Testing the ability of trajectory statistics to reproduce emission inventories of air pollutants in cases of negligible measurement and transport errors

1999 ◽  
Vol 33 (18) ◽  
pp. 3037-3043 ◽  
Author(s):  
Gerhard Wotawa ◽  
Heinke Kröger
Keyword(s):  
Air Pollutants ◽  
Emission Inventories

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.

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

Bottom-up emission inventories of multiple air pollutants from open straw burning: A case study of Jiangsu province, Eastern China

2019 ◽  
Vol 10 (2) ◽  
pp. 501-507 ◽  
Author(s):  
Li Li ◽  
Qiuyue Zhao ◽  
Jie Zhang ◽  
Huipeng Li ◽  
Qian Liu ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Eastern China ◽  
Jiangsu Province ◽  
Emission Inventories ◽  
Bottom Up ◽  
Straw Burning

scholarly journals Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

2017 ◽  
Vol 17 (21) ◽  
pp. 13103-13118 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Health Effect ◽  
Model Performance ◽  
Performance Criteria ◽  
Ensemble Prediction ◽  
Daily Maximum ◽  
Observation Data ◽  
Emission Inventories

Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four 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 of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) 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 O3-1h. The study demonstrates that ensemble predictions from 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 effect studies.

Identifying Policy Measures for Reducing Expected Air Pollution Across Israel and Analyzing their Expected Effects

2018 ◽  
Vol 20 (01) ◽  
pp. 1850001 ◽  
Author(s):  
Doron Lavee ◽  
Ofer Menachem
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Emission Inventories ◽  
Dispersion Models ◽  
Target Values ◽  
Policy Measures ◽  
Business As Usual

This study analyzes the effects of setting policy measures for reducing the emission of air pollutants. The study focuses on five main air pollutants, PM[Formula: see text], PM[Formula: see text], O3, NO2 and SO2. Appropriate policy measures for improving air quality were determined based on data on current and expected air quality obtained by dispersion models, as well as on expected emission inventories for the target years 2015 and 2020. The scenario anticipated to result from implementing these measures was also analyzed by dispersion models. The findings indicate that “business as usual” (BAU) policy measures are not sufficient for meeting the target values for the target years. This study identifies additional policy measures that can considerably improve air quality. However, the concentrations of several pollutants are still expected to exceed their target values in many areas, requiring an even greater expansion of the proposed measures for their reduction.

scholarly journals What can we learn about ship emission inventories from measurements of air pollutants over the Mediterranean Sea?

2009 ◽  
Vol 9 (2) ◽  
pp. 7155-7211 ◽  
Author(s):  
E. Marmer ◽  
F. Dentener ◽  
J. v. Aardenne ◽  
F. Cavalli ◽  
E. Vignati ◽  
...  
Keyword(s):  
Black Carbon ◽  
Atmospheric Chemistry ◽  
Air Pollutants ◽  
Transport Model ◽  
Eastern Mediterranean ◽  
Air Pollutant ◽  
Emission Inventories ◽  
Atmospheric Measurements ◽  
The Mediterranean ◽  
Ship Emission

Abstract. Ship emission estimates diverge widely for all chemical compounds for several reasons: use of different methodologies (bottom-up or top-down), activity data and emission factors can easily result in a difference from a factor of 1.5 to two orders of magnitude. Despite these large discrepancies in existing ship emission inventories for air pollutants very little has been done to evaluate their consistency with atmospheric measurements at open sea. Combining three sets of observational data – ozone and black carbon measurements sampled at three coastal sites and on board of a Mediterranean cruise ship, as well as satellite observations of atmospheric NO2 column concentration over the same area – we assess the accuracy of the three most commonly used ship emission inventories, EDGAR FT (Olivier et al., 2005), emissions described by Eyring et al. (2005) and emissions reported by EMEP (Vestreng et al., 2007). Our tool is a global atmospheric chemistry transport model which simulates the chemical state of the Mediterranean atmosphere applying different ship emission inventories. The simulated contributions of ships to air pollutant levels in the Mediterranean atmosphere are significant but strongly depend on the inventory applied. Close to the major shipping routes relative contributions vary from 10 to 50% for black carbon and from 2 to 12% for ozone in the surface layer, as well as from 5 to 20% for nitrogen dioxide atmospheric column burden. The relative contributions are still significant over the North African coast, but less so over the South European coast. The observations poorly constrain the ship emission inventories in the Eastern Mediterranean where the influence of uncertain land based emissions, the model transport and wet deposition are at least as important as the signal from ships. In the Western Mediterranean, the regional EMEP emission inventory gives the best match with most measurements, followed by Eyring for NO2 and ozone and by EDGAR for black carbon. Given the uncertainty of the measurements and the model, each of the three emission inventories could actually be right, implying that large uncertainties in ship emissions need to be considered for future scenario analysis.

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