ◽  
Pollutant Emissions ◽  
Vehicle Emission ◽  
Traffic Data ◽  
Near Surface

Abstract. In a companion paper (Jing et al., 2015), a high temporal–spatial resolution vehicle emission inventory (HTSVE) for 2013 in Beijing has been established based on near real time (NRT) traffic data and bottom up methodology. In this study, based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modeling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing main urban areas in the periods of summer (July) and winter (December) 2013. Generally, CUACE model had good performance of pollutants concentration simulation. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes over moment. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO2, while 5.4 and 10.5 % for PM2.5 in July and December 2013, respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO2 is 59.2 and 57.8 % in July and December 2013, while 8.7 and 13.9 % for PM2.5. The RVEC to PM2.5 is lower than PM2.5 contribution rate for vehicle emission in total emission, which may be caused by easily dry deposition of PM2.5 from vehicle emission in near-surface layer compared to elevated source emission.

A high-resolution spatial and temporal on-road vehicle emission inventory in an Iranian metropolitan area, Isfahan, based on detailed hourly traffic data

2020 ◽  
Vol 11 (9) ◽  
pp. 1598-1609 ◽  
Author(s):  
Omid Ghaffarpasand ◽  
Mohammad Reza Talaie ◽  
Hossein Ahmadikia ◽  
Amirreza Talaie Khozani ◽  
Maryam Davari Shalamzari
Keyword(s):  
High Resolution ◽  
Metropolitan Area ◽  
Emission Inventory ◽  
Vehicle Emission ◽  
Traffic Data ◽  
Road Vehicle

The Application of the IVE Model for Establishing the Vehicle Emission Inventory in the Urban Areas of Chongqing

2013 ◽  
Vol 361-363 ◽  
pp. 854-859 ◽  
Author(s):  
Xiao Feng ◽  
Ting Li Wang ◽  
Qi Zhao
Keyword(s):  
Urban Areas ◽  
Emission Inventory ◽  
Emission Factors ◽  
Survey Method ◽  
Vehicle Emission ◽  
Emission Model ◽  
Modified Method ◽  
Mobile Vehicle ◽  
Environmental Treatment ◽  
Gps System

This paper introduces a mobile vehicle emission model---- the IVE (international vehicle emission) model and presents the survey method to the main data and the modified method to the base emission factors by taking the urban areas of Chongqing as a case. The main data are collected by using some professional instruments such as global positioning satellite (GPS) system, video cameras and vehicle occupancy characteristics enumerator (VOCE), etc. The base emission factors can be modified by the data from on-broad emission test. This paper establish vehicle emission inventory in the urban areas of Chongqing and will make a good preparation for traffic environmental treatment in Chongqing.

scholarly journals Forecasting Greenhouse Gas Emissions from Heavy Vehicles: A Case study of Semarang City

2021 ◽  
Vol 18 (2) ◽  
pp. 254-260
Author(s):  
Mochamad Arief Budihardjo ◽  
Isaaf Fadhilah ◽  
Natasya Ghinna Humaira ◽  
Mochtar Hadiwidodo ◽  
Irawan Wisnu Wardhana ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Vehicle Emissions ◽  
Greenhouse Gas Emission ◽  
Control Strategies ◽  
Road Transport ◽  
Vehicle Emission ◽  
Heavy Duty ◽  
Intergovernmental Panel ◽  
Heavy Duty Vehicle ◽  
Using Data

In Indonesia, transportation sector, specifically road transport consumed most energy compared to other sectors. Eventually, the energy consumption will increase due to the growth of vehicle number that also escalate emission. Vehicle emissions had been recognized as a significant contributor to atmospheric greenhouse gas (GHG) pollution. Heavy-duty vehicles are considered as main sources of vehicular emissions in most cities. Therefore, it is crucial to take into account heavy-duty vehicle emission projections in order to support policymakers to identify vehicle emissions and develop pollution control strategies. The aim of this study is to forecast heavy-duty vehicle population, vehicle kilometers travelled (VKT), fuel consumption, and heavy-duty vehicle emissions using data of Semarang City to illustrate greenhouse gas emission of big cities in Indonesia. Business as Usual (BAU) and The Intergovernmental Panel on Climate Change (IPCC) method were incorporated to determine vehicle emission projection. Heavy-duty vehicle emissions increase from 2021 to 2030 by 12.317 to 22.865 Gg CO2/year with amount trucks and buses emissions of 21.981,5 Gg CO2/year and 884,2 Gg CO2/year, respectively.

scholarly journals Compilation of a road transport emission inventory for the Province of Turin: Advantages and key factors of a bottom–up approach

2014 ◽  
Vol 5 (4) ◽  
pp. 648-655 ◽  
Author(s):  
Luca Pallavidino ◽  
Rossella Prandi ◽  
Alessandro Bertello ◽  
Elisa Bracco ◽  
Francesco Pavone
Keyword(s):  
Emission Inventory ◽  
Road Transport ◽  
Key Factors ◽  
Bottom Up ◽  
Transport Emission

scholarly journals VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories

2018 ◽  
Vol 11 (6) ◽  
pp. 2209-2229 ◽  
Author(s):  
Sergio Ibarra-Espinosa ◽  
Rita Ynoue ◽  
Shane O'Sullivan ◽  
Edzer Pebesma ◽  
María de Fátima Andrade ◽  
...  
Keyword(s):  
Environmental Protection Agency ◽  
Transport Model ◽  
Road Transport ◽  
R Package ◽  
The United States ◽  
Source Model ◽  
Emission Factors ◽  
Vehicular Emissions ◽  
Bottom Up ◽  
Emissions Inventories

Abstract. Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies but also for urban planning and environmental health protection. We developed an open-source model (called Vehicular Emissions Inventory – VEIN v0.2.2) that provides high-resolution vehicular emissions inventories for different fields of studies. We focused on vehicular sources at street and hourly levels due to the current lack of information about these sources, mainly in developing countries.The type of emissions covered by VEIN are exhaust (hot and cold) and evaporative considering the deterioration of the factors. VEIN also performs speciation and incorporates functions to generate and spatially allocate emissions databases. It allows users to load their own emission factors, but it also provides emission factors from the road transport model (Copert), the United States Environmental Protection Agency (EPA) and Brazilian databases. The VEIN model reads, distributes by age of use and extrapolates hourly traffic data, and it estimates emissions hourly and spatially. Based on our knowledge, VEIN is the first bottom–up vehicle emissions software that allows input to the WRF-Chem model. Therefore, the VEIN model provides an important, easy and fast way of elaborating or analyzing vehicular emissions inventories under different scenarios. The VEIN results can be used as an input for atmospheric models, health studies, air quality standardizations and decision making.

scholarly journals Study on Gasoline Vehicle Emission Inventory Considering Regional Differences in China

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Dong Guo ◽  
Zhan-gu Wang ◽  
Liang Sun ◽  
Kai Li ◽  
Juan Wang ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Regional Differences ◽  
Vehicle Emissions ◽  
Emission Inventory ◽  
Control Strategies ◽  
Vehicle Emission ◽  
Correction Factors ◽  
Road Vehicles ◽  
Theoretical Support ◽  
Gasoline Vehicles

Rapid growth of China’s urban road vehicles, in particular, the increase in the number of gasoline vehicles, leads to an increase in the traffic congestion and problems pertaining to air pollution. The establishment of the emission inventory of gasoline vehicles is influenced by several factors, like environmental characteristics, vehicle conditions, road conditions, and so on. In order to obtain gasoline vehicle emission inventory in accordance with the actual situation in different regions, this study proposed a method of establishing a list of gasoline vehicles with regional differences. Comprehensive consideration and evaluation of various factors that affect the vehicle emissions were carried out and the corresponding correction factors were obtained. According to the formula of comprehensive emission factor for Zibo city, the emission inventory of gasoline vehicle was established. This method can be effectively utilized to obtain the emission inventory of gasoline vehicles in different cities more accurately and provide theoretical support for control strategies of gasoline vehicle emissions.

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