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

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 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

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-26711-2015 ◽

2015 ◽

Vol 15 (19) ◽

pp. 26711-26744 ◽

Cited By ~ 1

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).

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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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Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽

10.28974/idojaras.2021.4.5 ◽

2021 ◽

Vol 125 (4) ◽

pp. 625-646

Author(s):

Zita Ferenczi ◽

Emese Homolya ◽

Krisztina Lázár ◽

Anita Tóth

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Weather Prediction ◽

Horizontal Resolution ◽

Model System ◽

Air Quality Model ◽

Quality Model ◽

Near Surface ◽

The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

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Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-6393-2017 ◽

2017 ◽

Vol 17 (10) ◽

pp. 6393-6421 ◽

Cited By ~ 48

Author(s):

Eri Saikawa ◽

Hankyul Kim ◽

Min Zhong ◽

Alexander Avramov ◽

Yu Zhao ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Emission Inventory ◽

Air Pollutant ◽

Central China ◽

Pollutant Emissions ◽

Mitigation Measures ◽

Economic Activities ◽

Temporal And Spatial Distribution ◽

Emissions Inventories

Abstract. Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 µg m−3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.

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A high-resolution emission inventory of primary pollutants for the Huabei region, China

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-481-2012 ◽

2012 ◽

Vol 12 (1) ◽

pp. 481-501 ◽

Cited By ~ 136

Author(s):

B. Zhao ◽

P. Wang ◽

J. Z. Ma ◽

S. Zhu ◽

A. Pozzer ◽

...

Keyword(s):

Energy Consumption ◽

Air Quality ◽

Emission Inventory ◽

Emission Factors ◽

Cloud Microphysics ◽

Point Sources ◽

Air Pollutant ◽

Pollutant Emissions ◽

Environmental Research ◽

Straw Burning

Abstract. Huabei, located between 32° N and 42° N, is part of eastern China and includes administratively the Beijing and Tianjin Municipalities, Hebei and Shanxi Provinces, and Inner-Mongolia Autonomous Region. Over the past decades, the region has experienced dramatic changes in air quality and climate, and has become a major focus of environmental research in China. Here we present a new inventory of air pollutant emissions in Huabei for the year 2003 developed as part of the project Influence of Pollution on Aerosols and Cloud Microphysics in North China (IPAC-NC). Our estimates are based on data from the statistical yearbooks of the state, provinces and local districts, including major sectors and activities of power generation, industrial energy consumption, industrial processing, civil energy consumption, crop straw burning, oil and solvent evaporation, manure, and motor vehicles. The emission factors are selected from a variety of literature and those from local measurements in China are used whenever available. The estimated total emissions in the Huabei administrative region in 2003 are 4.73 Tg SO2, 2.72 Tg NOx (in equivalent NO2), 1.77 Tg VOC, 24.14 Tg CO, 2.03 Tg NH3, 4.57 Tg PM10, 2.42 Tg PM2.5, 0.21 Tg EC, and 0.46 Tg OC. For model convenience, we consider a larger Huabei region with Shandong, Henan and Liaoning Provinces included in our inventory. The estimated total emissions in the larger Huabei region in 2003 are: 9.55 Tg SO2, 5.27 Tg NOx (in equivalent NO2), 3.82 Tg VOC, 46.59 Tg CO, 5.36 Tg NH3, 10.74 Tg PM10, 5.62 Tg PM2.5, 0.41 Tg EC, and 0.99 Tg OC. The estimated emission rates are projected into grid cells at a horizontal resolution of 0.1° latitude by 0.1° longitude. Our gridded emission inventory consists of area sources, which are classified into industrial, civil, traffic, and straw burning sectors, and large industrial point sources, which include 345 sets of power plants, iron and steel plants, cement plants, and chemical plants. The estimated regional NO2 emissions are about 2–3% (administrative Huabei region) or 5% (larger Huabei region) of the global anthropogenic NO2 emissions. We compare our inventory (IPAC-NC) with the global emission inventory EDGAR-CIRCE and the Asian emission inventory INTEX-B. Except for a factor of 3 lower EC emission rate in comparison with INTEX-B, the biases of the total emissions of most primary air pollutants in Huabei estimated in our inventory, with respect to EDGAR-CIRCE and INTEX-B, generally range from −30% to +40%. Large differences up to a factor of 2–3 for local emissions in some areas (e.g. Beijing and Tianjin) are found. It is recommended that the inventories based on the activity rates and emission factors for each specific year should be applied in future modeling work related to the changes in air quality and atmospheric chemistry over this region.

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An AIS-based emission inventory and the impact on air quality in Tianjin port based on localized emission factors

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.146869 ◽

2021 ◽

Vol 783 ◽

pp. 146869

Author(s):

Lei Yang ◽

Qijun Zhang ◽

Yanjie Zhang ◽

Zongyan Lv ◽

Yanan Wang ◽

...

Keyword(s):

Air Quality ◽

Emission Inventory ◽

Emission Factors ◽

The Impact

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Contiguous United States wildland fire emission estimates during 2003–2015

10.5194/essd-2018-100 ◽

2018 ◽

Author(s):

Shawn P. Urbanski ◽

Matt C. Reeves ◽

Rachel Corley ◽

Robin Silverstein ◽

Wei Min Hao

Keyword(s):

United States ◽

Air Pollution ◽

Air Quality ◽

Emission Inventory ◽

Ambient Air ◽

The United States ◽

Pollutant Emissions ◽

Burned Area ◽

Large Set ◽

Fuel Loading

Abstract. Wildfires are a major source of air pollutants in the United States. Wildfire smoke can trigger severe pollution episodes with substantial impacts on public health. In addition to acute episodes, wildfires can have a marginal effect on air quality at significant distances from the source presenting significant challenges to air regulators’ efforts to meet National Ambient Air Quality Standards. Improved emission estimates are needed to quantify the contribution of wildfires to air pollution and thereby inform decision making activities related to the control and regulation of anthropogenic air pollution sources. To address the need of air regulators and land managers for improved wildfire emission estimates we developed the Missoula Fire Lab Emission Inventory (MFLEI), a retrospective, daily wildfire emission inventory for the contiguous United States (CONUS). MFLEI was produced using multiple datasets of fire activity and burned area, a newly developed wildland fuels map and an updated emission factor database. Daily burned area is based on a combination of Monitoring Trends in Burn Severity (MTBS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) burned area and active fire detection products, incident fire perimeters, and a spatial wildfire occurrence database. The fuel type classification map is a merger of a national forest type map, produced by the USDA Forest Service (USFS) Forest Inventory and Analysis (FIA) program and the Geospatial Technology and Applications Center (GTAC), with a shrub and grassland vegetation map developed by the USFS Missoula Forestry Sciences Laboratory. Forest fuel loading is from a fuel classification developed from a large set (> 26 000 sites) of FIA surface fuel measurements. Herbaceous fuel loading is estimated using site specific parameters with normalized differenced vegetation index from MODIS. Shrub fuel loading is quantified by applying numerous allometric equations linking stand structure and composition to biomass and fuels, with the structure and composition data derived from geospatial data layers of the LANDFIRE Project. MFLEI provides estimates of CONUS daily wildfire burned area, fuel consumption, and pollutant emissions at a 250 m × 250 m resolution for 2003–2015. A spatially aggregated emission product (10 km × 10 km, 1 d) with uncertainty estimates is included to provide a representation of emission uncertainties at a spatial scale pertinent to air quality modelling. MFLEI will be updated, with recent years, as the MTBS burned area product becomes available. The data associated with this article can be found at https://doi.org/10.2737/RDS-2017-0039.

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Elevated 3D structures of PM<sub>2.5</sub> and impact of complex terrain-forcing circulations on heavy haze pollution over Sichuan Basin, China

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-9253-2021 ◽

2021 ◽

Vol 21 (11) ◽

pp. 9253-9268

Author(s):

Zhuozhi Shu ◽

Yubao Liu ◽

Tianliang Zhao ◽

Junrong Xia ◽

Chenggang Wang ◽

...

Keyword(s):

Air Pollution ◽

Sichuan Basin ◽

Southwest China ◽

Pollutant Emissions ◽

Atmospheric Environment ◽

Observation Data ◽

Free Troposphere ◽

Near Surface ◽

Haze Pollution ◽

The Impact

Abstract. Deep basins create uniquely favorable conditions for causing air pollution, and the Sichuan Basin (SCB) in Southwest China is such a basin featuring frequent heavy pollution. A wintertime heavy haze pollution event in the SCB was studied with conventional and intensive observation data and the WRF-Chem model to explore the 3D distribution of PM2.5 to understand the impact of regional pollutant emissions, basin circulations associated with plateaus, and downwind transport to the adjacent areas. It was found that the vertical structure of PM2.5 over the SCB was characterized by a remarkable hollow sandwiched by high PM2.5 layers at heights of 1.5–3 km and a highly polluted near-surface layer. The southwesterlies over the Tibetan Plateau (TP) and Yunnan-Guizhou Plateau (YGP) resulted in a lee vortex over the SCB, which helped form and maintain heavy PM2.5 pollution. The basin PM2.5 was lifted into the free troposphere and transported outside of the SCB. At the bottom of the SCB, high PM2.5 concentrations were mostly located in the northwestern and southern regions. Due to the blocking effect of the plateau terrain on the northeasterly winds, PM2.5 gradually increased from northeast to southwest in the basin. In the lower free troposphere, the high PM2.5 centers were distributed over the northwestern and southwestern SCB areas, as well as the central SCB region. For this event, the regional emissions from the SCB contributed 75.4 %–94.6 % to the surface PM2.5 concentrations in the SCB. The SCB emissions were the major source of PM2.5 over the eastern regions of the TP and the northern regions of the YGP, with contribution rates of 72.7 % and 70.5 %, respectively, during the dissipation stage of heavy air pollution over the SCB, which was regarded as the major pollutant source affecting atmospheric environment changes in Southwest China.

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Development of a unit-based industrial emission inventory in the Beijing-Tianjin-Hebei region and resulting improvement in air quality modeling

10.5194/acp-2018-858 ◽

2018 ◽

Author(s):

Haotian Zheng ◽

Siyi Cai ◽

Shuxiao Wang ◽

Bin Zhao ◽

Xing Chang ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Pollution Control ◽

Emission Inventory ◽

Ferrous Metal ◽

Pollutant Emissions ◽

Air Pollution Control ◽

Industrial Enterprises ◽

Industrial Sectors ◽

Bth Region

Abstract. The Beijing-Tianjin-Hebei (BTH) region is a metropolitan area with the most severe fine particle (PM2.5) pollution in China. Accurate emission inventory plays an important role in air pollution control policy making. In this study, we develop a unit-based emission inventory for industrial sectors in the BTH region, including power plants, industrial boilers, and steel, non-ferrous metal, coking, cement, glass, brick, lime, ceramics, refinery, and chemical industries, based on detailed information for each enterprise, such as location, annual production, production technology/process and air pollution control facilities. In the BTH region, the emissions of sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter with diameter less than 10 μm (PM10), PM2.5, black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) from industrial sectors are 869 kt, 1164 kt, 910 kt, 622 kt, 71 kt, 63 kt and 1390 kt in 2014, respectively, accounting for 61 %, 55 %, 62 %, 56 %, 58 %, 22 % and 36 %, respectively, of the total emissions. Compared with the traditional proxy-based emission inventory, much less emissions in the high-resolution unit-based inventory are allocated to the urban center because of the accurate positioning of industrial enterprises. We apply the Community Multi-scale Air Quality (CMAQ) model simulation to evaluate the unit-based inventory. The simulation results show that the unit-based emission inventory gives better performance of both PM2.5 and gaseous pollutants than the proxy-based emission inventory. The normalized mean biases (NMBs) are 81 %, 21 %, 1 % and −7 % for concentrations of SO2, NO2, ozone and PM2.5, respectively, with the unit-based inventory, in contrast to 124 %, 39 %, −8 % and 9 % with the proxy-based inventory. Furthermore, the concentration gradients of PM2.5, which are defined as the ratio of urban concentration to suburban concentration, are 1.6, 2.1 and 1.5 in January and 1.3, 1.5 and 1.3 in July, for simulations with the unit-based inventory, simulations with the proxy-based inventory, and observations, respectively, in Beijing. For ozone, the corresponding gradients are 0.7, 0.5 and 0.9 in January and 0.9, 0.8 and 1.1 in July, implying that the unit-based emission inventory better reproduces the distributions of pollutant emissions between the urban and suburban areas.

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