Anthropogenic emission inventory of multiple air pollutants and their spatiotemporal variations in 2017 for the Shandong Province, China

2021 ◽  
pp. 117666
Author(s):  
Mimi Zhou ◽  
Wei Jiang ◽  
Weidong Gao ◽  
Xiaomei Gao ◽  
Mingchun Ma ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Emission Inventory ◽  
Shandong Province ◽  
Anthropogenic Emission ◽  
Spatiotemporal Variations

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

Influence of emission inventory resolution on the modeled spatio-temporal distribution of air pollutants in Buenos Aires, Argentina, using WRF-Chem

2021 ◽  
pp. 118839
Author(s):  
Ana I. López-Noreña ◽  
Lucas Berná ◽  
Maria Florencia Tames ◽  
Emmanuel N. Millán ◽  
S. Enrique Puliafito ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Emission Inventory ◽  
Buenos Aires ◽  
Temporal Distribution ◽  
Spatio Temporal

A high spatial-temporal resolution emission inventory of multi-type air pollutants for Wuxi city

2019 ◽  
Vol 229 ◽  
pp. 278-288 ◽  
Author(s):  
Hui Hua ◽  
Songyan Jiang ◽  
Hu Sheng ◽  
You Zhang ◽  
Xuewei Liu ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Wuxi City

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

2020 ◽  
Author(s):  
Rahul Chaurasia ◽  
Manju Mohan
Keyword(s):  
Air Pollution ◽  
Spatial Distribution ◽  
High Resolution ◽  
Power Plant ◽  
Population Density ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Road Density ◽  
Criteria Air Pollutants ◽  
National Capital

<p>The megacities of the world are experiencing a punishing level of air pollution where primary sources of emissions are industrial, residential and transportation. Delhi is also no exception and had been worst performing in terms of air quality and air pollution. In this backdrop, a high-resolution emission inventory becomes an essential tool to predict and forecast pollutant concentration along with the assessment of the impact of various government policies. This study aims to prepare a high-resolution gridded emission inventory (1km*1km) of criteria air pollutants (PM10, PM2.5, NO<sub>2</sub>, SO<sub>2 </sub>and CO) for Delhi-NCT (National Capital Territory).  The bottom-up gridded emission inventory has been prepared taking account of population density, land use pattern and socio-economic status. The emission from all the primary sectors has been taken into accounts such as transport, residential burning, industries, power plants, and municipal solid waste burning.  The emissions are estimated using emission factors and activity data for each sector. The emission factor for various fuel type burning is taken from CPCB (Central Pollution Control Board) reports and previous literature. Data corresponding to various sectors such as the amount of fuel consumed, population density, road density, traffic congestion points, industrial location, unauthorized colonies, slums, and total solid waste generation has been acquired from various government bodies, reports, and literature. The result reveals that the total estimated emissions from transportation, industries and domestic sector contribute nearly 72%, 60%, 52% of NOx, SO2 and PM10 emission respectively.  The transport sector has been found as the bulk contributor towards CO and NOx emissions. Domestic sector and Power plant emission have been found to be a bulk contributor of CO and SO2. Later, the spatial distribution of the emission is done using GIS technique (Arc-GIS). For spatial distribution of emission, district-wise population data, road density data, power plant location and digitization of the road network was carried out.</p>

scholarly journals Impact of model resolution on chemical ozone formation in Mexico City: application of the WRF-Chem model

2010 ◽  
Vol 10 (18) ◽  
pp. 8983-8995 ◽  
Author(s):  
X. Tie ◽  
G. Brasseur ◽  
Z. Ying
Keyword(s):  
Mexico City ◽  
Urban Areas ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Meteorological Conditions ◽  
Transport Processes ◽  
Ozone Production ◽  
Model Resolution ◽  
Coarse Resolution ◽  
Photochemical Ozone

Abstract. The resolution of regional chemical/dynamical models has important effects on the calculation of the distributions of air pollutants in urban areas. In this study, the sensitivity of air pollutants and photochemical ozone production to different model resolutions is assessed by applying a regional chemical/dynamical model (version 3 of Weather Research and Forecasting Chemical model – WRF-Chemv3) to the case of Mexico City. The model results with 3, 6, 12, and 24 km resolutions are compared to local surface measurements of CO, NOx, and O3. The study shows that the model resolutions of 3 and 6 km provide reasonable simulations of surface CO, NOx, and O3 concentrations and of diurnal variations. The model tends to underestimate the measurements when the resolution is reduced to 12 km or less. The calculated surface CO, NOx, and O3 concentrations at 24 km resolution are significantly lower than measured values. This study suggests that the ratio of the city size to the threshold resolution is 6 to 1, and that this ratio can be considered as a test value in other urban areas for model resolution setting. There are three major factors related to the effects of model resolution on the calculations of O3 and O3 precursors, including; (1) the calculated meteorological conditions, (2) the spatial distribution for the emissions of ozone precursors, and (3) the non-linearity in the photochemical ozone production. Model studies suggest that, for the calculations of O3 and O3 precursors, spatial resolutions (resulting from different meteorological condition and transport processes) have larger impacts than the effect of the resolution associated with emission inventories. The model shows that, with coarse resolution of emission inventory (24 km) and high resolution for meteorological conditions (6 km), the calculated CO and O3 are considerably improved compared to the results obtained with coarse resolution for both emission inventory and meteorological conditions (24 km). The resolution of the surface emissions has important effects on the calculated concentration fields, but the effects are smaller than those resulting from the model resolution. This study also suggests that the effect of model resolution on O3 precursors leads to important impacts on the photochemical formation of ozone. This results directly from the non-linear relationship between O3 formation and O3 precursor concentrations. Finally, this study suggests that, considering the balance between model performance and required computation time on current computers, the 6 km resolution is an optimal resolution for the calculation of ozone and its precursors in urban areas like Mexico City.

A regional high-resolution emission inventory of primary air pollutants in 2012 for Beijing and the surrounding five provinces of North China

2018 ◽  
Vol 181 ◽  
pp. 20-33 ◽  
Author(s):  
Huanjia Liu ◽  
Bobo Wu ◽  
Shuhan Liu ◽  
Panyang Shao ◽  
Xiangyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Air Pollutants ◽  
North China ◽  
Emission Inventory
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