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

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-9965-2016 ◽

2016 ◽

Vol 16 (15) ◽

pp. 9965-9981 ◽

Cited By ~ 21

Author(s):

Shaojun Zhang ◽

Ye Wu ◽

Ruikun Huang ◽

Jiandong Wang ◽

Han Yan ◽

...

Keyword(s):

High Resolution ◽

Air Pollutants ◽

Vehicle Emissions ◽

Emission Inventory ◽

Air Pollutant ◽

Assessment Tools ◽

Vehicle Emission ◽

Eastern Asia ◽

Temporal And Spatial ◽

Resolution Simulation

Abstract. Vehicle emissions containing air pollutants created substantial environmental impacts on air quality for many traffic-populated cities in eastern Asia. A high-resolution emission inventory is a useful 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, Macau, 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 multimodels. First, traffic volumes by vehicle category on 47 typical roads were investigated during weekdays in 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 Macau. Second, based on a localized vehicle emission model (e.g. the emission factor model for the Beijing vehicle fleet – Macau, EMBEV–Macau), this study established a link-based vehicle emission inventory in Macau with high resolution meshed in a temporal and spatial framework. Furthermore, we employed the AERMOD (AMS/EPA Regulatory Model) model to map concentrations of CO and primary PM2.5 contributed by local vehicle emissions during weekdays in 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 26 % between THC and PM2.5 emissions owing to spatially heterogeneous vehicle-use intensity between motorcycles and diesel fleets. We also identified that the estimated CO2 emissions from gasoline vehicles agreed well with the statistical fuel consumption in Macau. Therefore, this paper provides a case study and a solid framework for developing high-resolution environment assessment tools for other vehicle-populated cities in eastern Asia.

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Supplementary material to "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-supplement ◽

2016 ◽

Author(s):

Shaojun Zhang ◽

Ye Wu ◽

Ruikun Huang ◽

Han Yan ◽

Yali Zheng ◽

...

Keyword(s):

High Resolution ◽

Air Pollutants ◽

Vehicle Emissions ◽

East Asian ◽

Supplementary Material ◽

Resolution Simulation

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A new vehicle emission inventory for China with high spatial and temporal resolution

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-32005-2013 ◽

2013 ◽

Vol 13 (12) ◽

pp. 32005-32052 ◽

Cited By ~ 7

Author(s):

B. Zheng ◽

H. Huo ◽

Q. Zhang ◽

Z. L. Yao ◽

X. T. Wang ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Vehicle Emissions ◽

Emission Inventory ◽

Anthropogenic Sources ◽

Vehicle Emission ◽

County Level ◽

Road Transportation ◽

Geographical Differences ◽

Administrative Unit

Abstract. This study is the first in a series of papers that aim to develop high-resolution emission databases for different anthropogenic sources in China. Here we focus on on-road transportation. Because of the increasing impact of on-road transportation on regional air quality, developing an accurate and high-resolution vehicle emission inventory is important for both the research community and air quality management. This work proposes a new inventory methodology to improve the spatial and temporal accuracy and resolution of vehicle emissions in China. We calculate, for the first time, the monthly vehicle emissions (CO, NMHC, NOx, and PM2.5) for 2008 in 2364 counties (an administrative unit one level lower than city) by developing a set of approaches to estimate vehicle stock and monthly emission factors at county-level, and technology distribution at provincial level. We then introduce allocation weights for the vehicle kilometers traveled to assign the county-level emissions onto 0.05° × 0.05° grids based on the China Digital Road-network Map (CDRM). The new methodology overcomes the common shortcomings of previous inventory methods, including neglecting the geographical differences between key parameters and using surrogates that are weakly related to vehicle activities to allocate vehicle emissions. The new method has great advantages over previous methods in depicting the spatial distribution characteristics of vehicle activities and emissions. This work provides a better understanding of the spatial representation of vehicle emissions in China and can benefit both air quality modeling and management with improved spatial accuracy.

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High-resolution vehicle emission inventory and emission control policy scenario analysis, a case in the Beijing-Tianjin-Hebei (BTH) region, China

Journal of Cleaner Production ◽

10.1016/j.jclepro.2018.08.256 ◽

2018 ◽

Vol 203 ◽

pp. 530-539 ◽

Cited By ~ 23

Author(s):

Wen Yang ◽

Chengyao Yu ◽

Wen Yuan ◽

Xinyi Wu ◽

Wei Zhang ◽

...

Keyword(s):

High Resolution ◽

Scenario Analysis ◽

Emission Inventory ◽

Emission Control ◽

Control Policy ◽

Vehicle Emission ◽

Policy Scenario ◽

Bth Region

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Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

10.5194/egusphere-egu21-15182 ◽

2021 ◽

Author(s):

Adrian Wenzel ◽

Jia Chen ◽

Florian Dietrich ◽

Sebastian T. Thekkekara ◽

Daniel Zollitsch ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Sensor Network ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Sensor Nodes ◽

Sensor Data ◽

Small Scale ◽

Urban Air

Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution &#8211; especially with respect to spatial and temporally variability &#8211; measurement data with high spatial and temporal resolution are critical.Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O3) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].After having conducted a measurement campaign in 2016 to create a high-resolution NO2 concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O3 and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.&#160;[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&#8211;3735, https://doi.org/10.5194/amt-11-3717-2018, 2018[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937&#8211;1946, https://doi.org/10.5194/amt-11-1937-2018, 2018[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241&#8211;13251, https://doi.org/10.5194/acp-20-13241-2020, 2020[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4&#8211;8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020

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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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Gridded Emission Inventory of Criteria Air Pollutants for National Capital Territory, Delhi

10.5194/egusphere-egu2020-22571 ◽

2020 ◽

Author(s):

Rahul Chaurasia ◽

Manju Mohan

Keyword(s):

Air Pollution ◽

Spatial Distribution ◽

High Resolution ◽

Power Plant ◽

Population Density ◽

Air Pollutants ◽

Emission Inventory ◽

Road Density ◽

Criteria Air Pollutants ◽

National Capital

The megacities of the world are experiencing a punishing level of air pollution where primary sources of emissions are industrial, residential and transportation. Delhi is also no exception and had been worst performing in terms of air quality and air pollution. In this backdrop, a high-resolution emission inventory becomes an essential tool to predict and forecast pollutant concentration along with the assessment of the impact of various government policies. This study aims to prepare a high-resolution gridded emission inventory (1km*1km) of criteria air pollutants (PM10, PM2.5, NO2, SO2 and CO) for Delhi-NCT (National Capital Territory).&#160; The bottom-up gridded emission inventory has been prepared taking account of population density, land use pattern and socio-economic status. The emission from all the primary sectors has been taken into accounts such as transport, residential burning, industries, power plants, and municipal solid waste burning.&#160; The emissions are estimated using emission factors and activity data for each sector. The emission factor for various fuel type burning is taken from CPCB (Central Pollution Control Board) reports and previous literature. Data corresponding to various sectors such as the amount of fuel consumed, population density, road density, traffic congestion points, industrial location, unauthorized colonies, slums, and total solid waste generation has been acquired from various government bodies, reports, and literature. The result reveals that the total estimated emissions from transportation, industries and domestic sector contribute nearly 72%, 60%, 52% of NOx, SO2 and PM10 emission respectively.&#160; The transport sector has been found as the bulk contributor towards CO and NOx emissions. Domestic sector and Power plant emission have been found to be a bulk contributor of CO and SO2. Later, the spatial distribution of the emission is done using GIS technique (Arc-GIS). For spatial distribution of emission, district-wise population data, road density data, power plant location and digitization of the road network was carried out.

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