EDGAR V4 Research Articles

Evaluating major anthropogenic VOC emission sources in densely populated Vietnamese cities.

Volatile organic compounds (VOCs) play an important role in urban air pollution, both as primary pollutants and through their contribution to the formation of secondary pollutants, such as tropospheric ozone and secondary organic aerosols. In this study, more than 30 VOC species were continuously monitored in the two most populous cities in Vietnam, namely Ho Chi Minh City (HCMC, September-October 2018 and March 2019) and Hanoi (March 2019). In parallel with ambient VOC sampling, grab sampling was used to target the most prevalent regional-specific emission sources and estimate their emission factors (EFs).Emission ratios (ERs) obtained from ambient sampling were compared between Vietnamese cities and other cities across the globe. No significant differences were observed between HCMC and Hanoi, suggesting the presence of similar sources. Moreover, a good global agreement was obtained in the spatial comparison within a factor of 2, with greater ER for aromatics and pentanes obtained in the Vietnamese cities.The detailed analysis of sources included the evaluation of EF from passenger cars, buses, trucks, motorcycles, 3-wheeled motorcycles, waste burning, and coal-burning emissions. Our comparisons between ambient and near-source concentration profiles show that road transport sources are the main contributors to VOC concentrations in Vietnamese cities.VOC emissions were calculated from measured EF and consumption data available in Hanoi and compared with those estimated by a global emission inventory (EDGAR v4.3.2). The total VOC emissions from the road transport sector estimated by the inventory do not agree with those calculated from our observations which showed higher total emissions by a factor of 3. Furthermore, the inventory misrepresented the VOCs speciation, mainly for isoprene, monoterpenes, aromatics, and oxygenated compounds. Accounting for these differences in regional air quality models would lead to improved predictions of their impacts and help to prioritise pollution reduction strategies in the region.

Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C

Abstract. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period 2009–2017 with stable carbon isotope (δ13C–CO2) information. We applied two Lagrangian particle dispersion models driven by output from two different numerical weather prediction systems (FLEXPART–COSMO and STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on regional CO2 fluxes, to estimate atmospheric δ13C–CO2, and to obtain model-based estimates of the mixed source signatures (δ13Cm). Anthropogenic fluxes were taken from a fuel-type-specific version of the EDGAR v4.3 inventory, while ecosystem fluxes were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulations of CO2, δ13C–CO2, and δ13Cm were then compared to observations performed by quantum cascade laser absorption spectroscopy. The models captured around 40 % of the regional CO2 variability above or below the large-scale background and up to 35 % of the regional variability in δ13C–CO2. This is according to expectations considering the complex Alpine topography, the low intensity of regional signals at JFJ, and the challenging measurements. Best agreement between simulations and observations in terms of short-term variability and intensity of the signals for CO2 and δ13C–CO2 was found between late autumn and early spring. The agreement was inferior in the early autumn periods and during summer. This may be associated with the atmospheric transport representation in the models. In addition, the net ecosystem exchange fluxes are a possible source of error, either through inaccuracies in their representation in VPRM for the (Alpine) vegetation or through a day (uptake) vs. night (respiration) transport discrimination to JFJ. Furthermore, the simulations suggest that JFJ is subject to relatively small regional anthropogenic contributions due to its remote location (elevated and far from major anthropogenic sources) and the limited planetary boundary layer influence during winter. Instead, the station is primarily exposed to summertime ecosystem CO2 contributions, which are dominated by rather nearby sources (within 100 km). Even during winter, simulated gross ecosystem respiration accounted for approximately 50 % of all contributions to the CO2 concentrations above the large-scale background. The model-based monthly mean δ13Cm ranged from − 22 ‰ in winter to − 28 ‰ in summer and reached the most depleted values of − 35 ‰ at higher fractions of natural gas combustion, as well as the most enriched values of − 17 ‰ to − 12 ‰ when impacted by cement production emissions. Observation-based δ13Cm values were derived independently from the simulations by a moving Keeling-plot approach. While model-based estimates spread in a narrow range, observation-based δ13Cm values exhibited a larger scatter and were limited to a smaller number of data points due to the stringent analysis prerequisites.

PAPILA dataset: a regional emission inventory of reactive gases for South America based on the combination of local and global information

Abstract. The multidisciplinary project Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) is dedicated to the development and implementation of an air quality analysis and forecasting system to assess pollution impacts on human health and economy. In this context, a comprehensive emission inventory for South America was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGAR v4.3.2 historical data), enriching it with data derived from locally available emission inventories for Argentina, Chile, and Colombia. This work presents the results of the first joint effort of South American researchers and European colleagues to generate regional maps of emissions, together with a methodological approach to continue incorporating information into future versions of the dataset. This version of the PAPILA dataset includes CO, NOx, NMVOCs, NH3, and SO2 annual emissions from anthropogenic sources for the period 2014–2016, with a spatial resolution of 0.1∘ × 0.1∘ over a domain that covers 32–120∘ W and 34∘ N–58∘ S. The PAPILA dataset is presented as netCDF4 files and is available in an open-access data repository under a CC-BY 4 license: https://doi.org/10.17632/btf2mz4fhf.3 (Castesana et al., 2021). A comparative assessment of PAPILA–CAMS datasets was carried out for (i) the South American region, (ii) the countries with local data (Argentina, Colombia, and Chile), and (iii) downscaled emission maps for urban domains with different environmental and anthropogenic factors. Relevant differences were found at both country and urban levels for all the compounds analyzed. Among them, we found that when comparing PAPILA total emissions versus CAMS datasets at the national level, higher levels of NOx and considerably lower levels of the other species were obtained for Argentina, higher levels of SO2 and lower levels of CO and NOx for Colombia, and considerably higher levels of CO, NMVOCs, and SO2 for Chile. These discrepancies are mainly related to the representativeness of local practices in the local emission estimates, to the improvements made in the spatial distribution of the locally estimated emissions, or to both. Both datasets were evaluated against surface concentrations of CO and NOx by using them as input data to the WRF-Chem model for one of the analyzed domains, the metropolitan area of Buenos Aires, for summer and winter of 2015. PAPILA-based modeling results had a smaller bias for CO and NOx concentrations in winter while CAMS-based results for the same period tended to deliver an underestimation of these concentrations. Both inventories exhibited similar performances for CO in summer, while the PAPILA simulation outperformed CAMS for NOx concentrations. These results highlight the importance of refining global inventories with local data to obtain accurate results with high-resolution air quality models.

An integrated analysis of contemporary methane emissions and concentration trends over China using in situ and satellite observations and model simulations

Abstract. China, being one of the major emitters of greenhouse gases, has taken strong actions to tackle climate change, e.g., to achieve carbon neutrality by 2060. It also becomes important to better understand the changes in the atmospheric mixing ratios and emissions of CH4, the second most important human-influenced greenhouse gas, in China. Here we analyze the sources contributing to the atmospheric CH4 mixing ratios and their trends in China over 2007–2018 using the GEOS-Chem model simulations driven by two commonly used global anthropogenic emission inventories: the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) and the Community Emissions Data System (CEDS). The model results are interpreted with an ensemble of surface, aircraft, and satellite observations of CH4 mixing ratios over China and the Pacific region. The EDGAR and CEDS estimates show considerable differences reflecting large uncertainties in estimates of Chinese CH4 emissions. Chinese CH4 emission estimates based on EDGAR and natural sources increase from 46.7 Tg per annum (Tg a−1) in 1980 to 69.8 Tg a−1 in 2012 with an increase rate of 0.7 Tg a−2, and estimates with CEDS increase from 32.9 Tg a−1 in 1980 and 76.7 Tg a−1 in 2014 (a much stronger trend of 1.3 Tg a−2 over the period). Both surface, aircraft, and satellite measurements indicate CH4 increase rates of 7.0–8.4 ppbv a−1 over China in the past decade. We find that the model simulation using the CEDS inventory and interannually varying OH levels can best reproduce these observed CH4 mixing ratios and trends over China. Model results over China are sensitive to the global OH level, with a 10 % increase in the global tropospheric volume-weighted mean OH concentration presenting a similar effect to that of a 47 Tg a−1 decrease in global CH4 emissions. We further apply a tagged tracer simulation to quantify the source contributions from different emission sectors and regions. We find that domestic CH4 emissions account for 14.0 % of the mean surface mixing ratio and drive 66.7 % of the surface trend (mainly via the energy sector) in China over 2007–2018. We emphasize that intensive CH4 measurements covering eastern China will help us better assess the driving factors of CH4 mixing ratios and support the emission mitigation in China.

Estimating lightning NOx production over South Africa

Nitrogen oxides (NOx = NO + NO2) are toxic air pollutants and play a significant role in tropospheric chemistry. Global NOx hotspots are the industrialised regions of the USA, Europe, Middle East, East Asia and eastern parts of South Africa. Lightning is one of the many natural and anthropogenic sources of NOx to the troposphere. It plays a role in the formation of particulate matter and tropospheric ozone, which are both linked to harmful health and climate effects. The discourse on NOx over the southern African continent has mainly focused on anthropogenic sources. However, lightning is known to be a main source of tropospheric NOx globally. It is therefore important to understand its contribution to the national and global NOx budget. Data from the South African Lightning Detection Network were used to approximate the influence of lightning on the NOx load over the country, and to develop a gridded data set of lightning-produced NOx (LNOx) emissions for the period 2008 2015. The Network monitors cloud-toground lightning strikes; and theoretically has a detection efficiency of 90% and a location accuracy of 0.5 km. An emission factor of 11.5 kg NO2/flash was employed to calculate the LNOx budget of ~270 kt NO2/year. The calculated LNOx was 14% of the total NOx emission estimates published in the EDGAR v4.2 data set for the year 2008. The LNOx emission inventory will improve model performance and prediction, and enhance the understanding of the contribution of lightning to ambient NO2. Significance: The results show that both lightning and industrial NOx sources are essential in evaluating NOx and tropospheric O3 chemistry over South Africa. As a result they should both be considered in air quality modelling, to assist in air quality management planning. LNOx emissions are projected to increase with climate change, which may lead to an increase in tropospheric O3. Thus it is important to have an LNOx inventory, to be used as input into air quality modelling, as it will improve model performance and forecasting, and the understanding of the sensitivity of ambient pollution to changes in lightning emission. It will further inform chemical transport modelling so that the contribution of both natural and anthropogenic sources can be better understood.

A New Divergence Method to Quantify Methane Emissions Using Observations of Sentinel‐5P TROPOMI

AbstractWe present a new divergence method to estimated methane (CH4) emissions from satellite observed mean mixing ratio of methane (XCH4) by deriving the regional enhancement of XCH4 in the Planetary Boundary Layer (PBL). The applicability is proven by comparing the estimated emissions with its known emission inventory from a 3‐month GEOS‐Chem simulation. When applied to TROPOspheric Monitoring Instrument observations, sources from well‐known oil/gas production areas, livestock farms and wetlands in Texas become clearly visible in the emission maps. The calculated yearly averaged total CH4 emission over the Permian Basin is 3.06 (2.82, 3.78) Tg a−1 for 2019, which is consistent with previous studies and double that of EDGAR v4.3.2 for 2012. Sensitivity tests on PBL heights, on the derived regional background and on wind speeds suggest our divergence method is quite robust. It is also a fast and simple method to estimate the CH4 emissions globally.

Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments

Abstract. We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of methane sources for 2019 at 2∘ × 2.5∘ horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals and to a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT and regional biases of 6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding 20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses an analytical solution to the Bayesian inference of methane sources, thus providing an explicit characterization of error statistics and information content together with the solution. TROPOMI has ∼ 100 times more observations than GOSAT, but error correlation on the 2∘ × 2.5∘ scale of the inversion and large spatial inhomogeneity in the number of observations make it less useful than GOSAT for quantifying emissions at that scale. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI and GOSAT inversions show consistent downward adjustments of global oil–gas emissions relative to a prior estimate based on national inventory reports to the United Nations Framework Convention on Climate Change but consistent increases in the south-central US and in Venezuela. Global emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We find large artifacts in the TROPOMI inversion over southeast China, where seasonal rice emissions are particularly high but in phase with extensive cloudiness and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the global scale.

Anthropogenic and natural controls on atmospheric <i>δ</i><sup>13</sup>C-CO<sub>2</sub> variations in the Yangtze River delta: insights from a carbon isotope modeling framework

Abstract. The atmospheric carbon dioxide (CO2) mixing ratio and its carbon isotope (δ13C-CO2) composition contain important CO2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO2 and δ13C-CO2 can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13C-CO2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO2 and δ13C-CO2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO2 budget and investigate the main factors that dominated δ13C-CO2 variations for the Yangtze River delta (YRD) region, one of the largest anthropogenic CO2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v4.3.2 CO2 inventories to simulate hourly CO2 mixing ratios and δ13C-CO2, evaluated these simulations with observations, and constrained the total anthropogenic CO2 emission. We show that (1) top-down and bottom-up estimates of anthropogenic CO2 emissions agreed well (bias < 6 %) on an annual basis, (2) the WRF-STILT model can generally reproduce the observed diel and seasonal atmospheric δ13C-CO2 variations, and (3) anthropogenic CO2 emissions played a much larger role than ecosystems in controlling the δ13C-CO2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13C-CO2 seasonality increased from 1.53 ‰ to 1.66 ‰. (4) Atmospheric transport processes in summer amplified the cement CO2 enhancement proportions in the YRD area, which dominated monthly δs (the mixture of δ13C-CO2 from all regional end-members) variations. These findings show that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities.

Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling

Abstract. Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448 and 720 kt CH4 yr−1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2), respectively, make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g., single facilities) to regional-scale (e.g., agglomerations) sources in the context of the CoMet 1.0 campaign in early summer 2018. A quantum cascade–interband cascade laser (QCL–ICL)-based spectrometer adapted for airborne research was deployed aboard the German Aerospace Center (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility-level estimates. Emission rates from individual sources not only are needed for unambiguous comparisons between bottom-up and top-down inventories but also become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional-scale and facility-level emission estimates. We find instantaneous coal mine emission estimates of 451/423 ± 77/79 kt CH4 yr−1 for the morning/afternoon flight of 6 June 2018. The derived fuel-exploitation emission rates coincide (±6 %) with annual-average inventorial data from E-PRTR 2017 although they are distinctly lower (−28 %/−32 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories.

Wintertime Nitrous Oxide Emissions in the San Joaquin Valley of California Estimated from Aircraft Observations.

Nitrous oxide (N2O) is a long-lived greenhouse gas that also destroys stratospheric ozone. N2O emissions are uncertain and characterized by high spatiotemporal variability, making individual observations difficult to upscale, especially in mixed land use source regions like the San Joaquin Valley (SJV) of California. Here, we calculate spatially integrated N2O emission rates using nocturnal and convective boundary-layer budgeting methods. We utilize vertical profile measurements from the NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign, which took place January-February, 2013. For empirical constraints on N2O source identity, we analyze N2O enhancement ratios with methane, ammonia, carbon dioxide, and carbon monoxide separately in the nocturnal boundary layer, nocturnal residual layer, and convective boundary layer. We find that an established inventory (EDGAR v4.3.2) underestimates N2O emissions by at least a factor of 2.5, that wintertime emissions from animal agriculture are important to annual totals, and that there is evidence for higher N2O emissions during the daytime than at night.

Quantifying burning efficiency in megacities using the NO2∕CO ratio from the Tropospheric Monitoring Instrument (TROPOMI)

Abstract. This study investigates the use of co-located nitrogen dioxide (NO2) and carbon monoxide (CO) retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors (EFs) over the megacities of Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles. Efficient combustion is characterized by high NOx (NO+NO2) and low CO emissions, making the NO2∕CO ratio a useful proxy for combustion efficiency (CE). The local enhancement of CO and NO2 above megacities is well captured by TROPOMI at short averaging times compared with previous satellite missions. In this study, the upwind background and plume rotation methods are used to investigate the accuracy of satellite-derived ΔNO2∕ΔCO ratios. The column enhancement ratios derived using these two methods vary by 5 % to 20 % across the selected megacities. TROPOMI-derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3.2 (Emission Database for Global Atmospheric Research v4.3.2) and the MACCity (Monitoring Atmospheric Chemistry and Climate and CityZen) 2018 emission inventories. TROPOMI correlates strongly (r=0.85 and 0.7) with EDGAR and MACCity, showing the highest emission ratio for Riyadh and lowest emission ratio for Lahore. However, inventory-derived emission ratios are 60 % to 85 % higher than TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and the different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI-retrieved column enhancement ratios into corresponding emission ratios, thereby accounting for these influences. Except for Los Angeles and Lahore, TROPOMI-derived emission ratios are close (within 10 % to 25 %) to MACCity values. For EDGAR, however, emission ratios are ∼65 % higher for Cairo and 35 % higher for Riyadh. For Los Angeles, EDGAR and MACCity are a factor of 2 and 3 higher than TROPOMI respectively. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. For Mexico City and Los Angeles, these measurements are consistent with TROPOMI-derived emission ratios, demonstrating the potential of TROPOMI with respect to monitoring burning efficiency.

A global gridded (0.1° × 0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change

Abstract. Individual countries report national emissions of methane, a potent greenhouse gas, in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). We present a global inventory of methane emissions from oil, gas, and coal exploitation that spatially allocates the national emissions reported to the UNFCCC (Scarpelli et al., 2019). Our inventory is at 0.1∘×0.1∘ resolution and resolves the subsectors of oil and gas exploitation, from upstream to downstream, and the different emission processes (leakage, venting, flaring). Global emissions for 2016 are 41.5 Tg a−1 for oil, 24.4 Tg a−1 for gas, and 31.3 Tg a−1 for coal. An array of databases is used to spatially allocate national emissions to infrastructure, including wells, pipelines, oil refineries, gas processing plants, gas compressor stations, gas storage facilities, and coal mines. Gridded error estimates are provided in normal and lognormal forms based on emission factor uncertainties from the IPCC. Our inventory shows large differences with the EDGAR v4.3.2 global gridded inventory both at the national scale and in finer-scale spatial allocation. It shows good agreement with the gridded version of the United Kingdom's National Atmospheric Emissions Inventory (NAEI). There are significant errors on the 0.1∘×0.1∘ grid associated with the location and magnitude of large point sources, but these are smoothed out when averaging the inventory over a coarser grid. Use of our inventory as prior estimate in inverse analyses of atmospheric methane observations allows investigation of individual subsector contributions and can serve policy needs by evaluating the national emissions totals reported to the UNFCCC. Gridded data sets can be accessed at https://doi.org/10.7910/DVN/HH4EUM (Scarpelli et al., 2019).

Development of a spatialized atmospheric emission inventory for the main industrial sources in Brazil.

In this work, atmospheric pollutant emissions of NOx, SOx, CO, particulate matter (PM), total organic compounds (TOC), and CO2 from larger stationary sources of pollutants in Brazil were inventoried and spatialized over the whole Brazilian territory for the base year 2011. The developed inventory comprises a total of 16 refining units, 1730 thermoelectric power plants (TPPs), 96 cement industries, and 64 paper and cellulose industries. To obtain the dataset, some strategies were used, including mail contact, official datasets, personal requesting, web maps usage, and official industry websites. The emission factors were based on lower and upper limits proposed by the AP-42 standards of the US Environmental Protection Agency - USEPA, as well as, emission factors provided by air pollution control agencies, industries, and those identified in the scientific literature. The results show values of 857 ± 415Gg/year for NOx, 1.51 ± 1.23Tg/year for SOx, 21.2 ± 13.7Tg/year for CO, 10.4 ± 10.1Tg/year for PM, 1.14 ± 0.95Tg/year for TOC, and 476 ± 142Tg/year for CO2. In comparison with the official vehicular emission inventory provided by the Ministry of Environment for the year 2011, the total NOx emissions estimated in this work were slightly lower than vehicular emissions, while SOx was 300 times greater than vehicular emissions. For CO, the stationary emissions inventoried were around 17 times greater than vehicular emissions, while PM was approximately 360 times greater than those from vehicles. In terms of comparison with existing global databases, the estimates of this work showed a good level of agreement with the pollutants estimated by the Global Emissions EDGAR v4.3.1, except for PM and CO, which were higher in our estimates. The major contribution of the proposed inventory lies in its improved spatialized distribution, higher resolution, and greater distinctness about the high level of uncertainty associated with the emission inventories for the region.

Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

Characterisation of methane sources in Lutjewad, The Netherlands, using quasi-continuous isotopic composition measurements

Despite the importance of methane for climate change mitigation, uncertainties regarding the temporal and spatial variability of the emissions remain. Measurements of CH4 isotopic composition are used to partition the relative contributions of different emission sources. We report continuous isotopic measurements during 5 months at the Lutjewad tower (north of the Netherlands). Time-series of χ(CH4), δ13C-CH4, and δD-CH4 in ambient air were analysed using the Keeling plot method. Resulting source signatures ranged from −67.4 to −52.4‰ vs V-PDB and from −372 to −211‰ vs V-SMOW, for δ13C and δD respectively, indicating a prevalence of biogenic sources. Analysis of isotope and wind data indicated that (i) emissions from off-shore oil and gas platforms in the North Sea were not detected during this period, (ii) CH4 from fossil fuel related sources was usually advected from the east, pointing towards the Groningen gas field or regions further east in Germany. The results from two atmospheric transport models, CHIMERE and FLEXPART-COSMO, using the EDGAR v4.3.2 and TNO-MACC III emission inventories, reproduce χ(CH4) variations relatively well, but the isotope signatures were over-estimated by the model compared to the observations. Accounting for geographical variations of the δ13C signatures from fossil fuel emissions improved the model results significantly. The difference between model and measured isotopic signatures was larger when using TNO-MACC III compared to EDGAR v4.3.2 inventory. Uncertainties in the isotope signatures of the sources could explain a significant fraction of the discrepancy, thus a better source characterisation could further strengthen the use of isotopes in constraining emissions.

Anthropogenic VOCs in Abidjan, southern West Africa: from source quantification to atmospheric impacts

Abstract. Several field campaigns were conducted in the framework of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project to measure a broad range of atmospheric constituents. Here we present the analysis of an unprecedented and comprehensive dataset integrating up to 56 volatile organic compounds (VOCs) from ambient sites and emission sources. VOCs were collected on multi-sorbent tubes in the coastal city of Abidjan, Côte d'Ivoire, in winter and summer 2016 and later analysed by gas chromatography coupled with flame ionization and mass spectrometer detectors (GC-FID and GC-MS) at the laboratory. The comparison between VOC emission source profiles and ambient profiles suggests the substantial impact of two-stroke motorized two-wheel vehicles and domestic fires on the composition of Abidjan's atmosphere. However, despite high VOC concentrations near-source, moderate ambient levels were observed (by factors of 10 to 4000 lower), similar to the concentrations observed in northern mid-latitude urban areas. Besides photochemistry, the reported high wind speeds seem to be an essential factor that regulates air pollution levels in Abidjan. Emission ratios (ΔVOC∕ΔCO) were established based on real-world measurements achieved for a selected number of representative combustion sources. Maximum measured molar mass emissions were observed from two-wheel vehicles, surpassing other regional sources by 2 orders of magnitude. Local practices like waste burning also make a significant contribution to VOC emissions, higher than those from light-duty vehicles by 1.5 to 8 orders of magnitude. These sources also largely govern the VOC's atmospheric impacts in terms of OH reactivity, secondary organic aerosol formation (SOAP), and photochemical ozone creation potential (POCP). While the contribution of aromatics dominates the atmospheric impact, our measurements reveal the systematic presence of anthropogenic terpenoids in all residential combustion sectors. Finally, emission factors were used to retrieve and quantify VOC emissions from the main anthropogenic source sectors at the national level. Our detailed estimation of VOC emissions suggests that the road transport sector is the dominant source in Côte d'Ivoire, emitting around 1200 Gg yr−1 of gas-phase VOCs. These new estimates are 100 and 160 times larger than global inventory estimations from MACCity or EDGAR (v4.3.2), respectively. Additionally, the residential sector is largely underestimated in the global emission inventories, by factors of 13 to 43. Considering only Côte d'Ivoire, these new estimates for VOCs are 3 to 6 times higher than the whole of Europe. Given the significant underestimation of VOC emissions from the transport and residential sectors in Côte d'Ivoire, there is an urgent need to build more realistic and region-specific emission inventories for the entire West African region. This might be true not only for VOCs, but also for all atmospheric pollutants. The lack of waste burning, wood fuel burning and charcoal burning, and fabrication representation in regional inventories also needs to be addressed, particularly in low-income areas where these types of activities are ubiquitous sources of VOC emissions.

Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015

Abstract. We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used, even when prior error standard deviation distributions are lognormal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil and gas emissions in the EDGAR v4.3.2 inventory but little error in the United States where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil and gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC), and our inversion is generally more consistent with the UNFCCC data. The observed 2010–2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8±0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

Carbon monoxide air pollution on sub-city scales and along arterial roads detected by the Tropospheric Monitoring Instrument

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite provides measurements of carbon monoxide (CO) total column concentrations based on earthshine radiance measurements in the 2.3 µm spectral range with a spatial resolution of 7 km×7 km and daily global coverage. Due to the high accuracy of the observations, CO pollution can be detected over cities and industrial areas using single orbit overpasses. In this study, we analyzed local CO enhancements in an area around Iran from 1 November to 20 December 2017. We employed the Weather Research and Forecasting (WRF) model v3.8.1 using the EDGAR v4.2 emission inventory and evaluated CO emissions from the cities of Tehran, Yerevan, Urmia, and Tabriz on a spatial resolution comparable to that of TROPOMI. For background conditions, the WRF simulation agrees well with TROPOMI CO, with a mean difference of 5.7 %. However, the emissions for the city area had to be significantly increased in order to match the observations. Moreover, significant differences at the sub-city scale remain. To match the TROPOMI CO observations around the Armenian city of Yerevan, it is necessary to introduce CO emissions along a southeast arterial road of Yerevan. Overall, this hints at deficits in the EDGAR inventory in the region around Iran and indicates TROPOMI's capability to identify localized CO pollution on sub-city scales, which at the same time challenges current atmospheric modeling at high spatial and temporal resolution.

Time variations in the concentration of pollutants in the atmosphere over Moscow and estimation of their emissions

Data on the surface NO, NO2, CO, SO2, CH4, and PM10 concentrations measured at 46 stations from 2005 to 2014 in Moscow have been used in studying the characteristic features of their distribution over the city area and their time variations. Both seasonal and interannual variations in pollutant concentrations, as well as their 10-year trends, are closely related with variations in the urban infrastructure and weather conditions. The weekly cycle of the pollutants has been analyzed. Its largest amplitude is 18.9 ± 5.6% for NO. For CO, NO2, and PM10, the amplitudes amount to 9.3 ± 3.2%, 13.6 ± 2.8%, and 10.9 ± 5.5%, respectively. The weekly variations in CH4 and SO2 concentrations are not significant for such large-scale territorial averaging. The emission fluxes of CO, NOx, SO2, and CH4 and their integral emissions from the Moscow megacity have been estimated from multiyear measurements of their surface concentrations and both vertical air-temperature and wind stratifications. During 2005—2014, the annual integral emissions of CO, NOx, and CH4 decreased with a rate of -1.9 ± 0.3, -1.7 ± 0.4, and -7.8 ± 3.1 %·yr-1, respectively, and that of SO2 increased with a rate of 3.3 ± 2.3 %·yr-1. The means of integral annual pollutant emissions from Moscow differ slightly from those for other world megacities. The CO emissions coincide with their EDGAR v4.2 inventory values interpolated to the territory of Moscow. However, the EDGAR v4.2 values of NOx, SO2 and CH4 significantly exceed their calculated values.

Methane budget estimates in Finland from the CarbonTracker Europe-CH4 data assimilation system

We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation , bias ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (11 in this case) optimization is possible with a dense observation network.