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

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).

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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

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-3161-2016 ◽

2016 ◽

Vol 16 (5) ◽

pp. 3161-3170 ◽

Cited By ~ 50

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).

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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

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-3171-2016 ◽

2016 ◽

Vol 16 (5) ◽

pp. 3171-3184 ◽

Cited By ~ 29

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.

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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

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-19239-2015 ◽

2015 ◽

Vol 15 (13) ◽

pp. 19239-19273 ◽

Cited By ~ 2

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.

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Development and Application of Urban High Temporal-Spatial Resolution Vehicle Emission Inventory Model and Decision Support System

Environmental Modeling & Assessment ◽

10.1007/s10666-017-9551-9 ◽

2017 ◽

Vol 22 (5) ◽

pp. 445-458 ◽

Cited By ~ 4

Author(s):

Yi Zhang ◽

Lin Wu ◽

Chao Zou ◽

Boyu Jing ◽

Xiaoyu Li ◽

...

Keyword(s):

Decision Support ◽

Decision Support System ◽

Spatial Resolution ◽

Support System ◽

Emission Inventory ◽

Inventory Model ◽

Vehicle Emission

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

Atmospheric Pollution Research ◽

10.1016/j.apr.2020.06.006 ◽

2020 ◽

Vol 11 (9) ◽

pp. 1598-1609 ◽

Cited By ~ 2

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

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.361-363.854 ◽

2013 ◽

Vol 361-363 ◽

pp. 854-859 ◽

Cited By ~ 1

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.

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Study on Gasoline Vehicle Emission Inventory Considering Regional Differences in China

Journal of Advanced Transportation ◽

10.1155/2018/7497354 ◽

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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High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated East Asian city

10.5194/acp-2016-69 ◽

2016 ◽

Cited By ~ 1

Author(s):

Shaojun Zhang ◽

Ye Wu ◽

Ruikun Huang ◽

Han Yan ◽

Yali Zheng ◽

...

Keyword(s):

High Resolution ◽

East Asia ◽

Air Pollutants ◽

Vehicle Emissions ◽

Emission Inventory ◽

Air Pollutant ◽

Assessment Tools ◽

Vehicle Emission ◽

Temporal And Spatial ◽

Resolution Simulation

Abstract. Vehicle emissions of air pollutants created substantial environmental impacts on air quality for many traffic-populated cities in East Asia. A high-resolution emission inventory is an irreplaceable tool compared with traditional tools (e.g., registration data based approach) to accurately evaluate real-world traffic dynamics and their environmental burden. In this study, Macao, one of the most populated cities in the world, is selected to demonstrate a high-resolution simulation of vehicular emissions and their contribution to air pollutant concentrations by coupling multi-models. First, traffic volumes by vehicle category on 47 typical roads were investigated during weekdays of 2010 and further applied in a networking demand simulation with the TransCAD model to establish hourly profiles of link-level vehicle counts. Local vehicle driving speed and vehicle age distribution data were also collected in Macao. Second, based on a localized vehicle emission model (e.g., the EMBEV-Macao), this study established a link-based vehicle emission inventory in Macao with high resolution meshed in a temporal and spatial framework. Furthermore, we employed the AERMOD model to map concentrations of CO, NO2 and primary PM2.5 contributed by local vehicle emissions during the weekdays of November 2010. This study has discerned the strong impact of traffic flow dynamics on the temporal and spatial patterns of vehicle emissions, such as a geographic discrepancy of spatial allocation up to 25 % between THC and PM2.5 emissions owing to spatially heterogeneous vehicle-use intensity between motorcycles and diesel fleets. We also identified that local vehicles are a dominant source of ambient NO2 in traffic-populated areas as evidenced by good agreement between AERMOD-simulated data and observed results. Therefore, this paper provides a case study and a solid framework for developing high-resolution environment assessment tools for other vehicle-populated cities in East Asia.

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Nepal Emission Inventory (NEEMI): a high resolution technology-based bottom-up emissions inventory for Nepal 2001–2016

10.5194/acp-2019-113 ◽

2019 ◽

Cited By ~ 2

Author(s):

Pankaj Sadavarte ◽

Maheswar Rupakheti ◽

Prakash V. Bhave ◽

Kiran Shakya ◽

Mark G. Lawrence

Keyword(s):

Air Pollution ◽

Energy Consumption ◽

High Resolution ◽

Total Energy ◽

Emission Inventory ◽

Point Sources ◽

Detailed Knowledge ◽

Total Energy Consumption ◽

Bottom Up ◽

Open Burning

Abstract. The lack of a comprehensive, up-to-date emission inventory for the Himalayan region is a major challenge in understanding the regional air pollution, including its impacts, mitigation, and the relevant atmospheric processes. This study develops a high resolution (1 km × 1 km) present-day emission inventory for Nepal with a higher-tier approach (detailed) to understanding the current combustion technologies and sectoral energy consumption. We estimate emissions of aerosols, trace gases and greenhouse gases from five energy-use sectors (residential, industry, commercial, agriculture and transport) and an open-burning source (agro-residue) for the period 2001–2016 (with 2011 as the base year), using bottom-up methodologies. Newly-measured country-specific emission factors (EFs) are used for emission estimates. It is estimated that the national total energy consumption in 2011 was 378 PJ with the residential sector being the largest energy consumer (79 %), followed by the industry (11 %) and transport (7 %) sectors. Biomass is the dominant energy source contributing 88 % to national total energy consumption, while the share of fossil fuel is only 12 %. With regards to open burning of the crop waste, it is estimated that 9.3 million tons of agro-waste was burned after harvesting crops in 2011. Nationally, 8.4 Tg CO2, 666 Gg CH4, 2.5 Gg N2O, 72 Gg NOX, 1984 Gg CO, 477 Gg NMVOC, 239 Gg PM2.5, 28 Gg BC, 99 Gg OC and 28 Gg SO2 were emitted from these sources in 2011. The energy consumption was also estimated for each year for the period 2001–2016 which shows an increase by a factor of 1.6 in 2016, while the emissions of various species increased by a factor of 1.2–2.4 with respect to 2001. An assessment of the top polluting technologies shows high emissions from traditional cookstoves using firewood, dungcakes, and agricultural residues, and open burning emissions of wood and residues. In addition, high emissions were also encountered from fixed chimney Bull's Trench kilns for brick production, cement kilns, two-wheeler gasoline vehicles, heavy diesel freight vehicles and kerosene lamps. A GIS-based gridded 1 km × 1 km population density map incorporating land-use and land cover data, settlement points, and topography was used for the spatial distribution of residential emissions. Geospatial locations were assigned to point sources, while activity-based proxies were used for other sources. Emissions were apportioned across different months from brick production, the agriculture sector, diesel generators, and space and water heating, using respective temporal variations of the activities. It was found that April had the maximum PM2.5 emissions, followed by December, January and February. Also, a wide variation in emissions distribution was found, highlighting the pockets of growing urbanization and the detailed knowledge about the emission sources. These emissions will be of value for further studies, especially air quality modelling studies focused on understanding the likely effectiveness of air pollution mitigation measures in Nepal.

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