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.

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

2017 ◽  
Vol 17 (10) ◽  
pp. 6393-6421 ◽  
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.

scholarly journals Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area

2020 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Kirsten Warrach-Sagi ◽  
Thomas Bönisch ◽  
Volker Wulfmeyer
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Urban Areas ◽  
Single Layer ◽  
Urban Canopy ◽  
Surface Fluxes ◽  
Residential Areas ◽  
Near Surface ◽  
Air Quality Forecast

Abstract. Air pollution is one of the major challenges in urban areas. It can have a major impact on human health and society and is currently a subject of several litigations at European courts. Information on the level of air pollution is based on near surface measurements, which are often irregularly distributed along the main traffic roads and provide almost no information about the residential areas and office districts in the cities. To further enhance the process understanding and give scientific support to decision makers, we developed a prototype for an air quality forecasting system (AQFS) within the EU demonstration project Open Forecast. For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF-Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a turbulence permitting resolution of 50 m were used and a single layer urban canopy model was active in all domains. As demonstration case study the 21 January 2019 was selected which was a heavy polluted day with observed PM10 concentrations exceeding 50 µg m−3. Our results show that the model is capable to reasonably simulate the diurnal cycle of surface fluxes and 2-m temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in Stuttgart. The simulated fields of particulates with a diameter of less than 10 µm (PM10) and Nitrogen dioxide (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, AQFS will serve as a valuable tool for air quality forecast and has the potential of being applied to other cities around the world.

scholarly journals Correlation between Air Quality and Wastewater Pollution

2021 ◽  
Author(s):  
Karzan Mohammed Khalid
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Wastewater Treatment Plants ◽  
Pollutant Emissions ◽  
Negative Effects ◽  
Environmental Technology ◽  
Wastewater Pollution ◽  
Pollutant Sources ◽  
Living Organisms

Recently, air pollution is a universal problematic concern which adversely affects global warming and more importantly human body systems. This chapter focuses on the importance of air quality, and indicates the negative effects of emissions originated from both municipal and industrial wastewaters to atmosphere. More importantly, the improvements in wastewater treatment plants to eliminate the crisis of emissions on environment and human health is also clarified. Urbanization and distribution of industrials in urban areas influence the air pollution via releasing pollutants and contaminants to environment. The pollutant emissions from wastewaters are volatile organic compounds, Greenhouse gases and other inorganic pollutants (heavy metals) which are causes to many reactions through atmosphere, then products detriment whole environment and living organisms including human. Moreover, contaminants are also released into air from influents of municipal wastewaters and they are considered as the main resources of most threatened infections in human and other animals. As conclusion, because of the persistently development urbanization and industrialization as the wastewater pollutant sources, the environmental technology regarding wastewater treatments must depend on prevention of emissions to air before thinking on cost and good quality effluents.

scholarly journals Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area

2021 ◽  
Vol 21 (6) ◽  
pp. 4575-4597
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Kirsten Warrach-Sagi ◽  
Thomas Bönisch ◽  
Volker Wulfmeyer
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Urban Areas ◽  
Single Layer ◽  
Urban Canopy ◽  
Surface Fluxes ◽  
Residential Areas ◽  
Near Surface ◽  
Air Quality Forecasting

Abstract. Air pollution is one of the major challenges in urban areas. It can have a major impact on human health and society and is currently a subject of several litigations in European courts. Information on the level of air pollution is based on near-surface measurements, which are often irregularly distributed along the main traffic roads and provide almost no information about the residential areas and office districts in the cities. To further enhance the process understanding and give scientific support to decision makers, we developed a prototype for an air quality forecasting system (AQFS) within the EU demonstration project “Open Forecast”. For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF-Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a turbulence-permitting resolution of 50 m were used, and a single-layer urban canopy model was active in all domains. As a demonstration case study, 21 January 2019 was selected, which was a heavily polluted day with observed PM10 concentrations exceeding 50 µg m−3. Our results show that the model is able to reasonably simulate the diurnal cycle of surface fluxes and 2 m temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in Stuttgart. The simulated fields of particulates with a diameter of less than 10 µm (PM10) and nitrogen dioxide (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, AQFS will serve as a valuable tool for air quality forecasting and has the potential of being applied to other cities around the world.

scholarly journals Contiguous United States wildland fire emission estimates during 2003–2015

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.

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

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.

scholarly journals Contiguous United States wildland fire emission estimates during 2003–2015

2018 ◽  
Vol 10 (4) ◽  
pp. 2241-2274 ◽  
Author(s):  
Shawn P. Urbanski ◽  
Matt C. Reeves ◽  
Rachel E. Corley ◽  
Robin P. 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 the Normalized Difference 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 day) with uncertainty estimates is included to provide a representation of emission uncertainties at a spatial scale pertinent to air quality modeling. 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 (Urbanski et al., 2017).

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).

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