The Experts below are selected from a list of 21285 Experts worldwide ranked by ideXlab platform
Ying Zhou - One of the best experts on this subject based on the ideXlab platform.
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a high temporal spatial resolution air pollutant Emission Inventory for agricultural machinery in china
Journal of Cleaner Production, 2018Co-Authors: Jianlei Lang, Jingjing Tian, Ying Zhou, Dongsheng Chen, Qing Huang, Xiaofan Xing, Yanyun Zhang, Shuiyuan ChengAbstract:Abstract Agricultural machinery is an important non-road mobile source, which can exhaust multi-pollutants, making primary and secondary contributions to the air pollution. China is a significant agricultural country of the world; however, the agricultural machinery Emissions research is at an early stage, and an Emission Inventory with a high temporal-spatial resolution is still needed. In this study, a comprehensive Emission Inventory with a high temporal-spatial resolution for agricultural machinery in China was first developed. The results showed that the total Emissions in 2014 were 262.69 Gg, 249.25 Gg, 1211.39 Gg, 2192.05 Gg, 1448.16 Gg and 25.14 Gg for PM10, PM2.5, THC, NOx, CO and SO2, respectively. Tractors and farm transport vehicles were the top two greatest contributors, accounting for approximately 39.9%-53.6% and 17.4%-24.6%, respectively, of the total Emissions of the five pollutants (except THC). The farm transport vehicles contributed the most (81.8%) to the THC Emissions. The county-level Emissions were further allocated into 1 km × 1 km grids according to source-specific allocation surrogates. The spatial characteristic analysis indicated that high Emissions were distributed in northeast, north and central-south China. To obtain a high temporal resolution Emission Inventory, a comprehensive investigation on the agricultural practice timing in different provinces was conducted. Then, the annual Emissions in the different provinces were distributed to a spatial resolution of ten-day periods (i.e. the early, mid- and late ten-day periods in each month). It was found that higher Emissions in China occurred in late April, mid-June and early October. In addition, the Emission uncertainty was also analyzed based on the Monte Carlo simulation. The estimated high temporal-spatial resolution Emission Inventory could provide important basic information for environmental/climate implications research, Emission control policy making, and air quality modeling.
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High-spatiotemporal-resolution ship Emission Inventory of China based on AIS data in 2014.
The Science of the total environment, 2017Co-Authors: Dongsheng Chen, Jianlei Lang, Ying Zhou, Xiaotong Wang, Xiurui Guo, Yuehua ZhaoAbstract:Ship exhaust Emissions have been considered a significant source of air pollution, with adverse impacts on the global climate and human health. China, as one of the largest shipping countries, has long been in great need of in-depth analysis of ship Emissions. This study for the first time developed a comprehensive national-scale ship Emission Inventory with 0.005°×0.005° resolution in China for 2014, using the bottom-up method based on Automatic Identification System (AIS) data of the full year of 2014. The Emission estimation involved 166,546 unique vessels observed from over 15billion AIS reports, covering OGVs (ocean-going vessels), CVs (coastal vessels) and RVs (river vessels). Results show that the total estimated ship Emissions for China in 2014 were 1.1937×106t (SO2), 2.2084×106t (NOX), 1.807×105t (PM10), 1.665×105t (PM2.5), 1.116×105t (HC), 2.419×105t (CO), and 7.843×107t (CO2, excluding RVs), respectively. OGVs were the main Emission contributors, with proportions of 47%-74% of the Emission totals for different species. Vessel type with the most Emissions was container (~43.6%), followed by bulk carrier (~17.5%), oil tanker (~5.7%) and fishing ship (~4.9%). Monthly variations showed that Emissions from transport vessels had a low point in February, while fishing ship presented two Emission peaks in May and September. In terms of port clusters, ship Emissions in BSA (Bohai Sea Area), YRD (Yangtze River Delta) and PRD (Pearl River Delta) accounted for ~13%, ~28% and ~17%, respectively, of the total Emissions in China. On the contrast, the average Emission intensities in PRD were the highest, followed by the YRD and BSA regions. The establishment of this high-spatiotemporal-resolution ship Emission Inventory fills the gap of national-scale ship Emission Inventory of China, and the corresponding ship Emission characteristics are expected to provide certain reference significance for the management and control of the ship Emissions.
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a comprehensive biomass burning Emission Inventory with high spatial and temporal resolution in china
Atmospheric Chemistry and Physics, 2016Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Xiaofan Xing, Shuiyuan Cheng, Lin Wei, Xiao Wei, Chao LiuAbstract:Abstract. Biomass burning injects many different gases and aerosols into the atmosphere that could have a harmful effect on air quality, climate, and human health. In this study, a comprehensive biomass burning Emission Inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is presented, which was developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific Emission factors (EFs). The Emission Inventory within a 1 × 1 km2 grid was generated using geographical information system (GIS) technology according to source-based spatial surrogates. A range of key information related to Emission estimation (e.g. province-specific proportion of domestic and in-field straw burning, detailed firewood burning quantities, uneven temporal distribution coefficient) was obtained from field investigation, systematic combing of the latest research, and regression analysis of statistical data. The established Emission Inventory includes the major precursors of complex pollution, greenhouse gases, and heavy metal released from biomass burning. The results show that the Emissions of SO2, NOx, PM10, PM2.5, NMVOC, NH3, CO, EC, OC, CO2, CH4, and Hg in 2012 are 336.8 Gg, 990.7 Gg, 3728.3 Gg, 3526.7 Gg, 3474.2 Gg, 401.2 Gg, 34 380.4 Gg, 369.7 Gg, 1189.5 Gg, 675 299.0 Gg, 2092.4 Gg, and 4.12 Mg, respectively. Domestic straw burning, in-field straw burning, and firewood burning are identified as the dominant biomass burning sources. The largest contributing source is different for various pollutants. Domestic straw burning is the largest source of biomass burning Emissions for all the pollutants considered, except for NH3, EC (firewood), and NOx (in-field straw). Corn, rice, and wheat represent the major crop straws. The combined Emission of these three straw types accounts for 80 % of the total straw-burned Emissions for each specific pollutant mentioned in this study. As for the straw burning Emission of various crops, corn straw burning has the largest contribution to all of the pollutants considered, except for CH4; rice straw burning has highest contribution to CH4 and the second largest contribution to other pollutants, except for SO2, OC, and Hg; wheat straw burning is the second largest contributor to SO2, OC, and Hg and the third largest contributor to other pollutants. Heilongjiang, Shandong, and Henan provinces located in the north-eastern and central-southern regions of China have higher Emissions compared to other provinces in China. Gridded Emissions, which were obtained through spatial allocation based on the gridded rural population and fire point data from Emission inventories at county resolution, could better represent the actual situation. High biomass burning Emissions are concentrated in the areas with more agricultural and rural activity. The months of April, May, June, and October account for 65 % of Emissions from in-field crop residue burning, while, regarding EC, the Emissions in January, February, October, November, and December are relatively higher than other months due to biomass domestic burning in heating season. There are regional differences in the monthly variations of Emissions due to the diversity of main planted crops and climatic conditions. Furthermore, PM2.5 component results showed that OC, Cl−, EC, K+, NH4+, elemental K, and SO42− are the main PM2.5 species, accounting for 80 % of the total Emissions. The species with relatively high contribution to NMVOC Emission include ethylene, propylene, toluene, mp-xylene, and ethyl benzene, which are key species for the formation of secondary air pollution. The detailed biomass burning Emission Inventory developed by this study could provide useful information for air-quality modelling and could support the development of appropriate pollution-control strategies.
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a comprehensive ammonia Emission Inventory with high resolution and its evaluation in the beijing tianjin hebei bth region china
Atmospheric Environment, 2015Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Beibei Zhao, Chao LiuAbstract:Abstract A comprehensive ammonia (NH3) Emission Inventory for the Beijing–Tianjin–Hebei (BTH) region was developed based on the updated source-specific Emission factors (EFs) and the county-level activity data obtained from a full-coverage investigation launched in the BTH region for the first time. The NH3 Emission Inventory within 1 km × 1 km grid was generated using source-based spatial surrogates with geographical information system (GIS) technology. The total NH3 Emission was 1573.7 Gg for the year 2010. The contributions from livestock, farmland, human, biomass burning, chemical industry, fuel combustion, waste disposal and on-road mobile source were approximately 56.6%, 28.6%, 7.2%, 3.4%, 1.1%, 1.3%, 1.0% and 0.8%, respectively. Among different cities, Shijiazhang, Handan, Xingtai, Tangshan and Cangzhou had higher NH3 Emissions. Statistical analysis aiming at county-level Emission of 180 counties in BTH indicated that the NH3 Emission in most of the counties were less than 16 Gg. The maximum value of the county level Emission was approximately 25.5 Gg. Higher NH3 Emission was concentrated in the areas with more rural and agricultural activity. Monthly, higher NH3 Emission occurred during the period from April to September, which could be attributed to the temperature and timing of planting practice. The validity of the estimated Emissions were further evaluated from multiple perspectives covering (1) uncertainty analysis based on Monte Carlo simulation, (2) comparison with other studies, (3) quantitative analysis of improvement in spatial resolution of activity data, and (4) verification based on a comparison of the simulated and observed surface concentrations of ammonium. The detailed and validated ammonia Emission Inventory could provide valuable information for understanding air pollution formation mechanisms and help guide decision-making with respect to control strategies.
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a new statistical approach for establishing high resolution Emission Inventory of primary gaseous air pollutants
Atmospheric Environment, 2014Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Beibei Zhao, Wei WeiAbstract:Abstract This paper, which aims at the primary gaseous air pollutants (i.e., SO2, NOx, VOCS and CO), is the third paper in the series papers published in Atmospheric Environment to develop new Emission estimation models by the regression method. A group of regression models for various industrial and non-industrial sectors were proposed based on an Emission investigation case study of Handan region in northern China. The main data requirements of the regression models for industrial sectors were coal consumption, oil consumption, gaseous fuel consumption and annual industrial output. The data requirements for non-industrial sector Emission estimations were the population, the number of resident population households, the vehicle population, the area of construction sites, the forestland area, and the orchard area. The models were then applied to Tangshan region in northern China. The results showed that the developed regression models had relatively satisfactory performance. The modeling errors at the regional level for SO2, NOx, VOCS and CO were −16.5%, −10.6%, −11.8% and −22.6%, respectively. The corresponding modeling errors at the county level were 39.9%, 33.9%, 46.3% and 46.9%, respectively. The models were also applied to other regions in northern China. The results revealed that the new models could develop Emission inventories with generally lower error than found in previous Emission Inventory studies. The developed models had the advantages of only using publicly available statistical information for developing high-accuracy and high-resolution Emission Inventory, without requiring detailed data investigation which is necessary by conventional “bottom-up” Emission Inventory development approach.
Jianlei Lang - One of the best experts on this subject based on the ideXlab platform.
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a high temporal spatial resolution air pollutant Emission Inventory for agricultural machinery in china
Journal of Cleaner Production, 2018Co-Authors: Jianlei Lang, Jingjing Tian, Ying Zhou, Dongsheng Chen, Qing Huang, Xiaofan Xing, Yanyun Zhang, Shuiyuan ChengAbstract:Abstract Agricultural machinery is an important non-road mobile source, which can exhaust multi-pollutants, making primary and secondary contributions to the air pollution. China is a significant agricultural country of the world; however, the agricultural machinery Emissions research is at an early stage, and an Emission Inventory with a high temporal-spatial resolution is still needed. In this study, a comprehensive Emission Inventory with a high temporal-spatial resolution for agricultural machinery in China was first developed. The results showed that the total Emissions in 2014 were 262.69 Gg, 249.25 Gg, 1211.39 Gg, 2192.05 Gg, 1448.16 Gg and 25.14 Gg for PM10, PM2.5, THC, NOx, CO and SO2, respectively. Tractors and farm transport vehicles were the top two greatest contributors, accounting for approximately 39.9%-53.6% and 17.4%-24.6%, respectively, of the total Emissions of the five pollutants (except THC). The farm transport vehicles contributed the most (81.8%) to the THC Emissions. The county-level Emissions were further allocated into 1 km × 1 km grids according to source-specific allocation surrogates. The spatial characteristic analysis indicated that high Emissions were distributed in northeast, north and central-south China. To obtain a high temporal resolution Emission Inventory, a comprehensive investigation on the agricultural practice timing in different provinces was conducted. Then, the annual Emissions in the different provinces were distributed to a spatial resolution of ten-day periods (i.e. the early, mid- and late ten-day periods in each month). It was found that higher Emissions in China occurred in late April, mid-June and early October. In addition, the Emission uncertainty was also analyzed based on the Monte Carlo simulation. The estimated high temporal-spatial resolution Emission Inventory could provide important basic information for environmental/climate implications research, Emission control policy making, and air quality modeling.
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High-spatiotemporal-resolution ship Emission Inventory of China based on AIS data in 2014.
The Science of the total environment, 2017Co-Authors: Dongsheng Chen, Jianlei Lang, Ying Zhou, Xiaotong Wang, Xiurui Guo, Yuehua ZhaoAbstract:Ship exhaust Emissions have been considered a significant source of air pollution, with adverse impacts on the global climate and human health. China, as one of the largest shipping countries, has long been in great need of in-depth analysis of ship Emissions. This study for the first time developed a comprehensive national-scale ship Emission Inventory with 0.005°×0.005° resolution in China for 2014, using the bottom-up method based on Automatic Identification System (AIS) data of the full year of 2014. The Emission estimation involved 166,546 unique vessels observed from over 15billion AIS reports, covering OGVs (ocean-going vessels), CVs (coastal vessels) and RVs (river vessels). Results show that the total estimated ship Emissions for China in 2014 were 1.1937×106t (SO2), 2.2084×106t (NOX), 1.807×105t (PM10), 1.665×105t (PM2.5), 1.116×105t (HC), 2.419×105t (CO), and 7.843×107t (CO2, excluding RVs), respectively. OGVs were the main Emission contributors, with proportions of 47%-74% of the Emission totals for different species. Vessel type with the most Emissions was container (~43.6%), followed by bulk carrier (~17.5%), oil tanker (~5.7%) and fishing ship (~4.9%). Monthly variations showed that Emissions from transport vessels had a low point in February, while fishing ship presented two Emission peaks in May and September. In terms of port clusters, ship Emissions in BSA (Bohai Sea Area), YRD (Yangtze River Delta) and PRD (Pearl River Delta) accounted for ~13%, ~28% and ~17%, respectively, of the total Emissions in China. On the contrast, the average Emission intensities in PRD were the highest, followed by the YRD and BSA regions. The establishment of this high-spatiotemporal-resolution ship Emission Inventory fills the gap of national-scale ship Emission Inventory of China, and the corresponding ship Emission characteristics are expected to provide certain reference significance for the management and control of the ship Emissions.
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a comprehensive biomass burning Emission Inventory with high spatial and temporal resolution in china
Atmospheric Chemistry and Physics, 2016Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Xiaofan Xing, Shuiyuan Cheng, Lin Wei, Xiao Wei, Chao LiuAbstract:Abstract. Biomass burning injects many different gases and aerosols into the atmosphere that could have a harmful effect on air quality, climate, and human health. In this study, a comprehensive biomass burning Emission Inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is presented, which was developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific Emission factors (EFs). The Emission Inventory within a 1 × 1 km2 grid was generated using geographical information system (GIS) technology according to source-based spatial surrogates. A range of key information related to Emission estimation (e.g. province-specific proportion of domestic and in-field straw burning, detailed firewood burning quantities, uneven temporal distribution coefficient) was obtained from field investigation, systematic combing of the latest research, and regression analysis of statistical data. The established Emission Inventory includes the major precursors of complex pollution, greenhouse gases, and heavy metal released from biomass burning. The results show that the Emissions of SO2, NOx, PM10, PM2.5, NMVOC, NH3, CO, EC, OC, CO2, CH4, and Hg in 2012 are 336.8 Gg, 990.7 Gg, 3728.3 Gg, 3526.7 Gg, 3474.2 Gg, 401.2 Gg, 34 380.4 Gg, 369.7 Gg, 1189.5 Gg, 675 299.0 Gg, 2092.4 Gg, and 4.12 Mg, respectively. Domestic straw burning, in-field straw burning, and firewood burning are identified as the dominant biomass burning sources. The largest contributing source is different for various pollutants. Domestic straw burning is the largest source of biomass burning Emissions for all the pollutants considered, except for NH3, EC (firewood), and NOx (in-field straw). Corn, rice, and wheat represent the major crop straws. The combined Emission of these three straw types accounts for 80 % of the total straw-burned Emissions for each specific pollutant mentioned in this study. As for the straw burning Emission of various crops, corn straw burning has the largest contribution to all of the pollutants considered, except for CH4; rice straw burning has highest contribution to CH4 and the second largest contribution to other pollutants, except for SO2, OC, and Hg; wheat straw burning is the second largest contributor to SO2, OC, and Hg and the third largest contributor to other pollutants. Heilongjiang, Shandong, and Henan provinces located in the north-eastern and central-southern regions of China have higher Emissions compared to other provinces in China. Gridded Emissions, which were obtained through spatial allocation based on the gridded rural population and fire point data from Emission inventories at county resolution, could better represent the actual situation. High biomass burning Emissions are concentrated in the areas with more agricultural and rural activity. The months of April, May, June, and October account for 65 % of Emissions from in-field crop residue burning, while, regarding EC, the Emissions in January, February, October, November, and December are relatively higher than other months due to biomass domestic burning in heating season. There are regional differences in the monthly variations of Emissions due to the diversity of main planted crops and climatic conditions. Furthermore, PM2.5 component results showed that OC, Cl−, EC, K+, NH4+, elemental K, and SO42− are the main PM2.5 species, accounting for 80 % of the total Emissions. The species with relatively high contribution to NMVOC Emission include ethylene, propylene, toluene, mp-xylene, and ethyl benzene, which are key species for the formation of secondary air pollution. The detailed biomass burning Emission Inventory developed by this study could provide useful information for air-quality modelling and could support the development of appropriate pollution-control strategies.
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a comprehensive ammonia Emission Inventory with high resolution and its evaluation in the beijing tianjin hebei bth region china
Atmospheric Environment, 2015Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Beibei Zhao, Chao LiuAbstract:Abstract A comprehensive ammonia (NH3) Emission Inventory for the Beijing–Tianjin–Hebei (BTH) region was developed based on the updated source-specific Emission factors (EFs) and the county-level activity data obtained from a full-coverage investigation launched in the BTH region for the first time. The NH3 Emission Inventory within 1 km × 1 km grid was generated using source-based spatial surrogates with geographical information system (GIS) technology. The total NH3 Emission was 1573.7 Gg for the year 2010. The contributions from livestock, farmland, human, biomass burning, chemical industry, fuel combustion, waste disposal and on-road mobile source were approximately 56.6%, 28.6%, 7.2%, 3.4%, 1.1%, 1.3%, 1.0% and 0.8%, respectively. Among different cities, Shijiazhang, Handan, Xingtai, Tangshan and Cangzhou had higher NH3 Emissions. Statistical analysis aiming at county-level Emission of 180 counties in BTH indicated that the NH3 Emission in most of the counties were less than 16 Gg. The maximum value of the county level Emission was approximately 25.5 Gg. Higher NH3 Emission was concentrated in the areas with more rural and agricultural activity. Monthly, higher NH3 Emission occurred during the period from April to September, which could be attributed to the temperature and timing of planting practice. The validity of the estimated Emissions were further evaluated from multiple perspectives covering (1) uncertainty analysis based on Monte Carlo simulation, (2) comparison with other studies, (3) quantitative analysis of improvement in spatial resolution of activity data, and (4) verification based on a comparison of the simulated and observed surface concentrations of ammonium. The detailed and validated ammonia Emission Inventory could provide valuable information for understanding air pollution formation mechanisms and help guide decision-making with respect to control strategies.
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a new statistical approach for establishing high resolution Emission Inventory of primary gaseous air pollutants
Atmospheric Environment, 2014Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Beibei Zhao, Wei WeiAbstract:Abstract This paper, which aims at the primary gaseous air pollutants (i.e., SO2, NOx, VOCS and CO), is the third paper in the series papers published in Atmospheric Environment to develop new Emission estimation models by the regression method. A group of regression models for various industrial and non-industrial sectors were proposed based on an Emission investigation case study of Handan region in northern China. The main data requirements of the regression models for industrial sectors were coal consumption, oil consumption, gaseous fuel consumption and annual industrial output. The data requirements for non-industrial sector Emission estimations were the population, the number of resident population households, the vehicle population, the area of construction sites, the forestland area, and the orchard area. The models were then applied to Tangshan region in northern China. The results showed that the developed regression models had relatively satisfactory performance. The modeling errors at the regional level for SO2, NOx, VOCS and CO were −16.5%, −10.6%, −11.8% and −22.6%, respectively. The corresponding modeling errors at the county level were 39.9%, 33.9%, 46.3% and 46.9%, respectively. The models were also applied to other regions in northern China. The results revealed that the new models could develop Emission inventories with generally lower error than found in previous Emission Inventory studies. The developed models had the advantages of only using publicly available statistical information for developing high-accuracy and high-resolution Emission Inventory, without requiring detailed data investigation which is necessary by conventional “bottom-up” Emission Inventory development approach.
Dongsheng Chen - One of the best experts on this subject based on the ideXlab platform.
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a high temporal spatial resolution air pollutant Emission Inventory for agricultural machinery in china
Journal of Cleaner Production, 2018Co-Authors: Jianlei Lang, Jingjing Tian, Ying Zhou, Dongsheng Chen, Qing Huang, Xiaofan Xing, Yanyun Zhang, Shuiyuan ChengAbstract:Abstract Agricultural machinery is an important non-road mobile source, which can exhaust multi-pollutants, making primary and secondary contributions to the air pollution. China is a significant agricultural country of the world; however, the agricultural machinery Emissions research is at an early stage, and an Emission Inventory with a high temporal-spatial resolution is still needed. In this study, a comprehensive Emission Inventory with a high temporal-spatial resolution for agricultural machinery in China was first developed. The results showed that the total Emissions in 2014 were 262.69 Gg, 249.25 Gg, 1211.39 Gg, 2192.05 Gg, 1448.16 Gg and 25.14 Gg for PM10, PM2.5, THC, NOx, CO and SO2, respectively. Tractors and farm transport vehicles were the top two greatest contributors, accounting for approximately 39.9%-53.6% and 17.4%-24.6%, respectively, of the total Emissions of the five pollutants (except THC). The farm transport vehicles contributed the most (81.8%) to the THC Emissions. The county-level Emissions were further allocated into 1 km × 1 km grids according to source-specific allocation surrogates. The spatial characteristic analysis indicated that high Emissions were distributed in northeast, north and central-south China. To obtain a high temporal resolution Emission Inventory, a comprehensive investigation on the agricultural practice timing in different provinces was conducted. Then, the annual Emissions in the different provinces were distributed to a spatial resolution of ten-day periods (i.e. the early, mid- and late ten-day periods in each month). It was found that higher Emissions in China occurred in late April, mid-June and early October. In addition, the Emission uncertainty was also analyzed based on the Monte Carlo simulation. The estimated high temporal-spatial resolution Emission Inventory could provide important basic information for environmental/climate implications research, Emission control policy making, and air quality modeling.
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High-spatiotemporal-resolution ship Emission Inventory of China based on AIS data in 2014.
The Science of the total environment, 2017Co-Authors: Dongsheng Chen, Jianlei Lang, Ying Zhou, Xiaotong Wang, Xiurui Guo, Yuehua ZhaoAbstract:Ship exhaust Emissions have been considered a significant source of air pollution, with adverse impacts on the global climate and human health. China, as one of the largest shipping countries, has long been in great need of in-depth analysis of ship Emissions. This study for the first time developed a comprehensive national-scale ship Emission Inventory with 0.005°×0.005° resolution in China for 2014, using the bottom-up method based on Automatic Identification System (AIS) data of the full year of 2014. The Emission estimation involved 166,546 unique vessels observed from over 15billion AIS reports, covering OGVs (ocean-going vessels), CVs (coastal vessels) and RVs (river vessels). Results show that the total estimated ship Emissions for China in 2014 were 1.1937×106t (SO2), 2.2084×106t (NOX), 1.807×105t (PM10), 1.665×105t (PM2.5), 1.116×105t (HC), 2.419×105t (CO), and 7.843×107t (CO2, excluding RVs), respectively. OGVs were the main Emission contributors, with proportions of 47%-74% of the Emission totals for different species. Vessel type with the most Emissions was container (~43.6%), followed by bulk carrier (~17.5%), oil tanker (~5.7%) and fishing ship (~4.9%). Monthly variations showed that Emissions from transport vessels had a low point in February, while fishing ship presented two Emission peaks in May and September. In terms of port clusters, ship Emissions in BSA (Bohai Sea Area), YRD (Yangtze River Delta) and PRD (Pearl River Delta) accounted for ~13%, ~28% and ~17%, respectively, of the total Emissions in China. On the contrast, the average Emission intensities in PRD were the highest, followed by the YRD and BSA regions. The establishment of this high-spatiotemporal-resolution ship Emission Inventory fills the gap of national-scale ship Emission Inventory of China, and the corresponding ship Emission characteristics are expected to provide certain reference significance for the management and control of the ship Emissions.
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a comprehensive biomass burning Emission Inventory with high spatial and temporal resolution in china
Atmospheric Chemistry and Physics, 2016Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Xiaofan Xing, Shuiyuan Cheng, Lin Wei, Xiao Wei, Chao LiuAbstract:Abstract. Biomass burning injects many different gases and aerosols into the atmosphere that could have a harmful effect on air quality, climate, and human health. In this study, a comprehensive biomass burning Emission Inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is presented, which was developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific Emission factors (EFs). The Emission Inventory within a 1 × 1 km2 grid was generated using geographical information system (GIS) technology according to source-based spatial surrogates. A range of key information related to Emission estimation (e.g. province-specific proportion of domestic and in-field straw burning, detailed firewood burning quantities, uneven temporal distribution coefficient) was obtained from field investigation, systematic combing of the latest research, and regression analysis of statistical data. The established Emission Inventory includes the major precursors of complex pollution, greenhouse gases, and heavy metal released from biomass burning. The results show that the Emissions of SO2, NOx, PM10, PM2.5, NMVOC, NH3, CO, EC, OC, CO2, CH4, and Hg in 2012 are 336.8 Gg, 990.7 Gg, 3728.3 Gg, 3526.7 Gg, 3474.2 Gg, 401.2 Gg, 34 380.4 Gg, 369.7 Gg, 1189.5 Gg, 675 299.0 Gg, 2092.4 Gg, and 4.12 Mg, respectively. Domestic straw burning, in-field straw burning, and firewood burning are identified as the dominant biomass burning sources. The largest contributing source is different for various pollutants. Domestic straw burning is the largest source of biomass burning Emissions for all the pollutants considered, except for NH3, EC (firewood), and NOx (in-field straw). Corn, rice, and wheat represent the major crop straws. The combined Emission of these three straw types accounts for 80 % of the total straw-burned Emissions for each specific pollutant mentioned in this study. As for the straw burning Emission of various crops, corn straw burning has the largest contribution to all of the pollutants considered, except for CH4; rice straw burning has highest contribution to CH4 and the second largest contribution to other pollutants, except for SO2, OC, and Hg; wheat straw burning is the second largest contributor to SO2, OC, and Hg and the third largest contributor to other pollutants. Heilongjiang, Shandong, and Henan provinces located in the north-eastern and central-southern regions of China have higher Emissions compared to other provinces in China. Gridded Emissions, which were obtained through spatial allocation based on the gridded rural population and fire point data from Emission inventories at county resolution, could better represent the actual situation. High biomass burning Emissions are concentrated in the areas with more agricultural and rural activity. The months of April, May, June, and October account for 65 % of Emissions from in-field crop residue burning, while, regarding EC, the Emissions in January, February, October, November, and December are relatively higher than other months due to biomass domestic burning in heating season. There are regional differences in the monthly variations of Emissions due to the diversity of main planted crops and climatic conditions. Furthermore, PM2.5 component results showed that OC, Cl−, EC, K+, NH4+, elemental K, and SO42− are the main PM2.5 species, accounting for 80 % of the total Emissions. The species with relatively high contribution to NMVOC Emission include ethylene, propylene, toluene, mp-xylene, and ethyl benzene, which are key species for the formation of secondary air pollution. The detailed biomass burning Emission Inventory developed by this study could provide useful information for air-quality modelling and could support the development of appropriate pollution-control strategies.
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a comprehensive ammonia Emission Inventory with high resolution and its evaluation in the beijing tianjin hebei bth region china
Atmospheric Environment, 2015Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Beibei Zhao, Chao LiuAbstract:Abstract A comprehensive ammonia (NH3) Emission Inventory for the Beijing–Tianjin–Hebei (BTH) region was developed based on the updated source-specific Emission factors (EFs) and the county-level activity data obtained from a full-coverage investigation launched in the BTH region for the first time. The NH3 Emission Inventory within 1 km × 1 km grid was generated using source-based spatial surrogates with geographical information system (GIS) technology. The total NH3 Emission was 1573.7 Gg for the year 2010. The contributions from livestock, farmland, human, biomass burning, chemical industry, fuel combustion, waste disposal and on-road mobile source were approximately 56.6%, 28.6%, 7.2%, 3.4%, 1.1%, 1.3%, 1.0% and 0.8%, respectively. Among different cities, Shijiazhang, Handan, Xingtai, Tangshan and Cangzhou had higher NH3 Emissions. Statistical analysis aiming at county-level Emission of 180 counties in BTH indicated that the NH3 Emission in most of the counties were less than 16 Gg. The maximum value of the county level Emission was approximately 25.5 Gg. Higher NH3 Emission was concentrated in the areas with more rural and agricultural activity. Monthly, higher NH3 Emission occurred during the period from April to September, which could be attributed to the temperature and timing of planting practice. The validity of the estimated Emissions were further evaluated from multiple perspectives covering (1) uncertainty analysis based on Monte Carlo simulation, (2) comparison with other studies, (3) quantitative analysis of improvement in spatial resolution of activity data, and (4) verification based on a comparison of the simulated and observed surface concentrations of ammonium. The detailed and validated ammonia Emission Inventory could provide valuable information for understanding air pollution formation mechanisms and help guide decision-making with respect to control strategies.
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a new statistical approach for establishing high resolution Emission Inventory of primary gaseous air pollutants
Atmospheric Environment, 2014Co-Authors: Ying Zhou, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Beibei Zhao, Wei WeiAbstract:Abstract This paper, which aims at the primary gaseous air pollutants (i.e., SO2, NOx, VOCS and CO), is the third paper in the series papers published in Atmospheric Environment to develop new Emission estimation models by the regression method. A group of regression models for various industrial and non-industrial sectors were proposed based on an Emission investigation case study of Handan region in northern China. The main data requirements of the regression models for industrial sectors were coal consumption, oil consumption, gaseous fuel consumption and annual industrial output. The data requirements for non-industrial sector Emission estimations were the population, the number of resident population households, the vehicle population, the area of construction sites, the forestland area, and the orchard area. The models were then applied to Tangshan region in northern China. The results showed that the developed regression models had relatively satisfactory performance. The modeling errors at the regional level for SO2, NOx, VOCS and CO were −16.5%, −10.6%, −11.8% and −22.6%, respectively. The corresponding modeling errors at the county level were 39.9%, 33.9%, 46.3% and 46.9%, respectively. The models were also applied to other regions in northern China. The results revealed that the new models could develop Emission inventories with generally lower error than found in previous Emission Inventory studies. The developed models had the advantages of only using publicly available statistical information for developing high-accuracy and high-resolution Emission Inventory, without requiring detailed data investigation which is necessary by conventional “bottom-up” Emission Inventory development approach.
Ji-ming Hao - One of the best experts on this subject based on the ideXlab platform.
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global anthropogenic atmospheric Emission Inventory of twelve typical hazardous trace elements 1995 2012
Atmospheric Environment, 2020Co-Authors: He-zhong Tian, Chuanyong Zhu, Ji-ming HaoAbstract:Abstract Hazardous trace elements (HTEs) cannot be eliminated through microbial degradation in the environment, and moreover, high concentrations of HTEs do not only impose a long term and non-reversible burden on biogeochemical cycling in the ecosystem, but also threaten the health and life of animals and human beings. Taking account of the obvious differences on economic development and technology diffusion among countries, we construct the best available representation of time-varying Emission factors of HTEs for primary anthropogenic activities in five regions of the world. Anthropogenic atmospheric Emission Inventory of twelve typical HTEs (Hg, As, Se, Pb, Cd, Cr, Ni, Sb, Mn, Co, Cu and Zn) characterized by global region, long-term spans and high spatiotemporal resolutions are elaborately established by synthetically using Emission factor methods and ArcGIS tools. Our results show that the global total Emissions of twelve HTEs from primary anthropogenic sources have decreased from about 268262 tons in 1995–216893 tons in 2012, with an annual decline rate of 1%. For HTE Emissions in 2012, coal combustion sources (CC, accounting for about 35–97% of Hg, Se, Cr, Mn, Co and Zn Emissions), liquid fuel combustion (LFC, accounting for about 31% of Pb Emission), nonferrous metal smelting (NFMS, being responsible for about 51–77% of As, Cd and Ni Emissions) and brake wear (BW, contributing about 42–67% of Sb and Cu Emissions) are identified as primary contribution sources for the corresponding HTEs. Asia is the highest HTEs emitting continent in 2012, accounting for approximately 59% of the global total Emissions. Generally, China, Chile, India, Russia, the United States and South Africa represent the top countries with high HTE Emissions from anthropogenic sources. Therein, China ranks as the world's largest country with HTE (except for Sb) Emissions. In contrast, the U.S. is found as the country producing the largest Sb Emission throughout the world. The top Emission intensities of HTEs are found in Eastern and Southern Asia and Eastern Europe.
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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
Atmospheric Chemistry and Physics, 2019Co-Authors: Haotian Zheng, Siyi Cai, Shuxiao Wang, Bin Zhao, Xing Chang, Ji-ming HaoAbstract:Abstract. The Beijing–Tianjin–Hebei (BTH) region is a metropolitan area with the most severe fine particle (PM 2.5 ) pollution in China. An 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, steel, non-ferrous metal smelting, coking plants, cement, glass, brick, lime, ceramics, refineries, and chemical industries, based on detailed information for each enterprise, such as location, annual production, production technology/processes, 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 µ m (PM 10 ), PM 2.5 , black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) from industrial sectors were 869, 1164, 910, 622, 71, 63, and 1390 kt in 2014, respectively, accounting for a respective 61 %, 55 %, 62 %, 56 %, 58 %, 22 %, and 36 % 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 centers due to the accurate positioning of industrial enterprises. We apply the Community Multi-scale Air Quality (CMAQ; version 5.0.2) model simulation to evaluate the unit-based Inventory. The simulation results show that the unit-based Emission Inventory shows better performance with respect to both PM 2.5 and gaseous pollutants than the proxy-based Emission Inventory. The normalized mean biases (NMBs) are 81 %, 21 %, 1 %, and −7 % for the concentrations of SO2 , NO2 , ozone ( O3 ), and PM 2.5 , respectively, with the unit-based Inventory, in contrast to 124 %, 39 %, −8 %, and 9 % with the proxy-based Inventory; furthermore, the concentration gradients of PM 2.5 , which are defined as the ratio of the urban concentration to the 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 O3 , 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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a highly resolved mercury Emission Inventory of chinese coal fired power plants
Environmental Science & Technology, 2018Co-Authors: Kaiyun Liu, He-zhong Tian, Lei Duan, Shuxiao Wang, Long Wang, Lei Zhang, Ji-ming HaoAbstract:As the largest coal consumer in China, the coal-fired power plants have come under increasing public concern in regard to atmospheric mercury pollution. This study developed an up-to-date and high-resolution mercury Emission Inventory of Chinese coal-fired power plants using a unit-based method that combined data from individual power plants, provincial coal characteristics, and industry removal efficiencies. National mercury Emissions in 2015 were estimated at 73 tons, including 54 tons of elemental mercury, 18 tons of gaseous oxidized mercury and 1 ton of particle-bound mercury. Pulverized coal boilers emitted 65 tons, mainly in the coastal provinces and coal-electricity bases. Circulating fluidized bed boilers emitted 8 tons, mainly in Inner Mongolia and Shanxi Province. The average mercury Emission intensity over the Chinese mainland was 18.3 g/GWh, which was similar to the limit for low-rank coal-fired units in the United States. The overall uncertainty of national mercury Emission was estimated to b...
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a comprehensive Emission Inventory of multiple air pollutants from iron and steel industry in china temporal trends and spatial variation characteristics
Science of The Total Environment, 2016Co-Authors: Kun Wang, He-zhong Tian, Ji-ming Hao, Chuanyong Zhu, Shenbing Hua, Jiajia Gao, Yifeng Xue, Yong Wang, Junrui ZhouAbstract:China has become the largest producer of iron and steel throughout the world since 1996. However, as an energy-and-pollution intensive manufacturing sector, a detailed comprehensive Emission Inventory of air pollutants for iron and steel industry of China is still not available. To obtain and better understand the temporal trends and spatial variation characteristics of typical hazardous air pollutants (HAPs) Emissions from iron and steel production in China, a comprehensive Emission Inventory of multiple air pollutants, including size segregated particulate matter (TSP/PM10/PM2.5), gaseous pollutants (SO2, NOx, CO), heavy metals (Pb, Cd, Hg, As, Cr, Ni etc.), as well as the more dangerous PCDD/Fs, is established with the unit-based annual activity, specific dynamic Emission factors for the historical period of 1978-2011, and the future potential trends till to 2050 are forecasted by using scenario analysis. Our results show that Emissions of gaseous pollutants and particulate matter have experienced a gradual increase tendency since 2000, while Emissions of priority-controlled heavy metals (Hg, Pb, As, Cd, Cr, and Ni) have exhibited a short-term fluctuation during the period of 1990 to 2005. With regard to the spatial distribution of HAPs Emissions in base year 2011, Bohai economic circle is identified as the top Emission intensity region where iron and steel smelting plants are densely built; within iron and steel industry, blast furnaces contribute the majority of PM Emissions, sinter plants account for most of gaseous pollutants and the majority of PCDD/Fs, whereas steel making processes are responsible for the majority of heavy metal Emissions. Moreover, comparisons of future Emission trends under three scenarios indicate that advanced technologies and integrated whole process management strategies are in great need to further diminish various hazardous air pollutants from iron and steel industry in the future.
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Atmospheric Emission Inventory of hazardous trace elements from China's coal-fired power plants-temporal trends and spatial variation characteristics
Environmental Science and Technology, 2014Co-Authors: He-zhong Tian, Kaiyun Liu, Junrui Zhou, Ji-ming Hao, Peipei Qiu, Chuanyong Zhu, Long Lu, Jiajia Gao, Kun Wang, Shenbing HuaAbstract:Coal-fired power plants are the important sources of anthropogenic atmospheric releases of various hazardous trace elements (HTE) because a large quantity of Emissions can cause wide dispersion and possible long-distance transportation. To obtain the temporal trends and spatial variation characteristics of various HTE discharged from coal-fired power plants of China, a multiple-year comprehensive Emission Inventory of HTE including Hg, As, Se, Pb, Cd, Cr, Ni, and Sb has been established for the period 2000?2010. Thanks to the cobenefit removal effects of conventional particulate matter/sulfur dioxide/nitrogen oxides (PM/SO2/NOx) control devices, Emissions of these 8 toxic elements have shown a gradual decline since the peak in 2006. The total Emissions of Hg, As, Se, Pb, Cd, Cr, Ni, and Sb are substantial and are estimated at about 118.54, 335.45, 459.4, 705.45, 13.34, 505.03, 446.42, and 82.33 tons (t), respectively, in 2010. Shandong, Jiangsu, Shanxi, and Hebei always rank among the top ten provinces with the highest Emissions. Further, future Emissions for 2015 and 2020 are projected with scenario analysis. Advanced technologies and integrated management strategies to control HTE are in great need.
Junyu Zheng - One of the best experts on this subject based on the ideXlab platform.
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anthropogenic atmospheric toxic metals Emission Inventory and its spatial characteristics in guangdong province china
Science of The Total Environment, 2019Co-Authors: Qinge Sha, Zibing Yuan, Zhuangmin Zhong, Zhijiong Huang, Guanglin Jia, Xiao Xiao, Zhiwei Zhang, Junyu ZhengAbstract:Abstract Atmospheric toxic metals (TMs) may cause adverse effects on the environment and human health due to their bioavailability and toxicity. High-resolution TMs Emission Inventory is important input data for assessing human exposure risks, especially synergistic toxicity of multiple toxic metals. By using the latest city- and enterprise-level environment statistical data, an Emission Inventory of five TMs (Hg, As, Pb, Cd, Cr) in Guangdong province for the year of 2014 was developed using a bottom-up approach. The total Emissions of Hg, As, Pb, Cd and Cr in Guangdong were estimated as 17.70, 32.59, 411.34, 13.13, and 84.16 t, respectively. Major Emission sources for each TM were different. Hg Emissions were dominated by coal combustion (33%), fluorescent lamp (18%) and cement (17%). 78% of Hg Emissions were in the form of Hg0, 19% of Hg2+, and only 3% of Hgp due to strict particulate matter control policies. Coal combustion (48%), nonferrous metal smelting (25%) and iron and steel industry (24%) were the major sources of As. Pb Emissions primarily came from battery production (42%), iron and steel industry (21%) and gasoline combustion (17%). Cd and Cr Emissions were dominated by nonferrous metal smelting (71%) and iron and steel industry (82%), respectively. Most of these TMs were emitted in the non-Pearl River Delta region, where the newly-built iron and steel industry, nonferrous metal smelting and cement production factories were intense. The uncertainties in the five TM Emissions were high, due much to high uncertainties in TM Emission factors and limited activity data. Thus, to improve the accuracy of these estimates, we recommend more field tests of TM Emissions, especially for the industrial process sector. This study provides scientific support for formulating robust TMs control policies to alleviate the high risk of TMs exposure in Guangdong.
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a refined 2010 based voc Emission Inventory and its improvement on modeling regional ozone in the pearl river delta region china
Science of The Total Environment, 2015Co-Authors: Shasha Yin, Zibing Yuan, Zhijiong Huang, Junyu Zheng, Liuju Zhong, Hui LinAbstract:Accurate and gridded VOC Emission inventories are important for improving regional air quality model performance. In this study, a four-level VOC Emission source categorization system was proposed. A 2010-based gridded Pearl River Delta (PRD) regional VOC Emission Inventory was developed with more comprehensive source coverage, latest Emission factors, and updated activity data. The total anthropogenic VOC Emission was estimated to be about 117.4 × 10(4)t, in which on-road mobile source shared the largest contribution, followed by industrial solvent use and industrial processes sources. Among the industrial solvent use source, furniture manufacturing and shoemaking were major VOC Emission contributors. The spatial surrogates of VOC Emission were updated for major VOC sources such as industrial sectors and gas stations. Subsector-based temporal characteristics were investigated and their temporal variations were characterized. The impacts of updated VOC Emission estimates and spatial surrogates were evaluated by modeling O₃ concentration in the PRD region in the July and October of 2010, respectively. The results indicated that both updated Emission estimates and spatial allocations can effectively reduce model bias on O₃ simulation. Further efforts should be made on the refinement of source classification, comprehensive collection of activity data, and spatial-temporal surrogates in order to reduce uncertainty in Emission Inventory and improve model performance.
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anthropogenic ammonia Emission Inventory and characteristics in the pearl river delta region
Environmental Sciences, 2010Co-Authors: Shasha Yin, Junyu Zheng, Lijun Zhang, Liuju ZhongAbstract:Based on the collected activity data and Emission factors of anthropogenic ammonia sources, a 2006-based anthropogenic ammonia Emission Inventory was developed for the Pearl River Delta (PRD) region by source categories and cities with the use of appropriate estimation methods. The results show: (1) the total NH3 Emission from anthropogenic sources in the PRD region was 194. 8 kt; (2) the agriculture sources were major contributors of anthropogenic ammonia sources, in which livestock sources shared 62.1% of total NH3 Emission and the contribution of application of nitrogen fertilizers was 21.7%; (3) the broiler was the largest contributor among the livestock sources, accounting for 43.4% of the livestock Emissions, followed by the hog with a contribution of 32.1%; (4) Guangzhou was the largest ammonia Emission city in the PRD region, and then Jiangmen, accounting for 23.4% and 19.1% of total NH3 Emission in the PRD region respectively, with major sources as livestock sources and application of nitrogen fertilizers.
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development of non road mobile source Emission Inventory for the pearl river delta region
Environmental Sciences, 2010Co-Authors: Lijun Zhang, Junyu Zheng, Shasha Yin, Kang Peng, Liuju ZhongAbstract:Based on the collected activity data and Emission factors, the Pearl River Delta (PRD) regional non-road mobile source Emission Inventory was developed by categories with the use of appropriate estimation methods for different non-road mobile sources. The results show that the total Emissions of SO2, NOx,VOC, CO and PM10 from non-road mobile sources in the PRD region were about 6.52 x 10(4) t, 1.24 x 10(5) t, 4.54 x 10(3) t, 2.67 x 10(4) and 4.51 x 10(3) t, respectively. The marine source is the largest non-road mobile source contributor to SO2, NOx, CO and PM10 Emissions, accounting for 96.4%, 73.8%, 39.4% and 50.5%, respectively; Freighter and dry bulk carrier are important marine Emission contributors, sharing 89.8%, 81.8%, 77.3%, 79.5% and 81.7% of the total marine SO2, NOx, VOC, CO and PM10 Emissions. The non-road mobile source has become the third largest SO2 and NOx contributor in the PRD region, accounting for about 8.6% and 13.5% of the regional total SO2 and NOx Emissions.
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speciated voc Emission Inventory and spatial patterns of ozone formation potential in the pearl river delta china
Environmental Science & Technology, 2009Co-Authors: Junyu Zheng, Lijun Zhang, Liuju Zhong, Min Shao, Wenwei Che, Yuanhang Zhang, David G. StreetsAbstract:The Pearl River Delta region (PRD) of China has long suffered from severe ground-level ozone pollution. Knowledge of the sources of volatile organic compounds (VOCs) is essential for ozone chemistry. In this work, a speciated VOC Emission Inventory was established on the basis of updated Emissions and local VOC source profiles. The top 10 species, in terms of ozone formation potentials (OFPs), consisted of isoprene, mp-xylene, toluene, ethylene, propene, o-xylene, 1,2,4-trimethylbenzene, 2-methyl-2-butene, 1-butene, and α-pinene. These species contributed only 35.9% to VOCs Emissions but accounted for 64.1% of the OFP in the region. The spatial patterns of the VOC source Inventory agreed well with city-based source apportionment results, especially for vehicle Emissions and industry plus VOC product-related Emissions. Mapping of the OFPs and measured ozone concentrations indicated that the formation of higher ozone in the south and southeast of the PRD region differed from that in the Conghua area, a remo...
