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 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 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 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 Ozone pollution during the COVID-19 lockdown in the spring 2020 over Europe analysed from satellite observations, in situ measurements and models

2021 ◽  
Author(s):  
Juan Cuesta ◽  
Lorenzo Costantino ◽  
Matthias Beekmann ◽  
Guillaume Siour ◽  
Laurent Menut ◽  
...  
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Surface Ozone ◽  
In Situ Measurements ◽  
Satellite Observations ◽  
Emission Inventories ◽  
Ozone Pollution ◽  
Chemistry Transport Model ◽  
Near Surface

Abstract. We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements and chemistry transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 multispectral synergism, which provides particularly enhanced sensitivity to near-surface ozone pollution. These observations are first analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in Central Europe and Northern Italy, and some other hotspots, which are typically characterized by VOC-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb and the same standard deviation and range of variability). An average bias of ∼8 ppb between the two observational datasets is remarked, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for withdrawing the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry transport model simulations using the meteorological fields of each year and identical emission inventories. This observational estimate of the influence of lockdown emission reduction is consistent for both datasets. They both show lockdown-associated ozone enhancements in hotspots over Central Europe and Northern Italy, with a reduced amplitude with respect to the total changes observed between the two years, and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally highlight the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozonesondes measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistry transport model. For analysing the uncertainty of the model estimates, we perform two sets of simulations with different setups, differing in the emission inventories, their modifications to account for changes in anthropogenic activities during the lockdown and the meteorological fields. Whereas a general qualitative consistency of positive and negative ozone anomalies is remarked between all model and observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational data sets, both for enhancements and decreases of ozone, while the large-scale ozone decrease is not simulated. With one of the setups, the model simulates ozone enhancements a factor 3 to 6 smaller than with the other configuration. This is partly linked to the emission inventories of ozone precursors (at least a 30 % difference), but mainly to differences in vertical mixing of atmospheric constituents depending on the choice of the meteorological model.

scholarly journals Evaluation of the LOTOS-EUROS NO<sub>2</sub> simulations using ground-based measurements and S5P/TROPOMI observations over Greece

2021 ◽  
Vol 21 (7) ◽  
pp. 5269-5288
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Astrid Manders ◽  
Arjo Segers ◽  
Dimitris Karagkiozidis ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Extreme Values ◽  
Transport Model ◽  
Chemical Transport ◽  
Pollution Monitoring ◽  
Optical Absorption Spectroscopy ◽  
Chemical Transport Model ◽  
Night Time ◽  
Tropospheric No2

Abstract. The evaluation of chemical transport models, CTMs, is essential for the assessment of their performance regarding the physical and chemical parameterizations used. While regional CTMs have been widely used and evaluated over Europe, their validation over Greece is limited. In this study, we investigate the performance of the Long Term Ozone Simulation European Operational Smog (LOTOS-EUROS) v2.2.001 regional chemical transport model in simulating nitrogen dioxide, NO2, over Greece from June to December 2018. In situ NO2 measurements obtained from 14 stations of the National Air Pollution Monitoring Network are compared with surface simulations over the two major cities of Greece, Athens and Thessaloniki. Overall the LOTOS-EUROS NO2 surface simulations compare very well to the in situ measurements showing a mild underestimation of the measurements with a mean relative bias of ∼-10 %, a high spatial correlation coefficient of 0.86 and an average temporal correlation of 0.52. The CTM underestimates the NO2 surface concentrations during daytime by ∼-50 ± 15 %, while it slightly overestimates during night-time ∼ 10 ± 35 %. Furthermore, the LOTOS-EUROS tropospheric NO2 columns are evaluated against ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) NO2 measurements in Athens and Thessaloniki. We report that the CTM tropospheric NO2 column simulations over both urban and rural locations represent the diurnal patterns and hourly levels for both summer and winter seasons satisfactorily. The relative biases range between ∼ −2 % and −35 %, depending on season and relative NO2 load observed. Finally, the CTM was assessed also against space-borne Sentinel-5 Precursor (S5P) carrying the Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 observations. We conclude that LOTOS-EUROS simulates extremely well the tropospheric NO2 patterns over the region with very high spatial correlation of 0.82 on average, ranging between 0.66 and 0.95, with negative biases in the summer and positive in the winter. Updated emissions for the simulations and model improvements when extreme values of boundary layer height are encountered are further suggested.

scholarly journals Evaluation of OMI operational standard NO<sub>2</sub> column retrievals using in situ and surface-based NO<sub>2</sub> observations

2014 ◽  
Vol 14 (10) ◽  
pp. 14519-14573 ◽  
Author(s):  
L. N. Lamsal ◽  
N. A. Krotkov ◽  
E. A. Celarier ◽  
W. H. Swartz ◽  
K. E. Pickering ◽  
...  
Keyword(s):  
Emission Inventory ◽  
Transport Model ◽  
In Situ Measurements ◽  
Nox Emission ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Vertical Column ◽  
Bottom Up ◽  
Tropospheric No2

Abstract. We assess the standard operational nitrogen dioxide (NO2) data product (OMNO2, version 2.1) retrieved from the Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite using a combination of aircraft and surface in situ measurements as well as ground-based column measurements at several locations and a bottom-up NOx emission inventory over the continental US. Despite considerable sampling differences, NO2 vertical column densities from OMI are modestly correlated (r = 0.3–0.8) with in situ measurements of tropospheric NO2 from aircraft, ground-based observations of NO2 columns from MAX-DOAS and Pandora instruments, in situ surface NO2 measurements from photolytic converter instruments, and a bottom-up NOx emission inventory. Overall, OMI retrievals tend to be lower in urban regions and higher in remote areas, but generally agree with other measurements to within ± 20%. No consistent seasonal bias is evident. Contrasting results between different data sets reveal complexities behind NO2 validation. Monthly mean vertical NO2 profile shapes from the Global Modeling Initiative (GMI) chemistry-transport model (CTM) used in the OMI retrievals are highly consistent with in situ aircraft measurements, but these measured profiles exhibit considerable day-to-day variation, affecting the retrieved daily NO2 columns by up to 40%. This assessment of OMI tropospheric NO2 columns, together with the comparison of OMI-retrieved and model-simulated NO2 columns, could offer diagnostic evaluation of the model.

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

2016 ◽  
Author(s):  
Guannan Geng ◽  
Qiang Zhang ◽  
Randall Martin ◽  
Jintai Lin ◽  
Hong Huo ◽  
...  
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Urban Areas ◽  
Road Transport ◽  
Emission Inventories ◽  
Bottom Up ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Current Emission ◽  
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 five gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem modeled tropospheric NO2 columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 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 columns. When applying industrial gross domestic product (GDP) values and an updated road network map as proxies for the industrial and on-road transport sectors respectively, modeled NO2 columns could better capture pollution hotspots in urban areas and exhibit best performance of the five cases comparing to satellite-based NO2 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.

Improving ozone forecasts over Europe by synergistic use of the LOTOS-EUROS chemical transport model and in-situ measurements

2012 ◽  
Vol 60 ◽  
pp. 217-226 ◽  
Author(s):  
R.L. Curier ◽  
R. Timmermans ◽  
S. Calabretta-Jongen ◽  
H. Eskes ◽  
A. Segers ◽  
...  
Keyword(s):  
Transport Model ◽  
Chemical Transport ◽  
In Situ Measurements ◽  
Chemical Transport Model

scholarly journals Estimating real driving emissions from MAX-DOAS measurements at the A60 motorway near Mainz, Germany

2020 ◽  
Author(s):  
Bianca Lauster ◽  
Steffen Dörner ◽  
Steffen Beirle ◽  
Sebastian Donner ◽  
Sergey Gromov ◽  
...  
Keyword(s):  
Nitrogen Oxides ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Volume ◽  
In Situ Measurements ◽  
Wind Data ◽  
Nox Emissions ◽  
Dominant Source ◽  
Emission Standards

Abstract. In urban areas, road traffic is a dominant source of nitrogen oxides (NOx = NO + NO2). Although the emissions from individual vehicles are regulated by the European emission standards, real driving emissions often exceed these limits. In this study, two MAX-DOAS instruments on opposite sides of the motorway were used to measure the NO2 absorption caused by road traffic at the A60 motorway close to Mainz, Germany. In combination with wind data, the total NOx emissions for the occurring traffic volume can be estimated. We show that the measured emissions exceed the maximum expected emissions calculated from the European emission standards by a factor of 11 ± 7. One major advantage of the method used here is that from MAX-DOAS measurements the integrated NO2 concentration over the lowermost 2 to 3 km is determined. Thus, all emitted NO2 molecules are detected independent from their altitude and therefore the whole emission plume originating from the nearby motorway is captured by these measurements which is a key advantage compared to other approaches such as in-situ measurements.

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