scholarly journals Climate forced air-quality modeling at urban scale: sensitivity to model resolution, emissions and meteorology

2015 ◽  
Vol 15 (4) ◽  
pp. 4767-4821
Author(s):  
K. Markakis ◽  
M. Valari ◽  
O. Perrussel ◽  
O. Sanchez ◽  
C. Honore
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Scale Effects ◽  
Air Quality Modeling ◽  
Anthropogenic Emissions ◽  
Vertical Resolution ◽  
Model Resolution ◽  
Post Processing ◽  
Quality Modeling ◽  
Forced Air

Abstract. While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects our knowledge is limited on how these uncertainties affect climate forced air-quality assessments. Using as reference a 10 yr model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~8%), the post-processing method (6.5%) and model resolution (~5) while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~32%) and the resolution of the emission inventory (~24%) while model horizontal and vertical resolution are of little effect. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~40% by adopting a higher resolution emission inventory, by 25% by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10% by adopting the annual emission totals of the local inventory. The bias on PM2.5 follows a more complex pattern with the positive bias associated to the coarse run (+3.6 μg m3) increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles the coarse simulation cannot selectively incorporate local scale features in order to reduce model error.

scholarly journals Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

2015 ◽  
Vol 15 (13) ◽  
pp. 7703-7723 ◽  
Author(s):  
K. Markakis ◽  
M. Valari ◽  
O. Perrussel ◽  
O. Sanchez ◽  
C. Honore
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Horizontal Resolution ◽  
Anthropogenic Emissions ◽  
Vertical Resolution ◽  
Post Processing ◽  
Air Quality Model ◽  
Quality Model ◽  
Quality Modeling ◽  
Forced Air

Abstract. While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climate-forced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (~ 5 %), while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~ 32 %) and the resolution of the emission inventory (~ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM2.5. In contrast this consistency is not observed for PM2.5. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 μg m−3), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error.

scholarly journals Emission Inventory Processing of Biomass Burning from A Global Dataset for Air Quality Modeling

2020 ◽  
Author(s):  
Ernesto Pino-Cortés ◽  
Samuel Carrasco ◽  
Luis A. Díaz-Robles ◽  
Francisco Cubillos ◽  
Fidel Vallejo ◽  
...  
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Emission Inventory ◽  
Sparse Matrix ◽  
Local Level ◽  
Air Quality Modeling ◽  
Simulation Software ◽  
Atmospheric Pollutants ◽  
Fire Emissions ◽  
Quality Modeling

Wildfires generate large amounts of atmospheric pollutants yearly. The development of an emissions inventory for this activity is a challenge today, mainly to perform modeling of air quality. There are free available databases with historical information about this source. The main goal of this study was to process the results of biomass burning emissions for the year 2014 from the Global Fire Assimilation System (GFAS). The pollutants studied were the black carbon, the organic carbon, fine and coarse particulate matter, respectively. The inputs were pre-formatted to enter to the simulation software of the emission inventory. In this case, the Sparse Matrix Operator Kernel Emissions (SMOKE) was used and the values obtained in various cities were analyzed. As a result, the spatial distribution of the forest fire emissions in the Southern Hemisphere was achieved, with the polar stereographic projection. The highest emissions were located in the African continent, followed by the northern region of Australia. Future air quality modeling at a local level could apply the results and the methodology of this study. The biomass burning emissions could add a better performance of the results and more knowledge on the effect of this source.

Air pollution emission inventory and air quality modeling for Can Tho City, Mekong Delta, Vietnam

2017 ◽  
Vol 11 (1) ◽  
pp. 35-47 ◽  
Author(s):  
Ho Quoc Bang ◽  
Vu Hoang Ngoc Khue ◽  
Nguyen Thoai Tam ◽  
Kristofer Lasko
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Emission Inventory ◽  
Mekong Delta ◽  
Air Quality Modeling ◽  
Pollution Emission ◽  
Quality Modeling ◽  
Can Tho City

scholarly journals Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China

2017 ◽  
Vol 17 (1) ◽  
pp. 211-233 ◽  
Author(s):  
Yaduan Zhou ◽  
Yu Zhao ◽  
Pan Mao ◽  
Qiang Zhang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Spatial Scales ◽  
Air Quality Modeling ◽  
Nox Emissions ◽  
Quality Modeling ◽  
Emission Estimation ◽  
Southern Jiangsu

Abstract. Improved emission inventories combining detailed source information are crucial for better understanding of the atmospheric chemistry and effectively making emission control policies using air quality simulation, particularly at regional or local scales. With the downscaled inventories directly applied, chemical transport models might not be able to reproduce the authentic evolution of atmospheric pollution processes at small spatial scales. Using the bottom-up approach, a high-resolution emission inventory was developed for Jiangsu China, including SO2, NOx, CO, NH3, volatile organic compounds (VOCs), total suspended particulates (TSP), PM10, PM2.5, black carbon (BC), organic carbon (OC), and CO2. The key parameters relevant to emission estimation for over 6000 industrial sources were investigated, compiled, and revised at plant level based on various data sources and on-site surveys. As a result, the emission fractions of point sources were significantly elevated for most species. The improvement of this provincial inventory was evaluated through comparisons with other inventories at larger spatial scales, using satellite observation and air quality modeling. Compared to the downscaled Multi-resolution Emission Inventory for China (MEIC), the spatial distribution of NOx emissions in our provincial inventory was more consistent with summer tropospheric NO2 VCDs observed from OMI, particularly for the grids with moderate emission levels, implying the improved emission estimation for small and medium industrial plants by this work. Three inventories (national, regional, and provincial by this work) were applied in the Models-3 Community Multi-scale Air Quality (CMAQ) system for southern Jiangsu October 2012, to evaluate the model performances with different emission inputs. The best agreement between available ground observation and simulation was found when the provincial inventory was applied, indicated by the smallest normalized mean bias (NMB) and normalized mean errors (NME) for all the concerned species SO2, NO2, O3, and PM2.5. The result thus implied the advantage of improved emission inventory at local scale for high-resolution air quality modeling. Under the unfavorable meteorology in which horizontal and vertical movement of atmosphere was limited, the simulated SO2 concentrations at downtown Nanjing (the capital city of Jiangsu) using the regional or national inventories were much higher than those observed, implying that the urban emissions were overestimated when economy or population densities were applied to downscale or allocate the emissions. With more accurate spatial distribution of emissions at city level, the simulated concentrations using the provincial inventory were much closer to observation. Sensitivity analysis of PM2.5 and O3 formation was conducted using the improved provincial inventory through the brute force method. Iron and steel plants and cement plants were identified as important contributors to the PM2.5 concentrations in Nanjing. The O3 formation was VOC-limited in southern Jiangsu, and the concentrations were negatively correlated with NOx emissions in urban areas owing to the accumulated NOx from transportation. More evaluations are further suggested for the impacts of speciation and temporal and vertical distribution of emissions on air quality modeling at regional or local scales in China.

scholarly journals Development of a high-resolution emission inventory and its evaluation through air quality modeling for Jiangsu Province, China

2016 ◽  
Author(s):  
Yaduan Zhou ◽  
Yu Zhao ◽  
Pan Mao ◽  
Qiang Zhang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Spatial Scales ◽  
Air Quality Modeling ◽  
Capital City ◽  
Nox Emissions ◽  
Quality Modeling ◽  
Emission Estimation ◽  
Southern Jiangsu

Abstract. Improved emission inventories combining detailed source information are crucial for better understanding the atmospheric chemistry and effectively making emission control policies using air quality simulation, particularly at regional or local scales. With the downscaled inventories directly applied, chemical transport model (CTM) might not be able to well reproduce the evolution of atmospheric pollution process at small spatial scales. Using the bottom-up approach, a high-resolution emission inventory was developed for Jiangsu China, including SO2, NOx, CO, NH3, volatile organic compounds (VOCs), total suspended particulates (TSP), PM10, PM2.5, black carbon (BC), organic carbon (OC), and CO2. The key parameters relevant to emission estimation for over 6000 industrial sources were investigated, compiled and revised at plant level based on various data sources and on-site survey. As a result, the emission fractions of point sources were significantly elevated for most species. The improvement of this provincial inventory was evaluated through comparisons with other inventories at larger spatial scales, using satellite observation and air quality modeling. Compared to the downscaled Multi-resolution Emission Inventory for China (MEIC), the spatial distribution of NOx emissions in our provincial inventory was more consistent with summer tropospheric NO2 VCDs observed from OMI, particularly for the grids with moderate emission levels, implying the improved emission estimation for small and medium industrial plants by this work. Three inventories (national, regional, and provincial by this work) were applied in the Models-3/Community Multi-scale Air Quality (CMAQ) system for southern Jiangsu, to evaluate the model performances with different emission inputs. The best agreement between available ground observation and simulation was found when the provincial inventory was applied, indicated by the smallest normalized mean bias (NMB) and normalized mean errors (NME) for all the concerned species SO2, NO2, O3 and PM2.5. The result thus implied the advantage of improved emission inventory at local scale for high resolution air quality modeling. Under the unfavorable meteorology for pollution transport, in particular, much higher SO2 concentrations than observation were simulated for downtown Nanjing (the capital city of Jiangsu) using the regional or national inventories, implying the overestimation in urban emissions when the locations of large emitters were not fully considered, and the densities of economy or population were simply applied to downscale or allocate the emissions. With more accurate spatial distribution of emissions at city level, the simulated concentrations using the provincial inventory were much closer to observation. Sensitivity analysis of PM2.5 and O3 formation was conducted using the improved provincial inventory through the Brute Force method. Iron & steel and cement plants were identified as important contributors to the PM2.5 concentrations in Nanjing (the capital city of Jiangsu). The O3 formation was VOCs-limited in southern Jiangsu, and the concentrations were negatively correlated with NOx emissions in urban areas owing to the accumulated NOx from transportation. More evaluations are further suggested for the impacts of speciation and temporal and vertical distribution of emissions on air quality modeling at regional or local scales in China.

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