Influence of anthropogenic emission inventories on simulations of air quality in China during winter and summer 2010

Abstract. In this work we couple the HTAPv2.2 global air pollutant emission inventory with the global source receptor model TM5-FASST to evaluate the relative contribution of the major anthropogenic emission sources (power generation, industry, ground transport, residential, agriculture and international shipping) to air quality and human health in 2010. We focus on particulate matter (PM) concentrations because of the relative importance of PM2.5 emissions in populated areas and the proven cumulative negative effects on human health. We estimate that in 2010 regional annual averaged anthropogenic PM2.5 concentrations varied between ca. 1 and 40 μg/m3 depending on the region, with the highest concentrations observed in China and India, and lower concentrations in Europe and North America. The relative contribution of anthropogenic emission source sectors to PM2.5 concentrations varies between the regions. European PM pollution is mainly influenced by the agricultural and residential sectors, while the major contributing sectors to PM pollution in Asia and the emerging economies are the power generation, industrial and residential sectors. We also evaluate the emission sectors and emission regions in which pollution reduction measures would lead to the largest improvement on the overall air quality. We show that in order to improve air quality, regional policies should be implemented (e.g. in Europe) due to the transboundary features of PM pollution. In addition, we investigate emission inventory uncertainties and their propagation to PM2.5 concentrations, in order to identify the most effective strategies to be implemented at sector and regional level to improve emission inventories knowledge and air quality. We show that the uncertainty of PM concentrations depends not only on the uncertainty of local emission inventories but also on that of the surrounding regions. Finally, we propagate emission inventories uncertainty to PM concentrations and health impacts.

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Contribution and uncertainty of sectorial and regional emissions to regional and global PM<sub>2.5</sub> health impacts

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-5165-2019 ◽

2019 ◽

Vol 19 (7) ◽

pp. 5165-5186 ◽

Cited By ~ 11

Author(s):

Monica Crippa ◽

Greet Janssens-Maenhout ◽

Diego Guizzardi ◽

Rita Van Dingenen ◽

Frank Dentener

Keyword(s):

Power Generation ◽

Air Quality ◽

Human Health ◽

Emission Inventory ◽

Health Impacts ◽

Pollutant Emission ◽

Emission Sources ◽

Emission Inventories ◽

Anthropogenic Emission ◽

Premature Deaths

Abstract. In this work we couple the HTAP_v2.2 global air pollutant emission inventory with the global source receptor model TM5-FASST to evaluate the relative contributions of the major anthropogenic emission sources (power generation, industry, ground transport, residential, agriculture and international shipping) to air quality and human health in 2010. We focus on particulate matter (PM) concentrations because of the relative importance of PM2.5 emissions in populated areas and the well-documented cumulative negative effects on human health. We estimate that in 2010, depending on the region, annual averaged anthropogenic PM2.5 concentrations varied between ca. 1 and 40 µg m−3, with the highest concentrations observed in China and India, and lower concentrations in Europe and North America. The relative contribution of anthropogenic emission sources to PM2.5 concentrations varies between the regions. European PM pollution is mainly influenced by the agricultural and residential sectors, while the major contributing sectors to PM pollution in Asia and the emerging economies are the power generation, industrial and residential sectors. We also evaluate the emission sectors and emission regions in which pollution reduction measures would lead to the largest improvement on the overall air quality. We show that air quality improvements would require regional policies, in addition to local- and urban-scale measures, due to the transboundary features of PM pollution. We investigate emission inventory uncertainties and their propagation to PM2.5 concentrations, in order to identify the most effective strategies to be implemented at sector and regional level to improve emission inventories, knowledge and air quality modelling. We show that the uncertainty of PM concentrations depends not only on the uncertainty of local emission inventories, but also on that of the surrounding regions. Countries with high emission uncertainties are often impacted by the uncertainty of pollution coming from surrounding regions, highlighting the need for effective efforts in improving emissions not only within a region but also from extra-regional sources. Finally, we propagate emission inventory uncertainty to PM concentrations and health impacts. We estimate 2.1 million premature deaths per year with an uncertainty of more than 1 million premature deaths per year due to the uncertainty associated only with the emissions.

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Effect of different emission inventories on modeled ozone and carbon monoxide in Southeast Asia

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-9345-2014 ◽

2014 ◽

Vol 14 (7) ◽

pp. 9345-9400 ◽

Cited By ~ 3

Author(s):

T. Amnuaylojaroen ◽

M. C. Barth ◽

L. K. Emmons ◽

G. R. Carmichael ◽

J. Kreasuwun ◽

...

Keyword(s):

Carbon Monoxide ◽

Air Quality ◽

Southeast Asia ◽

Biomass Burning ◽

Atmospheric Composition ◽

Anthropogenic Emissions ◽

Emission Inventories ◽

Anthropogenic Emission ◽

Regional Study ◽

Mixing Ratios

Abstract. In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using MOZART gas-phase chemistry and GOCART aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O3) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass burning emissions are from the Fire Inventory from NCAR (FINNv1) model. WRF-chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O3 is overpredicted. The NO2 tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict NO2 column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O3 and CO mixing ratios, while biomass burning emissions add more variability. The different anthropogenic emissions differ by up to 20% in CO emissions, but O3 and CO mixing ratios differ by ~4.5% and ~8%, respectively, among the simulations. Biomass burning emissions create a substantial increase for both O3 and CO by ~29% and ~16%, respectively, when comparing the March biomass burning period to December with low biomass burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others and any of the examined inventories can be used for air quality simulations in Southeast Asia.

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Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084155 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4155

Author(s):

Diogo Lopes ◽

Joana Ferreira ◽

Ka In Hoi ◽

Ka-Veng Yuen ◽

Kai Meng Mok ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Pearl River Delta ◽

Air Quality Modeling ◽

Pearl River ◽

Emission Inventories ◽

Atmospheric Emission ◽

The Pearl River Delta ◽

Quality Modeling ◽

The Pearl River

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study’s comparison of the three emission inventories’ data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.

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Sensitivity of air quality modelling to different emission inventories: a case study over Europe

Atmospheric Environment X ◽

10.1016/j.aeaoa.2021.100111 ◽

2021 ◽

pp. 100111

Author(s):

Philippe Thunis ◽

Monica Crippa ◽

Cornelis Cuvelier ◽

Diego Guizzardi ◽

Alexander de Meij ◽

...

Keyword(s):

Air Quality ◽

Emission Inventories ◽

Air Quality Modelling

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Dynamic evaluation of regional air quality model’s response to emission reductions in the presence of uncertain emission inventories

Atmospheric Environment ◽

10.1016/j.atmosenv.2011.03.030 ◽

2011 ◽

Vol 45 (24) ◽

pp. 4091-4098 ◽

Cited By ~ 34

Author(s):

Sergey L. Napelenok ◽

Kristen M. Foley ◽

Daiwen Kang ◽

Rohit Mathur ◽

Thomas Pierce ◽

...

Keyword(s):

Air Quality ◽

Emission Inventories ◽

Emission Reductions ◽

Dynamic Evaluation ◽

Regional Air Quality

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

10.5194/acp-2017-182 ◽

2017 ◽

Cited By ~ 1

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.

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