Emission Inventories for Air Pollutants and Greenhouse Gases with Emphasis on Data Management in the Cloud

2020 ◽  
pp. 41-71
Author(s):  
Thi Kim Oanh Nguyen ◽  
Nguyen Huy Lai ◽  
Didin Agustian Permadi ◽  
Nhat Ha Chi Nguyen ◽  
Kok Sothea ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Data Management ◽  
Air Pollutants ◽  
Emission Inventories

Common footprints of the greenhouse gases and air pollutants in China

2021 ◽  
Vol 293 ◽  
pp. 125991
Author(s):  
Li-Jing Liu ◽  
Lan-Cui Liu ◽  
Qiao-Mei Liang
Keyword(s):  
Greenhouse Gases ◽  
Air Pollutants

Low-power, open-path mobile sensing platform for high-resolution measurements of greenhouse gases and air pollutants

2015 ◽  
Vol 119 (1) ◽  
pp. 153-164 ◽  
Author(s):  
Lei Tao ◽  
Kang Sun ◽  
David J. Miller ◽  
Dan Pan ◽  
Levi M. Golston ◽  
...  
Keyword(s):  
High Resolution ◽  
Greenhouse Gases ◽  
Low Power ◽  
Air Pollutants ◽  
Mobile Sensing ◽  
Open Path ◽  
Sensing Platform

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.

scholarly journals Uncertainties in emissions estimates of greenhouse gases and air pollutants in India and their impacts on regional air quality

2017 ◽  
Vol 12 (6) ◽  
pp. 065002 ◽  
Author(s):  
Eri Saikawa ◽  
Marcus Trail ◽  
Min Zhong ◽  
Qianru Wu ◽  
Cindy L Young ◽  
...  
Keyword(s):  
Air Quality ◽  
Greenhouse Gases ◽  
Air Pollutants ◽  
Regional Air Quality

scholarly journals Global mapping of greenhouse gases and air pollutants

2007 ◽  
Vol 38 (6) ◽  
pp. 26-32 ◽  
Author(s):  
Adelbert P.H. Goede ◽  
John P. Burrows ◽  
Michael Buchwitz
Keyword(s):  
Greenhouse Gases ◽  
Air Pollutants ◽  
Global Mapping
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