scholarly journals Intercomparison of NO<sub><i>x</i></sub> emission inventories over East Asia

2017 ◽  
Vol 17 (16) ◽  
pp. 10125-10141 ◽  
Author(s):  
Jieying Ding ◽  
Kazuyuki Miyazaki ◽  
Ronald Johannes van der A ◽  
Bas Mijling ◽  
Jun-ichi Kurokawa ◽  
...  
Keyword(s):  
Kalman Filter ◽  
East Asia ◽  
Rural Areas ◽  
Emission Inventory ◽  
Regional Scale ◽  
Policy Support ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Temporal Behaviour

Abstract. We compare nine emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

scholarly journals Intercomparison of NO<sub><i>x</i></sub> emission inventories over East Asia

2017 ◽  
Author(s):  
Jieying Ding ◽  
Kazuyuki Miyazaki ◽  
Ronald Johannes van der A ◽  
Bas Mijling ◽  
Jun-ichi Kurokawa ◽  
...  
Keyword(s):  
Kalman Filter ◽  
East Asia ◽  
Rural Areas ◽  
Emission Inventory ◽  
Regional Scale ◽  
Policy Support ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Temporal Behaviour

Abstract. We compare 9 emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman Filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over Mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show: the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

scholarly journals Investigation of NO<sub>x</sub> emissions and NO<sub>x</sub>-related chemistry in East Asia using CMAQ-predicted and GOME-derived NO<sub>2</sub> columns

2009 ◽  
Vol 9 (3) ◽  
pp. 1017-1036 ◽  
Author(s):  
K. M. Han ◽  
C. H. Song ◽  
H. J. Ahn ◽  
R. S. Park ◽  
J. H. Woo ◽  
...  
Keyword(s):  
South Korea ◽  
East Asia ◽  
Seasonal Variations ◽  
North China ◽  
Emission Inventory ◽  
Nox Emission ◽  
Policy Support ◽  
Asia Pacific ◽  
Nox Emissions ◽  
Loss Rates

Abstract. In this study, NO2 columns from the US EPA Models-3/CMAQ model simulations carried out using the 2001 ACE-ASIA (Asia Pacific Regional Aerosol Characterization Experiment) emission inventory over East Asia were compared with the GOME-derived NO2 columns. There were large discrepancies between the CMAQ-predicted and GOME-derived NO2 columns in the fall and winter seasons. In particular, while the CMAQ-predicted NO2 columns produced larger values than the GOME-derived NO2 columns over South Korea for all four seasons, the CMAQ-predicted NO2 columns produced smaller values than the GOME-derived NO2 columns over North China for all seasons with the exception of summer (summer anomaly). It is believed that there might be some error in the NOx emission estimates as well as uncertainty in the NOx chemical loss rates over North China and South Korea. Regarding the latter, this study further focused on the biogenic VOC (BVOC) emissions that were strongly coupled with NOx chemistry during summer in East Asia. This study also investigated whether the CMAQ-modeled NO2/NOx ratios with the possibly overestimated isoprene emissions were higher than those with reduced isoprene emissions. Although changes in both the NOx chemical loss rates and NO2/NOx ratios from CMAQ-modeling with the different isoprene emissions affected the CMAQ-modeled NO2 levels, the effects were found to be limited, mainly due to the low absolute levels of NO2 in summer. Seasonal variations of the NOx emission fluxes over East Asia were further investigated by a set of sensitivity runs of the CMAQ model. Although the results still exhibited the summer anomaly possibly due to the uncertainties in both NOx-related chemistry in the CMAQ model and the GOME measurements, it is believed that consideration of both the seasonal variations in NOx emissions and the correct BVOC emissions in East Asia are critical. Overall, it is estimated that the NOx emissions are underestimated by ~57.3% in North China and overestimated by ~46.1% in South Korea over an entire year. In order to confirm the uncertainty in NOx emissions, the NOx emissions over South Korea and China were further investigated using the ACE-ASIA, REAS (Regional Emission inventory in ASia), and CAPSS (Clean Air Policy Support System) emission inventories. The comparison between the CMAQ-calculated and GOME-derived NO2 columns indicated that both the ACE-ASIA and REAS inventories have some uncertainty in NOx emissions over North China and South Korea, which can also lead to some errors in modeling the formation of ozone and secondary aerosols in South Korea and North China.

scholarly journals Evaluation of modeling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

2018 ◽  
Vol 18 (6) ◽  
pp. 4171-4186 ◽  
Author(s):  
Fei Liu ◽  
Ronald J. van der A ◽  
Henk Eskes ◽  
Jieying Ding ◽  
Bas Mijling
Keyword(s):  
Spatial Correlation ◽  
Rural Areas ◽  
Urban Areas ◽  
Transport Model ◽  
Chemical Transport ◽  
In Situ Measurements ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope  =  0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope  =  1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of −30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

scholarly journals Evaluation of modelling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in-situ measurements over China

2017 ◽  
Author(s):  
Fei Liu ◽  
Ronald J. van der A ◽  
Henk Eskes ◽  
Jieying Ding ◽  
Bas Mijling
Keyword(s):  
Spatial Correlation ◽  
Rural Areas ◽  
Urban Areas ◽  
Transport Model ◽  
Chemical Transport ◽  
In Situ Measurements ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modelling surface NO2 concentrations from the CHIMERE regional chemical-transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modelled ratio of NO2 to NOz. The model accurately reproduces the spatial variability of NO2 from in-situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74/0.64 for the daily-mean/daytime only) and the MIX (slope = 1.3/1.1) inventory respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modelled concentrations is reduced with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban/rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid-cell mean. This reduces the estimate of the negative bias of the DECSO based simulation to the range of −30 % to 0 % on average, and establishes more firmly that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer, due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle, but shows more significant disagreement between simulations and measurements during night time, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

scholarly journals Investigation of NO<sub>x</sub> emissions and NO<sub>x</sub>-related chemistry in East Asia using CMAQ-predicted and GOME-derived NO<sub>2</sub> columns

2008 ◽  
Vol 8 (5) ◽  
pp. 17297-17341
Author(s):  
K. M. Han ◽  
C. H. Song ◽  
H. J. Ahn ◽  
C. K. Lee ◽  
A. Richter ◽  
...  
Keyword(s):  
South Korea ◽  
East Asia ◽  
North China ◽  
Emission Inventory ◽  
Estimation Error ◽  
Policy Support ◽  
Environmental Research ◽  
Asia Pacific ◽  
Nox Emissions ◽  
Loss Rates

Abstract. This study examined the estimation accuracy of NOx emissions over East Asia with particular focus on North China and South Korea due to their strong source (North China)-receptor (South Korea) relationship. In order to determine contributions of North China emissions to South Korean air quality accurately, it is important to examine the accuracy of the emission inventories of both regions. In this study, NO2 columns from the US EPA Models-3/CMAQ model simulations carried out using the 2001 ACE-ASIA (Asia Pacific Regional Aerosol Characterization Experiment) emission inventory over East Asia were compared with the GOME-derived NO2 columns. There were large discrepancies between the CMAQ-predicted and GOME-derived NO2 columns in the fall and winter seasons. In particular, while the CMAQ-predicted NO2 columns produced larger values than the GOME-derived NO2 columns over South Korea (receptor region) for all four seasons, the CMAQ-predicted NO2 columns produced smaller values than the GOME-derived NO2 columns over North China (source region) for all seasons with the exception of summer. It is believed that there might be some estimation error in the NOx emissions as well as large uncertainty in NOx loss rates over North China and South Korea. Regarding the latter, this study further focused on the biogenic VOC emissions that were strongly coupled with NOx chemistry in East Asia. It was found that the rates of NOx loss determined by CMAQ modeling studies might be significantly low due to the possible overestimation of biogenic isoprene emissions during summer, particularly in China. In addition, due to the possible overestimation of isoprene emissions, the CMAQ-modeled NO2/NOx ratios might show an incorrectly high level, compared with the actual NO2/NOx ratios. In addition to the retarded NOx chemical loss rates and overestimated NO2/NOx ratios, the omission of soil NOx emissions over North China during summer can lead to an underestimation of NOx emissions over North China during summer. Overall, it is estimated that the NOx emissions in North China are underestimated possibly by ~50% over an entire year. In order to confirm the uncertainty in NOx emissions, the NOx emission over South Korea was further investigated using the ACE-ASIA inventory, REAS (Regional Emission inventory in ASia) and CAPSS (Clean Air Policy Support System) by NIER (National Institute of Environmental Research) in Korea. The NOx emissions from ACE-ASIA and the REAS inventories appear to be approximately 2 times larger for mega-cities in Korea than that from the CAPSS inventory. In contrast, the NOx emissions of ACE-ASIA and REAS inventories are only 10% smaller for North China than the recently-estimated "date-back" ANL (Argonne National Laboratory) inventory. A comparison between the CMAQ-predicted and GOME-derived NO2 columns indicated that both the ACE-ASIA and REAS inventories have some uncertainty in NOx emissions over North China (A) and South Korea (C), which can lead to some error in modeling the formation of ozone and secondary aerosols in South Korea and North China.

Evaluation of Air Pollutant Emission Inventories in East Asia

2020 ◽  
Author(s):  
Younha Kim ◽  
Jung-hun Woo ◽  
Youjung Jang ◽  
Minwoo Park ◽  
Bomi Kim ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
East Asia ◽  
Emission Inventory ◽  
Air Pollutant ◽  
Emission Inventories ◽  
Top Down ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Quality Study

&lt;p&gt;Concentration of air pollutants such as tropospheric ozone and aerosols are mainly affected by meteorological variables and emissions. East Asia has large amount of anthropogenic and natural air pollutant emissions and has been putting lots of efforts to improve air quality. In order to seek effective ways to mitigate future air pollution, it is essential to understand the current emissions and their impacts on air quality. Emission inventory is one of the key datasets required to understand air quality and find ways to improve it. Amounts and spatial-temporal distributions of emissions are, however, not easy to estimate due to their complicate nature, therefore introduce significant uncertainties.&lt;/p&gt;&lt;p&gt;In this study, we had developed an updated version of our Asian emissions inventory, named NIER/KU-CREATE (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment) in support of climate-air quality study. We first inter-compare multiple bottom-up inventories to understand discrepancies among the dataset(sectoral, spatial). We then inter-compare those bottom-up emissions to the satellite-based top-down emission estimates to understand uncertainties of the databases. The bottom-up emission inventories used for this study are: CREATE, MEIC(Multiresolution Emission Inventory for China), REAS (Regional Emission inventory in ASia), and ECLIPSE(Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). The satellite-derived top-down emission inventory had been acquired from the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm data from the GlobEmissions website.&lt;/p&gt;&lt;p&gt;The analysis showed that some discrepancies, in terms of emission amounts, sectoral shares and spatial distribution patterns, exist among the datasets. We analyzed further to find out which parameters could affect more on those discrepancies. Co-analysis of top-down and bottom-up emissions inventory help us to evaluate emissions amount and spatial distribution. These analysis are helpful for the development of more consistent and reliable inventories with the aim of reducing the uncertainties in air quality study. More results of evaluation of emissions will be presented on site.&amp;#160; &amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;Acknowledgements : This work was supported by National Institute of Environment Research (NIER-2019-03-02-005), Korea Environment Industry &amp; Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Program, funded by Korea Ministry of Environment(MOE)(2019000160007). This research was supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(MSIT), the Ministry of Environment(ME), and the Ministry of Health and Welfare(MOHW) (NRF-2017M3D8A1092022).&lt;/p&gt;

scholarly journals Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

2017 ◽  
Vol 17 (6) ◽  
pp. 4131-4145 ◽  
Author(s):  
Guannan Geng ◽  
Qiang Zhang ◽  
Randall V. Martin ◽  
Jintai Lin ◽  
Hong Huo ◽  
...  
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Urban Areas ◽  
Model Performance ◽  
Road Transport ◽  
Industrial Sector ◽  
Transport Sector ◽  
Emission Inventories ◽  
Bottom Up ◽  
The Impact

Abstract. Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled tropospheric NO2 vertical columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 vertical columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 vertical columns. Using vehicle population and an updated road network for the on-road transport sector could substantially enhance urban emissions and improve the model performance. When further applying industrial gross domestic product (IGDP) values for the industrial sector, modeled NO2 vertical columns could better capture pollution hotspots in urban areas and exhibit the best performance of the six cases compared to satellite-based NO2 vertical columns (slope  =  1.01 and R2 = 0. 85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

scholarly journals Improvement of the satellite-derived NO<sub><i>x</i></sub> emissions on air quality modeling and its effect on ozone and secondary inorganic aerosol formation in the Yangtze River Delta, China

2021 ◽  
Vol 21 (2) ◽  
pp. 1191-1209
Author(s):  
Yang Yang ◽  
Yu Zhao ◽  
Lei Zhang ◽  
Jie Zhang ◽  
Xin Huang ◽  
...  
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Model Performance ◽  
Yangtze River Delta ◽  
Nox Emissions ◽  
Top Down ◽  
Bottom Up ◽  
Emission Estimation ◽  
Reduced Rate ◽  
The Yrd

Abstract. We developed a top-down methodology combining the inversed chemistry transport modeling and satellite-derived tropospheric vertical column of NO2 and estimated the NOx emissions of the Yangtze River Delta (YRD) region at a horizontal resolution of 9 km for January, April, July, and October 2016. The effect of the top-down emission estimation on air quality modeling and the response of ambient ozone (O3) and inorganic aerosols (SO42-, NO3-, and NH4+, SNA) to the changed precursor emissions were evaluated with the Community Multi-scale Air Quality (CMAQ) system. The top-down estimates of NOx emissions were smaller than those (i.e., the bottom-up estimates) in a national emission inventory, Multi-resolution Emission Inventory for China (MEIC), for all the 4 months, and the monthly mean was calculated to be 260.0 Gg/month, 24 % less than the bottom-up one. The NO2 concentrations simulated with the bottom-up estimate of NOx emissions were clearly higher than the ground observations, indicating the possible overestimation in the current emission inventory, attributed to its insufficient consideration of recent emission control in the region. The model performance based on top-down estimate was much better, and the biggest change was found for July, with the normalized mean bias (NMB) and normalized mean error (NME) reduced from 111 % to −0.4 % and from 111 % to 33 %, respectively. The results demonstrate the improvement of NOx emission estimation with the nonlinear inversed modeling and satellite observation constraint. With the smaller NOx emissions in the top-down estimate than the bottom-up one, the elevated concentrations of ambient O3 were simulated for most of the YRD, and they were closer to observations except for July, implying the VOC (volatile organic compound)-limited regime of O3 formation. With available ground observations of SNA in the YRD, moreover, better model performance of NO3- and NH4+ was achieved for most seasons, implying the effectiveness of precursor emission estimation on the simulation of secondary inorganic aerosols. Through the sensitivity analysis of O3 formation for April 2016, the decreased O3 concentrations were found for most of the YRD region when only VOC emissions were reduced or the reduced rate of VOC emissions was 2 times of that of NOx, implying the crucial role of VOC control in O3 pollution abatement. The SNA level for January 2016 was simulated to decline 12 % when 30 % of NH3 emissions were reduced, while the change was much smaller with the same reduced rate for SO2 or NOx. The result suggests that reducing NH3 emissions was the most effective way to alleviate SNA pollution of the YRD in winter.

scholarly journals Temporal Analysis of OMI-Observed Tropospheric NO2 Columns over East Asia during 2006–2015

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 658 ◽  
Author(s):  
Kyung M. Han
Keyword(s):  
South Korea ◽  
East Asia ◽  
Temporal Analysis ◽  
Temporal Variations ◽  
Policy Support ◽  
Northwestern Pacific ◽  
Nox Emissions ◽  
Ozone Monitoring Instrument ◽  
Transportation Sector ◽  
Monitoring Service

The study analyzed temporal variations of Ozone Monitoring Instrument (OMI)-observed NO2 columns, interregional correlation, and comparison between NO2 columns and NOx emissions during the period from 2006 to 2015. Regarding the trend of the NO2 columns, the linear lines were classified into four groups: (1) ‘upward and downward’ over six defined geographic regions in central-east Asia; (2) ‘downward’ over Guangzhou, Japan, and Taiwan; (3) ‘stagnant’ over South Korea; and (4) ‘upward’ over North Korea, Mongolia, Qinghai, and Northwestern Pacific ocean. In particular, the levels of NO2 columns in 2015 returned to those in 2006 over most of the polluted regions in China. Quantitatively, their relative changes in 2015 compared to 2006 were approximately 10%. From the interregional correlation analysis, it was found that unlike positive relationships between the polluted areas, the different variations of monthly NO2 columns led to negative relationships in Mongolia and Qinghai. Regarding the comparison between NO2 columns and NOx emission, the NOx emissions from the Copernicus Atmosphere Monitoring Service (CAMS) and Clean Air Policy Support System (CAPSS) inventories did not follow the year-to-year variations of NO2 columns over the polluted regions. In addition, the weekly effect was only clearly shown in South Korea, Japan, and Taiwan, indicating that the amounts of NOx emissions are significantly contributed to by the transportation sector.

scholarly journals Regional nitrogen oxides emission trends in East Asia observed from space

2013 ◽  
Vol 13 (7) ◽  
pp. 17519-17544 ◽  
Author(s):  
B. Mijling ◽  
R. J. van der A ◽  
Q. Zhang
Keyword(s):  
Nitrogen Oxides ◽  
East Asia ◽  
Regional Scale ◽  
Satellite Observations ◽  
Anthropogenic Activity ◽  
Valuable Insight ◽  
Local Concentration ◽  
Positive Trend ◽  
Nox Emissions ◽  
Tropospheric No2

Abstract. Due to changing economic activity, emissions of air pollutants in East Asia change rapidly in space and time. Monthly emission estimates of nitrogen oxides derived from satellite observations provide valuable insight in the evolution of anthropogenic activity on a regional scale. We present the first results of a new emission estimation algorithm, specifically designed to use daily satellite observations of column concentrations for fast updates of emissions of short-lived atmospheric constituents on a~mesoscopic scale (~ 0.25° × 0.25°). The algorithm is used to construct a monthly NOx emission time series for 2007–2011 from tropospheric NO2 observations of GOME-2 for East Chinese provinces and surrounding countries. The new emission estimates correspond well with the bottom-up inventory of EDGAR v4.2, but are smaller than the inventories of INTEX-B and MEIC. They reveal a strong positive trend during 2007–2011 for almost all Chinese provinces, related to the country's economic development. We find a 41% increment of NOx emissions in East China during this period, which shows the need to update emission inventories in this region on a regular basis. Negative emission trends are found in Japan and South Korea, which can be attributed to a combined effect of local environmental policy and global economic crises. Analysis of seasonal variation distinguishes between regions with dominant anthropogenic or biogenic emissions. For regions with a mixed anthropogenic and biogenic signature, the opposite seasonality can be used for an estimation of the separate emission contributions. Finally, the non-local concentration/emission relationships calculated by the algorithm are used to quantify the direct effect of regional NOx emissions on tropospheric NO2 concentrations outside the region. For regions such as North Korea and Beijing province, a substantial part of the tropospheric NO2 originates from emissions elsewhere.

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