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To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at https://doi.org/10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and https://doi.org/10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b).

Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory. The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors. At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors). The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.

Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1∘ × 0.1∘) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).

Quantification of land surface–atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926 ± 13 Tg C yr−1, while eight other BU sources report a mean value of 948 [937,961] Tg C yr−1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for −91 ± 32 Tg C yr−1, while six other BU approaches reported a mean sink of −62 [-117,-49] Tg C yr−1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported −69 [-152,-5] Tg C yr−1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of −73 Tg C yr−1 has a slightly smaller spread (0th–100th percentiles of [-135,+45] Tg C yr−1), and it was calculated after removing net land–atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (−0.61) Tg C yr−2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr−1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.8148461 (McGrath et al., 2023).

This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the global and regional atmospheric modelling community's need of a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3, an ad hoc global mosaic of anthropogenic inventories, has been developed by integrating official inventories over specific areas (North America, Europe, Asia including Japan and South Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO2, NOx, CO, non-methane volatile organic compounds (NMVOCs), NH3, PM10, PM2.5, black carbon (BC), and organic carbon (OC), with a spatial resolution of 0.1∘ × 0.1∘ and time intervals of months and years, covering the period 2000–2018 (https://doi.org/10.5281/zenodo.7516361, Crippa, 2023, https://edgar.jrc.ec.europa.eu/dataset_htap_v3, last access: June 2023). The emissions are further disaggregated into 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past 2 decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policymaking, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

An updated and expanded representation of organics in the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) has been evaluated. First, the comprehensive Mainz Organic Mechanism (MOM) in the submodel MECCA (Module Efficiently Calculating the Chemistry of the Atmosphere) was activated with explicit degradation of organic species up to five carbon atoms and a simplified mechanism for larger molecules. Second, the ORACLE submodel (version 1.0) now considers condensation on aerosols for all organics in the mechanism. Parameterizations for aerosol yields are used only for the lumped species that are not included in the explicit mechanism. The simultaneous usage of MOM and ORACLE allows an efficient estimation of not only the chemical degradation of the simulated volatile organic compounds but also the contribution of organics to the growth and fate of (organic) aerosol, with the complexity of the mechanism largely increased compared to EMAC simulations with more simplified chemistry. The model evaluation presented here reveals that the OH concentration is reproduced well globally, whereas significant biases for observed oxygenated organics are present. We also investigate the general properties of the aerosols and their composition, showing that the more sophisticated and process-oriented secondary aerosol formation does not degrade the good agreement of previous model configurations with observations at the surface, allowing further research in the field of gas–aerosol interactions.

The EU, seeking to be a global leader in the fight against climate change, is moving ahead with ambitious policies to mitigate greenhouse gases emissions. In this context, the Fit for 55 package (FF55) is a set of proposals to revise and update EU legislation, to ensure that policies are in line with the climate goals of cutting emissions by at least 55% by 2030. Whilst these policies are designed for climate purposes, they will have positive side-effects (co-benefits) on air quality. Separately, additional policies are also in place to reduce emissions of related air pollutants and to improve air quality concentrations on EU territory. In this work, through a modelling study, we analyse the benefits of these policies via the health benefits arising from the resulting reductions in yearly average PM2.5 concentrations. Results are analysed by assessing and comparing morbidity and mortality impacts as computed using both the HRAPIE (Health risks of air pollution in Europe, WHO, as implemented in the CaRBonH model) and the GBD (Global Burden of Disease, as implemented in FASST-GBD model) approaches. Even when considering the uncertainty and variability in the results obtained using the two approaches, it is clear that EU policies can bring health and economic benefit in EU, with several Billions of Euro of benefits both in terms of morbidity and mortality indicators.

To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns, and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under https://doi.org/10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b).

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011–2015, the CO2 land sources and sinks from NGHGI estimates report −90 Tg C yr−1 ± 30 Tg C yr−1 while all other BU approaches report a mean sink of −98 Tg C yr−1 (± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr−1 ± 400 Tg C yr−1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO2 flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).

The Emissions Database for Global Atmospheric Research (EDGAR) estimates the human-induced emission rates on Earth. EDGAR collaborates with atmospheric modelling activities and aids policy in the design of mitigation strategies and in evaluating their effectiveness. In these applications, the uncertainty estimate is an essential component, as it quantifies the accuracy and qualifies the level of confidence in the emission. This study complements the EDGAR emissions inventory by providing an estimation of the structural uncertainty stemming from its base components (activity data, AD, statistics and emission factors, EFs) by (i) associating uncertainty to each AD and EF characterizing the emissions of the three main greenhouse gases (GHGs), namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O); (ii) combining them; and (iii) making assumptions regarding the cross-country uncertainty aggregation of source categories. It was deemed a natural choice to obtain the uncertainties in EFs and AD statistics from the Intergovernmental Panel on Climate Change (IPCC) guidelines issued in 2006 (with a few exceptions), as the EF and AD sources and methodological aspects used by EDGAR have been built over the years based on the IPCC recommendations, which assured consistency in time and comparability across countries. On the one hand, the homogeneity of the method is one of the key strengths of EDGAR, on the other hand, it facilitates the propagation of uncertainties when similar emission sources are aggregated. For this reason, this study aims primarily at addressing the aggregation of uncertainties' sectorial emissions across GHGs and countries. Globally, we find that the anthropogenic emissions covered by EDGAR for the combined three main GHGs for the year 2015 are accurate within an interval of −15 % to +20 % (defining the 95 % confidence of a log-normal distribution). The most uncertain emissions are those related to N2O from waste and agriculture, while CO2 emissions, although responsible for 74 % of the total GHG emissions, account for approximately 11 % of global uncertainty share. The sensitivity to methodological choices is also discussed.

Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH4 emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive comparison of China's anthropogenic CH4 emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH4 yr−1 in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH4 emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al., 2021).

A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBCtr) and the explained variation of m/z 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of m/z 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µg m−3 (7.8 %–9.0 %) and 0.7–1.2 µg m−3 (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µg m−3, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µg m−3, or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µg m−3, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µg m−3 (26.5 %) and 2.2 µg m−3 (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µg m−3 (3.4 %–15.9 %), from 0.6 to 2.2 µg m−3 (7.7 %–33.7 %) and from 0.9 to 3.1 µg m−3 (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30 %, ±11 %, ±25 % and ±12 %, respectively.

Global anthropogenic emission inventories remain vital for understanding the sources of atmospheric pollution and the associated impacts on the environment, human health, and society. Rapid changes in today's society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NOx; CO; SO2; NH3; non-methane volatile organic compounds, NMVOCs; black carbon, BC; organic carbon, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes; on-road and non-road transportation; separate residential, commercial, and other sectors (RCO); waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel, the sum of liquid-fuel and natural-gas combustion, and remaining process-level emissions). The CEDSGBD-MAPS inventory additionally includes monthly global gridded (0.5∘ × 0.5∘) emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system models. CEDSGBD-MAPS utilizes updated activity data, updates to the core CEDS default scaling procedure, and modifications to the final procedures for emissions gridding and aggregation. Relative to the previous CEDS inventory (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDSGBD-MAPS inventory provides the most contemporary global emission estimates to date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel types and source sectors. Dominant sources of global NOx and SO2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors as well as on-road transportation and international shipping for NOx. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), and emissions from the waste sector. Global emissions of NOx, SO2, CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH3 and NMVOCs continuously increase between 1970 and 2017, with agriculture as a major source of global NH3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDSGBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories. The CEDSGBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS (last access: 1 December 2020). The CEDSGBD-MAPS emission inventory dataset (both annual country-total and monthly global gridded files) is publicly available under https://doi.org/10.5281/zenodo.3754964 (McDuffie et al., 2020c).

China's fossil-fuel CO2 (FFCO2) emissions accounted for approximately 28 % of the global total FFCO2 in 2016. An accurate estimate of China's FFCO2 emissions is a prerequisite for global and regional carbon budget analyses and the monitoring of carbon emission reduction efforts. However, significant uncertainties and discrepancies exist in estimations of China's FFCO2 emissions due to a lack of detailed traceable emission factors (EFs) and multiple statistical data sources. Here, we evaluated China's FFCO2 emissions from nine published global and regional emission datasets. These datasets show that the total emissions increased from 3.4 (3.0–3.7) in 2000 to 9.8 (9.2–10.4) Gt CO2 yr−1 in 2016. The variations in these estimates were largely due to the different EF (0.491–0.746 t C per t of coal) and activity data. The large-scale patterns of gridded emissions showed a reasonable agreement, with high emissions being concentrated in major city clusters, and the standard deviation mostly ranged from 10 % to 40 % at the provincial level. However, patterns beyond the provincial scale varied significantly, with the top 5 % of the grid level accounting for 50 %–90 % of total emissions in these datasets. Our findings highlight the significance of using locally measured EF for Chinese coal. To reduce uncertainty, we recommend using physical CO2 measurements and use these values for dataset validation, key input data sharing (e.g., point sources), and finer-resolution validations at various levels.

Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded datasets produced for use by the modeling community, particularly for the Coupled Model Intercomparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios was coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-methane volatile organic compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work.

Anthropogenic aerosols have increased significantly since the industrial revolution, driven largely by growth in emissions from energy use in sectors including power generation, industry, and transport. Advances in emission control technologies since around 1970, however, have partially counteracted emissions increases from the above sectors. Using the fully coupled Community Earth System Model, we quantify the effective radiative forcing (ERF) and climate response to 1970–2010 aerosol changes associated with the above two policy-relevant emission drivers. Emissions from energy-use growth generate a global mean aerosol ERF (mean ± 1 standard deviation) of -0.31±0.22 W m−2 and result in a global mean cooling (-0.35±0.17 K) and a precipitation reduction (-0.03±0.02 mm d−1). By contrast, the avoided emissions from advances in emission control technology, which benefit air quality, generate a global mean ERF of +0.21±0.23 W m−2, a global warming of +0.10±0.13 K, and global mean precipitation increase of +0.01±0.02 mm d−1. Despite the relatively small changes in global mean precipitation, these two emission drivers have profound impacts at regional scales, in particular over Asia and Europe. The total net aerosol impacts on climate are dominated by energy-use growth, from Asia in particular. However, technology advances outweigh energy-use growth over Europe and North America. Various non-linear processes are involved along the pathway from aerosol and their precursor emissions to radiative forcing and ultimately to climate responses, suggesting that the diagnosed aerosol forcing and effects must be interpreted in the context of experiment designs. Further, the temperature response per unit aerosol ERF varies significantly across many factors, including location and magnitude of emission changes, implying that ERF, and the related metrics, needs to be used very carefully for aerosols. Future aerosol-related emission pathways have large temporal and spatial uncertainties; our findings provide useful information for both assessing and interpreting such uncertainties, and they may help inform future climate change impact reduction strategies.

The Emissions Database for Global Atmospheric Research (EDGAR) compiles anthropogenic emissions data for greenhouse gases (GHGs), and for multiple air pollutants, based on international statistics and emission factors. EDGAR data provide quantitative support for atmospheric modelling and for mitigation scenario and impact assessment analyses as well as for policy evaluation. The new version (v4.3.2) of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of the European Union's first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol, KP). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emissions of multiple chemical compounds, GHGs as well as air pollutants, from relevant sources (fossil fuel activities but also, for example, fermentation processes in agricultural activities) are compiled following a bottom-up (BU), transparent and IPCC-compliant methodology. This paper describes EDGAR v4.3.2 developments with respect to three major long-lived GHGs (CO2, CH4, and N2O) derived from a wide range of human activities apart from the land-use, land-use change and forestry (LULUCF) sector and apart from savannah burning; a companion paper quantifies and discusses emissions of air pollutants. Detailed information is included for each of the IPCC-relevant source sectors, leading to global totals for 2010 (in the middle of the first KP commitment period) (with a 95 % confidence interval in parentheses): 33.6(±5.9) Pg CO2 yr−1, 0.34(±0.16) Pg CH4 yr−1, and 7.2(±3.7) Tg N2O yr−1. We provide uncertainty factors in emissions data for the different GHGs and for three different groups of countries: OECD countries of 1990, countries with economies in transition in 1990, and the remaining countries in development (the UNFCCC non-Annex I parties). We document trends for the major emitting countries together with the European Union in more detail, demonstrating that effects of fuel markets and financial instability have had greater impacts on GHG trends than effects of income or population. These data (https://doi.org/10.5281/zenodo.2658138, Janssens-Maenhout et al., 2019) are visualised with annual and monthly global emissions grid maps of 0.1∘×0.1∘ for each source sector.

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

This paper describes, documents, and validates the TM5-FAst Scenario Screening Tool (TM5-FASST), a global reduced-form air quality source–receptor model that has been designed to compute ambient pollutant concentrations as well as a broad range of pollutant-related impacts on human health, agricultural crop production, and short-lived pollutant climate metrics, taking as input annual pollutant emission data aggregated at the national or regional level. The TM5-FASST tool, providing a trade-off between accuracy and applicability, is based on linearized emission-concentration sensitivities derived with the full chemistry-transport model TM5. The tool has been extensively applied in various recent critical studies. Although informal and fragmented validation has already been performed in various publications, this paper provides a comprehensive documentation of all components of the model and a validation against the full TM5 model. We find that the simplifications introduced in order to generate immediate results from emission scenarios do not compromise the validity of the output and as such TM5-FASST is proven to be a useful tool in science-policy analysis. Furthermore, it constitutes a suitable architecture for implementing the ensemble of source–receptor relations obtained in the frame of the HTAP modelling exercises, thus creating a link between the scientific community and policy-oriented users.

The new version of the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) compiles gaseous and particulate air pollutant emissions, making use of the same anthropogenic sectors, time period (1970–2012), and international activity data that is used for estimating GHG emissions, as described in a companion paper (Janssens-Maenhout et al., 2017). All human activities, except large scale biomass burning and land use, land-use change, and forestry are included in the emissions calculation. The bottom-up compilation methodology of sector-specific emissions was applied consistently for all world countries, providing methodological transparency and comparability between countries. In addition to the activity data used to estimate GHG emissions, air pollutant emissions are determined by the process technology and end-of-pipe emission reduction abatements. Region-specific emission factors and abatement measures were selected from recent available scientific literature and reports. Compared to previous versions of EDGAR, the EDGAR v4.3.2 dataset covers all gaseous and particulate air pollutants, has extended time series (1970–2012), and has been evaluated with quality control and quality assurance (QC and QA) procedures both for the emission time series (e.g. particulate matter – PM – mass balance, gap-filling for missing data, the split-up of countries over time, few updates in the emission factors, etc.) and grid maps (full coverage of the world, complete mapping of EDGAR emissions with sector-specific proxies, etc.). This publication focuses on the gaseous air pollutants of CO, NOx, SO2, total non-methane volatile organic compounds (NMVOCs), NH3, and the aerosols PM10, PM2.5, black carbon (BC), and organic carbon (OC). Considering the 1970–2012 time period, global emissions of SO2 increased from 99 to 103 Mt, CO from 441 to 562 Mt, NOx from 68 to 122 Mt, NMVOC from 119 to 170 Mt, NH3 from 25 to 59 Mt, PM10 from 37 to 65 Mt, PM2.5 from 24 to 41 Mt, BC from 2.7 to 4.5 Mt, and OC from 9 to 11 Mt. We present the country-specific emission totals and analyze the larger emitting countries (including the European Union) to provide insights on major sector contributions. In addition, per capita and per GDP emissions and implied emission factors – the apparent emissions per unit of production or energy consumption – are presented. We find that the implied emission factors (EFs) are higher for low-income countries compared to high-income countries, but in both cases decrease from 1970 to 2012. The comparison with other global inventories, such as the Hemispheric Transport of Air Pollution Inventory (HTAP v2.2) and the Community Emission Data System (CEDS), reveals insights on the uncertainties as well as the impact of data revisions (e.g. activity data, emission factors, etc.). As an additional metric, we analyze the emission ratios of some pollutants to CO2 (e.g. CO∕CO2, NOx∕CO2, NOx∕CO, and SO2∕CO2) by sector, region, and time to identify any decoupling of air pollutant emissions from energy production activities and to demonstrate the potential of such ratios to compare to satellite-derived emission data. Gridded emissions are also made available for the 1970–2012 historic time series, disaggregated for 26 anthropogenic sectors using updated spatial proxies. The analysis of the evolution of hot spots over time allowed us to identify areas with growing emissions and where emissions should be constrained to improve global air quality (e.g. China, India, the Middle East, and some South American countries are often characterized by high emitting areas that are changing rapidly compared to Europe or the USA, where stable or decreasing emissions are evaluated). Sector- and component-specific contributions to grid-cell emissions may help the modelling and satellite communities to disaggregate atmospheric column amounts and concentrations into main emitting sectors. This work addresses not only the emission inventory and modelling communities, but also aims to broaden the usefulness of information available in a global emission inventory such as EDGAR to also include the measurement community. Data are publicly available online through the EDGAR website http://edgar.jrc.ec.europa.eu/overview.php?v=432_AP and registered under https://doi.org/10.2904/JRC_DATASET_EDGAR.

We evaluated a modified VBS (volatility basis set) scheme to treat biomass-burning-like organic aerosol (BBOA) implemented in CAMx (Comprehensive Air Quality Model with extensions). The updated scheme was parameterized with novel wood combustion smog chamber experiments using a hybrid VBS framework which accounts for a mixture of wood burning organic aerosol precursors and their further functionalization and fragmentation in the atmosphere. The new scheme was evaluated for one of the winter EMEP intensive campaigns (February–March 2009) against aerosol mass spectrometer (AMS) measurements performed at 11 sites in Europe. We found a considerable improvement for the modelled organic aerosol (OA) mass compared to our previous model application with the mean fractional bias (MFB) reduced from −61 to −29 %. We performed model-based source apportionment studies and compared results against positive matrix factorization (PMF) analysis performed on OA AMS data. Both model and observations suggest that OA was mainly of secondary origin at almost all sites. Modelled secondary organic aerosol (SOA) contributions to total OA varied from 32 to 88 % (with an average contribution of 62 %) and absolute concentrations were generally under-predicted. Modelled primary hydrocarbon-like organic aerosol (HOA) and primary biomass-burning-like aerosol (BBPOA) fractions contributed to a lesser extent (HOA from 3 to 30 %, and BBPOA from 1 to 39 %) with average contributions of 13 and 25 %, respectively. Modelled BBPOA fractions were found to represent 12 to 64 % of the total residential-heating-related OA, with increasing contributions at stations located in the northern part of the domain. Source apportionment studies were performed to assess the contribution of residential and non-residential combustion precursors to the total SOA. Non-residential combustion and road transportation sector contributed about 30–40 % to SOA formation (with increasing contributions at urban and near industrialized sites), whereas residential combustion (mainly related to wood burning) contributed to a larger extent, around 60–70 %. Contributions to OA from residential combustion precursors in different volatility ranges were also assessed: our results indicate that residential combustion gas-phase precursors in the semivolatile range (SVOC) contributed from 6 to 30 %, with higher contributions predicted at stations located in the southern part of the domain. On the other hand, the oxidation products of higher-volatility precursors (the sum of intermediate-volatility compounds (IVOCs) and volatile organic compounds (VOCs)) contribute from 15 to 38 % with no specific gradient among the stations. Although the new parameterization leads to a better agreement between model results and observations, it still under-predicts the SOA fraction, suggesting that uncertainties in the new scheme and other sources and/or formation mechanisms remain to be elucidated. Moreover, a more detailed characterization of the semivolatile components of the emissions is needed.

Non-methane volatile organic compounds (NMVOCs) include a large number of chemical species which differ significantly in their chemical characteristics and thus in their impacts on ozone and secondary organic aerosol formation. It is important that chemical transport models (CTMs) simulate the chemical transformation of the different NMVOC species in the troposphere consistently. In most emission inventories, however, only total NMVOC emissions are reported, which need to be decomposed into classes to fit the requirements of CTMs. For instance, the Emissions Database for Global Atmospheric Research (EDGAR) provides spatially resolved global anthropogenic emissions of total NMVOCs. In this study the EDGAR NMVOC inventory was revised and extended in time and in sectors. Moreover the new version of NMVOC emission data in the EDGAR database were disaggregated on a detailed sector resolution to individual species or species groups, thus enhancing the usability of the NMVOC emission data by the modelling community. Region- and source-specific speciation profiles of NMVOC species or species groups are compiled and mapped to EDGAR processes (detailed resolution of sectors), with corresponding quality codes specifying the quality of the mapping. Individual NMVOC species in different profiles are aggregated to 25 species groups, in line with the common classification of the Global Emissions Initiative (GEIA). Global annual grid maps with a resolution of 0.1° × 0.1° for the period 1970–2012 are produced by sector and species. Furthermore, trends in NMVOC composition are analysed, taking road transport and residential sources in Germany and the United Kingdom (UK) as examples.

Four periods of EMEP (European Monitoring and Evaluation Programme) intensive measurement campaigns (June 2006, January 2007, September–October 2008 and February–March 2009) were modelled using the regional air quality model CAMx with VBS (volatility basis set) approach for the first time in Europe within the framework of the EURODELTA-III model intercomparison exercise. More detailed analysis and sensitivity tests were performed for the period of February–March 2009 and June 2006 to investigate the uncertainties in emissions as well as to improve the modelling of organic aerosol (OA). Model performance for selected gas phase species and PM2.5 was evaluated using the European air quality database AirBase. Sulfur dioxide (SO2) and ozone (O3) were found to be overestimated for all the four periods, with O3 having the largest mean bias during June 2006 and January–February 2007 periods (8.9 pbb and 12.3 ppb mean biases respectively). In contrast, nitrogen dioxide (NO2) and carbon monoxide (CO) were found to be underestimated for all the four periods. CAMx reproduced both total concentrations and monthly variations of PM2.5 for all the four periods with average biases ranging from −2.1 to 1.0 µg m−3. Comparisons with AMS (aerosol mass spectrometer) measurements at different sites in Europe during February–March 2009 showed that in general the model overpredicts the inorganic aerosol fraction and underpredicts the organic one, such that the good agreement for PM2.5 is partly due to compensation of errors. The effect of the choice of VBS scheme on OA was investigated as well. Two sensitivity tests with volatility distributions based on previous chamber and ambient measurements data were performed. For February–March 2009 the chamber case reduced the total OA concentrations by about 42 % on average. In contrast, a test based on ambient measurement data increased OA concentrations by about 42 % for the same period bringing model and observations into better agreement. Comparison with the AMS data at the rural Swiss site Payerne in June 2006 shows no significant improvement in modelled OA concentration. Further sensitivity tests with increased biogenic and anthropogenic emissions suggest that OA in Payerne was affected by changes in emissions from residential heating during the February–March 2009 whereas it was more sensitive to biogenic precursors in June 2006.

We use a three-dimensional regional chemical transport model (PMCAMx) with high grid resolution and high-resolution emissions (4 × 4 km2) over the Paris greater area to simulate the formation of carbonaceous aerosol during a summer (July 2009) and a winter (January/February 2010) period as part of the MEGAPOLI (megacities: emissions, urban, regional, and global atmospheric pollution and climate effects, and Integrated tools for assessment and mitigation) campaigns. Model predictions of carbonaceous aerosol are compared against Aerodyne aerosol mass spectrometer and black carbon (BC) high time resolution measurements from three ground sites. PMCAMx predicts BC concentrations reasonably well reproducing the majority (70 %) of the hourly data within a factor of two during both periods. The agreement for the summertime secondary organic aerosol (OA) concentrations is also encouraging (mean bias = 0.1 µg m−3) during a photochemically intense period. The model tends to underpredict the summertime primary OA concentrations in the Paris greater area (by approximately 0.8 µg m−3) mainly due to missing primary OA emissions from cooking activities. The total cooking emissions are estimated to be approximately 80 mg d−1 per capita and have a distinct diurnal profile in which 50 % of the daily cooking OA is emitted during lunch time (12:00–14:00 LT) and 20 % during dinner time (20:00–22:00 LT). Results also show a large underestimation of secondary OA in the Paris greater area during wintertime (mean bias = −2.3 µg m−3) pointing towards a secondary OA formation process during low photochemical activity periods that is not simulated in the model.

The EDGARv4.3.1 (Emissions Database for Global Atmospheric Research) global anthropogenic emissions inventory of gaseous (SO2, NOx, CO, non-methane volatile organic compounds and NH3) and particulate (PM10, PM2.5, black and organic carbon) air pollutants for the period 1970–2010 is used to develop retrospective air pollution emissions scenarios to quantify the roles and contributions of changes in energy consumption and efficiency, technology progress and end-of-pipe emission reduction measures and their resulting impact on health and crop yields at European and global scale. The reference EDGARv4.3.1 emissions include observed and reported changes in activity data, fuel consumption and air pollution abatement technologies over the past 4 decades, combined with Tier 1 and region-specific Tier 2 emission factors. Two further retrospective scenarios assess the interplay of policy and industry. The highest emission STAG_TECH scenario assesses the impact of the technology and end-of-pipe reduction measures in the European Union, by considering historical fuel consumption, along with a stagnation of technology with constant emission factors since 1970, and assuming no further abatement measures and improvement imposed by European emission standards. The lowest emission STAG_ENERGY scenario evaluates the impact of increased fuel consumption by considering unchanged energy consumption since the year 1970, but assuming the technological development, end-of-pipe reductions, fuel mix and energy efficiency of 2010. Our scenario analysis focuses on the three most important and most regulated sectors (power generation, manufacturing industry and road transport), which are subject to multi-pollutant European Union Air Quality regulations. Stagnation of technology and air pollution reduction measures at 1970 levels would have led to 129 % (or factor 2.3) higher SO2, 71 % higher NOx and 69 % higher PM2.5 emissions in Europe (EU27), demonstrating the large role that technology has played in reducing emissions in 2010. However, stagnation of energy consumption at 1970 levels, but with 2010 fuel mix and energy efficiency, and assuming current (year 2010) technology and emission control standards, would have lowered today's NOx emissions by ca. 38 %, SO2 by 50 % and PM2.5 by 12 % in Europe. A reduced-form chemical transport model is applied to calculate regional and global levels of aerosol and ozone concentrations and to assess the associated impact of air quality improvements on human health and crop yield loss, showing substantial impacts of EU technologies and standards inside as well as outside Europe. We assess that the interplay of policy and technological advance in Europe had substantial benefits in Europe, but also led to an important improvement of particulate matter air quality in other parts of the world.

Using a mass transfer model and the volatility basis set, we estimate the volatility distribution for the organic aerosol (OA) components during summer and winter in Paris, France as part of the collaborative project MEGAPOLI. The concentrations of the OA components as a function of temperature were measured combining data from a thermodenuder and an aerosol mass spectrometer (AMS) with Positive Matrix Factorization (PMF) analysis. The hydrocarbon-like organic aerosol (HOA) had similar volatility distributions for the summer and winter campaigns with half of the material in the saturation concentration bin of 10 µg m−3 and another 35–40 % consisting of low and extremely low volatility organic compounds (LVOCs with effective saturation concentrations C* of 10−3–0.1 µg m−3 and ELVOCs C* less or equal than 10−4 µg m−3, respectively). The winter cooking OA (COA) was more than an order of magnitude less volatile than the summer COA. The low-volatility oxygenated OA (LV-OOA) factor detected in the summer had the lowest volatility of all the derived factors and consisted almost exclusively of ELVOCs. The volatility for the semi-volatile oxygenated OA (SV-OOA) was significantly higher than that of the LV-OOA, containing both semi-volatile organic components (SVOCs with C* in the 1–100 µg m−3 range) and LVOCs. The oxygenated OA (OOA) factor in winter consisted of SVOCs (45 %), LVOCs (25 %) and ELVOCs (30 %). The volatility of marine OA (MOA) was higher than that of the other factors containing around 60 % SVOCs. The biomass burning OA (BBOA) factor contained components with a wide range of volatilities with significant contributions from both SVOCs (50 %) and LVOCs (30 %). Finally, combining the bulk average O : C ratios and volatility distributions of the various factors, our results are placed into the two-dimensional volatility basis set (2D-VBS) framework. The OA factors cover a broad spectrum of volatilities with no direct link between the average volatility and average O : C of the OA components.

Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and source apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 µm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 µg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in source apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved long-term data sets.

The mandate of the Task Force Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to improve the scientific understanding of the intercontinental air pollution transport, to quantify impacts on human health, vegetation and climate, to identify emission mitigation options across the regions of the Northern Hemisphere, and to guide future policies on these aspects. The harmonization and improvement of regional emission inventories is imperative to obtain consolidated estimates on the formation of global-scale air pollution. An emissions data set has been constructed using regional emission grid maps (annual and monthly) for SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC and OC for the years 2008 and 2010, with the purpose of providing consistent information to global and regional scale modelling efforts. This compilation of different regional gridded inventories – including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Inter-comparison Study for Asia (MICS-Asia III) for China, India and other Asian countries – was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4.3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.1° × 0.1° longitude-latitude, to yield monthly, global, sector-specific grid maps for each substance and year. The HTAP_v2.2 air pollutant grid maps are considered to combine latest available regional information within a complete global data set. The disaggregation by sectors, high spatial and temporal resolution and detailed information on the data sources and references used will provide the user the required transparency. Because HTAP_v2.2 contains primarily official and/or widely used regional emission grid maps, it can be recommended as a global baseline emission inventory, which is regionally accepted as a reference and from which different scenarios assessing emission reduction policies at a global scale could start. An analysis of country-specific implied emission factors shows a large difference between industrialised countries and developing countries for acidifying gaseous air pollutant emissions (SO2 and NOx) from the energy and industry sectors. This is not observed for the particulate matter emissions (PM10, PM2.5), which show large differences between countries in the residential sector instead. The per capita emissions of all world countries, classified from low to high income, reveal an increase in level and in variation for gaseous acidifying pollutants, but not for aerosols. For aerosols, an opposite trend is apparent with higher per capita emissions of particulate matter for low income countries.

Ambient particle number size distributions were measured in Paris, France, during summer (1–31 July 2009) and winter (15 January to 15 February 2010) at three fixed ground sites and using two mobile laboratories and one airplane. The campaigns were part of the Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation (MEGAPOLI) project. New particle formation (NPF) was observed only during summer on approximately 50 % of the campaign days, assisted by the low condensation sink (about 10.7 ± 5.9 × 10−3 s−1). NPF events inside the Paris plume were also observed at 600 m altitude onboard an aircraft simultaneously with regional events identified on the ground. Increased particle number concentrations were measured aloft also outside of the Paris plume at the same altitude, and were attributed to NPF. The Paris plume was identified, based on increased particle number and black carbon concentration, up to 200 km away from the Paris center during summer. The number concentration of particles with diameters exceeding 2.5 nm measured on the surface at the Paris center was on average 6.9 ± 8.7 × 104 and 12.1 ± 8.6 × 104 cm−3 during summer and winter, respectively, and was found to decrease exponentially with distance from Paris. However, further than 30 km from the city center, the particle number concentration at the surface was similar during both campaigns. During summer, one suburban site in the NE was not significantly affected by Paris emissions due to higher background number concentrations, while the particle number concentration at the second suburban site in the SW increased by a factor of 3 when it was downwind of Paris.

A detailed characterization of air quality in the megacity of Paris (France) during two 1-month intensive campaigns and from additional 1-year observations revealed that about 70 % of the urban background fine particulate matter (PM) is transported on average into the megacity from upwind regions. This dominant influence of regional sources was confirmed by in situ measurements during short intensive and longer-term campaigns, aerosol optical depth (AOD) measurements from ENVISAT, and modeling results from PMCAMx and CHIMERE chemistry transport models. While advection of sulfate is well documented for other megacities, there was surprisingly high contribution from long-range transport for both nitrate and organic aerosol. The origin of organic PM was investigated by comprehensive analysis of aerosol mass spectrometer (AMS), radiocarbon and tracer measurements during two intensive campaigns. Primary fossil fuel combustion emissions constituted less than 20 % in winter and 40 % in summer of carbonaceous fine PM, unexpectedly small for a megacity. Cooking activities and, during winter, residential wood burning are the major primary organic PM sources. This analysis suggests that the major part of secondary organic aerosol is of modern origin, i.e., from biogenic precursors and from wood burning. Black carbon concentrations are on the lower end of values encountered in megacities worldwide, but still represent an issue for air quality. These comparatively low air pollution levels are due to a combination of low emissions per inhabitant, flat terrain, and a meteorology that is in general not conducive to local pollution build-up. This revised picture of a megacity only being partially responsible for its own average and peak PM levels has important implications for air pollution regulation policies.

Marine-sourced organic aerosols (MOAs) have been shown to play an important role in tropospheric chemistry by impacting surface mass, cloud condensation nuclei, and ice nuclei concentrations over remote marine and coastal regions. In this work, an online marine primary organic aerosol emission parameterization, designed to be used for both global and regional models, was implemented into the GEOS-Chem (Global Earth Observing System Chemistry) model. The implemented emission scheme improved the large underprediction of organic aerosol concentrations in clean marine regions (normalized mean bias decreases from −79% when using the default settings to −12% when marine organic aerosols are added). Model predictions were also in good agreement (correlation coefficient of 0.62 and normalized mean bias of −36%) with hourly surface concentrations of MOAs observed during the summertime at an inland site near Paris, France. Our study shows that MOAs have weaker coastal-to-inland concentration gradients than sea-salt aerosols, leading to several inland European cities having >10% of their surface submicron organic aerosol mass concentration with a marine source. The addition of MOA tracers to GEOS-Chem enabled us to identify the regions with large contributions of freshly emitted or aged aerosol having distinct physicochemical properties, potentially indicating optimal locations for future field studies.

Measurements of gaseous and particulate organic carbon were performed during the MEGAPOLI experiments, in July 2009 and January–February 2010, at the SIRTA observatory in suburban Paris. Measurements comprise primary and secondary volatile organic compounds (VOCs), of both anthropogenic and biogenic origins, including C12–C16 n-alkanes of intermediate volatility (IVOCs), suspected to be efficient precursors of secondary organic aerosol (SOA). The time series of gaseous carbon are generally consistent with times series of particulate organic carbon at regional scale, and are clearly affected by meteorology and air mass origin. Concentration levels of anthropogenic VOCs in urban and suburban Paris were surprisingly low (2–963 ppt) compared to other megacities worldwide and to rural continental sites. Urban enhancement ratios of anthropogenic VOC pairs agree well between the urban and suburban Paris sites, showing the regional extent of anthropogenic sources of similar composition. Contrary to other primary anthropogenic VOCs (aromatics and alkanes), IVOCs showed lower concentrations in winter (< 5 ppt) compared to summer (13–27 ppt), which cannot be explained by the gas-particle partitioning theory. Higher concentrations of most oxygenated VOCs in winter (18–5984 ppt) suggest their dominant primary anthropogenic origin. The respective role of primary anthropogenic gaseous compounds in regional SOA formation was investigated by estimating the SOA mass concentration expected from the anthropogenic VOCs and IVOCs (I / VOCs) measured at SIRTA. From an integrated approach based on emission ratios and SOA yields, 38 % of the SOA measured at SIRTA is explained by the measured concentrations of I / VOCs, with a 2% contribution by C12–C16 n-alkane IVOCs. From the results of an alternative time-resolved approach, the average IVOC contribution to SOA formation is estimated to be 7%, which is half of the average contribution of the traditional aromatic compounds (15%). Both approaches, which are based on in situ observations of particular I / VOCs, emphasize the importance of the intermediate volatility compounds in the SOA formation, and support previous results from chamber experiments and modeling studies. They also support the need to make systematic the IVOCs' speciated measurement during field campaigns.

A detailed three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe, focusing on the formation and chemical transformation of organic matter. Three periods representative of different seasons were simulated, corresponding to intensive field campaigns. An extensive set of AMS measurements was used to evaluate the model and, using factor-analysis results, gain more insight into the sources and transformations of organic aerosol (OA). Overall, the agreement between predictions and measurements for OA concentration is encouraging, with the model reproducing two-thirds of the data (daily average mass concentrations) within a factor of 2. Oxygenated OA (OOA) is predicted to contribute 93% to total OA during May, 87% during winter and 96% during autumn, with the rest consisting of fresh primary OA (POA). Predicted OOA concentrations compare well with the observed OOA values for all periods, with an average fractional error of 0.53 and a bias equal to −0.07 (mean error = 0.9 μg m−3, mean bias = −0.2 μg m−3). The model systematically underpredicts fresh POA at most sites during late spring and autumn (mean bias up to −0.8 μg m−3). Based on results from a source apportionment algorithm running in parallel with PMCAMx, most of the POA originates from biomass burning (fires and residential wood combustion), and therefore biomass burning OA is most likely underestimated in the emission inventory. The sensitivity of POA predictions to the corresponding emissions' volatility distribution is discussed. The model performs well at all sites when the Positive Matrix Factorization (PMF)-estimated low-volatility OOA is compared against the OA with saturation concentrations of the OA surrogate species C* ≤ 0.1 μg m−3 and semivolatile OOA against the OA with C* > 0.1 μg m−3.

In the fine-particle mode (aerodynamic diameter < 1 μm) non-volatile material has been associated with black carbon (BC) and low-volatile organics and, to a lesser extent, with sea salt and mineral dust. This work analyzes non-volatile particles at the tropospheric research station Melpitz (Germany), combining experimental methods such as a mobility particle-size spectrometer (3–800 nm), a thermodenuder operating at 300 °C, a multi-angle absorption photometer (MAAP), and an aerosol mass spectrometer (AMS). The data were collected during two atmospheric field experiments in May–June 2008 as well as February–March 2009. As a basic result, we detected average non-volatile particle–volume fractions of 11 ± 3% (2008) and 17 ± 8% (2009). In both periods, BC was in close linear correlation with the non-volatile fraction, but not sufficient to quantitatively explain the non-volatile particle mass concentration. Based on the assumption that BC is not altered by the heating process, the non-volatile particle mass fraction could be explained by the sum of black carbon (47% in summer, 59% in winter) and a non-volatile organic contribution estimated as part of the low-volatility oxygenated organic aerosol (LV-OOA) (53% in summer, 41% in winter); the latter was identified from AMS data by factor analysis. Our results suggest that LV-OOA was more volatile in summer (May–June 2008) than in winter (February–March 2009) which was linked to a difference in oxidation levels (lower in summer). Although carbonaceous compounds dominated the sub-μm non-volatile particle mass fraction most of the time, a cross-sensitivity to partially volatile aerosol particles of maritime origin could be seen. These marine particles could be distinguished, however from the carbonaceous particles by a characteristic particle volume–size distribution. The paper discusses the uncertainty of the volatility measurements and outlines the possible merits of volatility analysis as part of continuous atmospheric aerosol measurements.

Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM1) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May–June and September–October) and 2009 (February–March). In this paper we focus on the identification of the main organic aerosol sources and we define a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our source apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous source apportionment results where only a separation in primary and secondary OA sources was possible. Generally, our solutions include two primary OA sources, i.e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i.e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol sources in Europe and an interesting set of highly time resolved data for modeling purposes.

For the investigation of megacity emission development and the impact outside the source region, mobile aerosol and trace gas measurements were carried out in the Paris metropolitan area between 1 July and 31 July 2009 (summer conditions) and 15 January and 15 February 2010 (winter conditions) in the framework of the European Union FP7 MEGAPOLI project. Two mobile laboratories, MoLa and MOSQUITA, were deployed, and here an overview of these measurements and an investigation of the applicability of such measurements for the analysis of megacity emissions are presented. Both laboratories measured physical and chemical properties of fine and ultrafine aerosol particles as well as gas phase constituents of relevance for urban pollution scenarios. The applied measurement strategies include cross-section measurements for the investigation of plume structure and quasi-Lagrangian measurements axially along the flow of the city's pollution plume to study plume aging processes. Results of intercomparison measurements between the two mobile laboratories represent the adopted data quality assurance procedures. Most of the compared measurement devices show sufficient agreement for combined data analysis. For the removal of data contaminated by local pollution emissions a video tape analysis method was applied. Analysis tools like positive matrix factorization and peak integration by key analysis applied to high-resolution time-of-flight aerosol mass spectrometer data are used for in-depth data analysis of the organic particulate matter. Several examples, including a combination of MoLa and MOSQUITA measurements on a cross section through the Paris emission plume, are provided to demonstrate how such mobile measurements can be used to investigate the emissions of a megacity. A critical discussion of advantages and limitations of mobile measurements for the investigation of megacity emissions completes this work.

Single-particle mixing state information can be a powerful tool for assessing the relative impact of local and regional sources of ambient particulate matter in urban environments. However, quantitative mixing state data are challenging to obtain using single-particle mass spectrometers. In this study, the quantitative chemical composition of carbonaceous single particles has been determined using an aerosol time-of-flight mass spectrometer (ATOFMS) as part of the MEGAPOLI 2010 winter campaign in Paris, France. Relative peak areas of marker ions for elemental carbon (EC), organic aerosol (OA), ammonium, nitrate, sulfate and potassium were compared with concurrent measurements from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), a thermal–optical OCEC analyser and a particle into liquid sampler coupled with ion chromatography (PILS-IC). ATOFMS-derived estimated mass concentrations reproduced the variability of these species well (R2 = 0.67–0.78), and 10 discrete mixing states for carbonaceous particles were identified and quantified. The chemical mixing state of HR-ToF-AMS organic aerosol factors, resolved using positive matrix factorisation, was also investigated through comparison with the ATOFMS dataset. The results indicate that hydrocarbon-like OA (HOA) detected in Paris is associated with two EC-rich mixing states which differ in their relative sulfate content, while fresh biomass burning OA (BBOA) is associated with two mixing states which differ significantly in their OA / EC ratios. Aged biomass burning OA (OOA2-BBOA) was found to be significantly internally mixed with nitrate, while secondary, oxidised OA (OOA) was associated with five particle mixing states, each exhibiting different relative secondary inorganic ion content. Externally mixed secondary organic aerosol was not observed. These findings demonstrate the range of primary and secondary organic aerosol mixing states in Paris. Examination of the temporal behaviour and chemical composition of the ATOFMS classes also enabled estimation of the relative contribution of transported emissions of each chemical species and total particle mass in the size range investigated. Only 22% of the total ATOFMS-derived particle mass was apportioned to fresh, local emissions, with 78% apportioned to regional/continental-scale emissions.

Secondary organic aerosol (SOA), a prominent fraction of particulate organic mass (OA), remains poorly constrained. Its formation involves several unknown precursors, formation and evolution pathways and multiple natural and anthropogenic sources. Here a combined gas-particle phase source apportionment is applied to wintertime and summertime data collected in the megacity of Paris in order to investigate SOA origin during both seasons. This was possible by combining the information provided by an aerosol mass spectrometer (AMS) and a proton transfer reaction mass spectrometer (PTR-MS). A better constrained apportionment of primary OA (POA) sources is also achieved using this methodology, making use of gas-phase tracers. These tracers made possible the discrimination between biogenic and continental/anthropogenic sources of SOA. We found that continental SOA was dominant during both seasons (24–50% of total OA), while contributions from photochemistry-driven SOA (9% of total OA) and marine emissions (13% of total OA) were also observed during summertime. A semi-volatile nighttime component was also identified (up to 18% of total OA during wintertime). This approach was successfully applied here and implemented in a new source apportionment toolkit.

Simulations with the chemistry transport model CHIMERE are compared to measurements performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer campaign in the Greater Paris region in July 2009. The volatility-basis-set approach (VBS) is implemented into this model, taking into account the volatility of primary organic aerosol (POA) and the chemical aging of semi-volatile organic species. Organic aerosol is the main focus and is simulated with three different configurations with a modified treatment of POA volatility and modified secondary organic aerosol (SOA) formation schemes. In addition, two types of emission inventories are used as model input in order to test the uncertainty related to the emissions. Predictions of basic meteorological parameters and primary and secondary pollutant concentrations are evaluated, and four pollution regimes are defined according to the air mass origin. Primary pollutants are generally overestimated, while ozone is consistent with observations. Sulfate is generally overestimated, while ammonium and nitrate levels are well simulated with the refined emission data set. As expected, the simulation with non-volatile POA and a single-step SOA formation mechanism largely overestimates POA and underestimates SOA. Simulation of organic aerosol with the VBS approach taking into account the aging of semi-volatile organic compounds (SVOC) shows the best correlation with measurements. High-concentration events observed mostly after long-range transport are well reproduced by the model. Depending on the emission inventory used, simulated POA levels are either reasonable or underestimated, while SOA levels tend to be overestimated. Several uncertainties related to the VBS scheme (POA volatility, SOA yields, the aging parameterization), to emission input data, and to simulated OH levels can be responsible for this behavior. Despite these uncertainties, the implementation of the VBS scheme into the CHIMERE model allowed for much more realistic organic aerosol simulations for Paris during summertime. The advection of SOA from outside Paris is mostly responsible for the highest OA concentration levels. During advection of polluted air masses from northeast (Benelux and Central Europe), simulations indicate high levels of both anthropogenic and biogenic SOA fractions, while biogenic SOA dominates during periods with advection from Southern France and Spain.

Aerosol hygroscopicity and refractory black carbon (rBC) properties were characterised during wintertime at a suburban site in Paris, one of the biggest European cities. Hygroscopic growth factor (GF) frequency distributions, characterised by distinct modes of more-hygroscopic background aerosol and non- or slightly hygroscopic aerosol of local (or regional) origin, revealed an increase of the relative contribution of the local sources compared to the background aerosol with decreasing particle size. BC-containing particles in Paris were mainly originating from fresh traffic emissions, whereas biomass burning only gave a minor contribution. The mass size distribution of the rBC cores peaked on average at an rBC core mass equivalent diameter of DMEV ~ 150 nm. The BC-containing particles were moderately coated (coating thickness Δcoat ~ 33 nm on average for rBC cores with DMEV = 180–280 nm) and an average mass absorption coefficient (MAC) of ~ 8.6 m2 g−1 at the wavelength λ = 880 nm was observed. Different time periods were selected to investigate the properties of BC-containing particles as a function of source and air mass type. The traffic emissions were found to be non-hygroscopic (GF ≈ 1.0), and essentially all particles with a dry mobility diameter (D0) larger than D0 = 110 nm contained an rBC core. rBC from traffic emissions was further observed to be uncoated within experimental uncertainty (Δcoat ~ 2 nm ± 10 nm), to have the smallest BC core sizes (maximum of the rBC core mass size distribution at DMEV ~ 100 nm) and to have the smallest MAC (~ 7.3 m2g−1 at λ = 880 nm). The biomass burning aerosol was slightly more hygroscopic than the traffic emissions (with a distinct slightly-hygroscopic mode peaking at GF ≈ 1.1–1.2). Furthermore, only a minor fraction (&leq; 10%) of the slightly-hygroscopic particles with 1.1 &leq; GF &leq; 1.2 (and D0 = 265 nm) contained a detectable rBC core. The BC-containing particles from biomass burning were found to have a medium coating thickness as well as slightly larger mean rBC core sizes and MAC values compared to traffic emissions. The aerosol observed under the influence of aged air masses and air masses from Eastern Continental Europe was dominated by a~more-hygroscopic mode peaking at GF ≈ 1.6. Most particles (95%), in the more-hygroscopic mode at D0 = 265 nm, did not contain a detectable rBC core. A significant fraction of the BC-containing particles had a substantial coating with non-refractory aerosol components. MAC values of ~ 8.8 m2g−1 and ~ 8.3 m2g−1 at λ = 880 nm and mass mean rBC core diameters of 150 nm and 200 nm were observed for the aged and continental air mass types, respectively. The reason for the larger rBC core sizes compared to the fresh emissions – transport effects or a different rBC source – remains unclear. The dominant fraction of the BC-containing particles was found to have no or very little coating with non-refractory matter. The lack of coatings is consistent with the observation that the BC-containing particles are non- or slightly-hygroscopic, which makes them poor cloud condensation nuclei. It can therefore be expected that wet removal through nucleation scavenging is inefficient for fresh BC-containing particles in urban plumes. The mixing-state-specific cloud droplet activation behaviour of BC-containing particles including the effects of atmospheric aging processes should be considered in global simulations of atmospheric BC, as the wet removal efficiency remains a major source of uncertainty in its life-cycle.

The effect of a post-industrial megacity on local and regional air quality was assessed via a month-long field measurement campaign in the Paris metropolitan area during winter 2010. Here we present source apportionment results from three aerosol mass spectrometers and two aethalometers deployed at three measurement stations within the Paris region. Submicron aerosol composition is dominated by the organic fraction (30–36%) and nitrate (28–29%), with lower contributions from sulfate (14–16%), ammonium (12–14%) and black carbon (7–13%). Organic source apportionment was performed using positive matrix factorization, resulting in a set of organic factors corresponding both to primary emission sources and secondary production. The dominant primary sources are traffic (11–15% of organic mass), biomass burning (13–15%) and cooking (up to 35% during meal hours). Secondary organic aerosol contributes more than 50% to the total organic mass and includes a highly oxidized factor from indeterminate and/or diverse sources and a less oxidized factor related to wood burning emissions. Black carbon was apportioned to traffic and wood burning sources using a model based on wavelength-dependent light absorption of these two combustion sources. The time series of organic and black carbon factors from related sources were strongly correlated. The similarities in aerosol composition, total mass and temporal variation between the three sites suggest that particulate pollution in Paris is dominated by regional factors, and that the emissions from Paris itself have a relatively low impact on its surroundings.

During July 2009, a one-month measurement campaign was performed in the megacity of Paris. Amongst other measurement platforms, three stationary sites distributed over an area of 40 km in diameter in the greater Paris region enabled a detailed characterization of the aerosol particle and gas phase. Simulation results from the FLEXPART dispersion model were used to distinguish between different types of air masses sampled. It was found that the origin of air masses had a large influence on measured mass concentrations of the secondary species particulate sulphate, nitrate, ammonium, and oxygenated organic aerosol measured with the Aerodyne aerosol mass spectrometer in the submicron particle size range: particularly high concentrations of these species (about 4 μg m−3, 2 μg m−3, 2 μg m−3, and 7 μg m−3, respectively) were measured when aged material was advected from continental Europe, while for air masses originating from the Atlantic, much lower mass concentrations of these species were observed (about 1 μg m−3, 0.2 μg m−3, 0.4 μg m−3, and 1–3 μg m−3, respectively). For the primary emission tracers hydrocarbon-like organic aerosol, black carbon, and NOx it was found that apart from diurnal source strength variations and proximity to emission sources, local meteorology had the largest influence on measured concentrations, with higher wind speeds leading to larger dilution and therefore smaller measured concentrations. Also the shape of particle size distributions was affected by wind speed and air mass origin. Quasi-Lagrangian measurements performed under connected flow conditions between the three stationary sites were used to estimate the influence of the Paris emission plume onto its surroundings, which was found to be rather small. Rough estimates for the impact of the Paris emission plume on the suburban areas can be inferred from these measurements: Volume mixing ratios of 1–14 ppb of NOx, and upper limits for mass concentrations of about 1.5 μg m−3 of black carbon and of about 3 μg m−3 of hydrocarbon-like organic aerosol can be deduced which originate from both, local emissions and the overall Paris emission plume. The secondary aerosol particle phase species were found to be not significantly influenced by the Paris megacity, indicating their regional origin. The submicron aerosol mass concentrations of particulate sulphate, nitrate, and ammonium measured during time periods when air masses were advected from eastern central Europe were found to be similar to what has been found from other measurement campaigns in Paris and south-central France for this type of air mass origin, indicating that the results presented here are also more generally valid.

The impact of wood combustion on ambient aerosols was investigated in Augsburg, Germany during a winter measurement campaign of a six-week period. Special attention was paid to the high time resolution observations of wood combustion with different mass spectrometric methods. Here we present and compare the results from an Aerodyne aerosol mass spectrometer (AMS) and gas chromatographic – mass spectrometric (GC-MS) analysed PM1 filters on an hourly basis. This includes source apportionment of the AMS derived organic matter (OM) using positive matrix factorisation (PMF) and analysis of levoglucosan as wood combustion marker, respectively. During the measurement period nitrate and OM mass are the main contributors to the defined submicron particle mass of AMS and Aethalometer with 28% and 35%, respectively. Wood combustion organic aerosol (WCOA) contributes to OM with 23% on average and 27% in the evening and night time. Conclusively, wood combustion has a strong influence on the organic matter and overall aerosol composition. Levoglucosan accounts for 14% of WCOA mass with a higher percentage in comparison to other studies. The ratio between the mass of levoglucosan and organic carbon amounts to 0.06. This study is unique in that it provides a one-hour time resolution comparison between the wood combustion results of the AMS and the GC-MS analysed filter method at a PM1 particle size range. The comparison of the concentration variation with time of the PMF WCOA factor, levoglucosan estimated by the AMS data and the levoglucosan measured by GC-MS is highly correlated (R2 = 0.84), and a detailed discussion on the contributors to the wood combustion marker ion at mass-to-charge ratio 60 is given. At the end, both estimations, the WCOA factor and the levoglucosan concentration estimated by AMS data, allow to observe the variation with time of wood combustion emissions (gradient correlation with GC-MS levoglucosan of R2 = 0.84). In the case of WCOA, it provides the estimated magnitude of wood combustion emission. Quantitative estimation of the levoglucosan concentration from the AMS data is problematic due to its overestimation in comparison to the levoglucosan measured by the GC-MS.

Organic aerosol (OA) represents a significant and often major fraction of the non-refractory PM1 (particulate matter with an aerodynamic diameter da < 1 μm) mass. Secondary organic aerosol (SOA) is an important contributor to the OA and can be formed from biogenic and anthropogenic precursors. Here we present results from the characterization of SOA produced from the emissions of three different anthropogenic sources. SOA from a log wood burner, a Euro 2 diesel car and a two-stroke Euro 2 scooter were characterized with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) and compared to SOA from α-pinene. The emissions were sampled from the chimney/tailpipe by a heated inlet system and filtered before injection into a smog chamber. The gas phase emissions were irradiated by xenon arc lamps to initiate photo-chemistry which led to nucleation and subsequent particle growth by SOA production. Duplicate experiments were performed for each SOA type, with the averaged organic mass spectra showing Pearson's r values >0.94 for the correlations between the four different SOA types after five hours of aging. High-resolution mass spectra (HR-MS) showed that the dominant peaks in the MS, m/z 43 and 44, are dominated by the oxygenated ions C2H3O+ and CO2+, respectively, similarly to the relatively fresh semi-volatile oxygenated OA (SV-OOA) observed in the ambient aerosol. The atomic O:C ratios were found to be in the range of 0.25–0.55 with no major increase during the first five hours of aging. On average, the diesel SOA showed the lowest O:C ratio followed by SOA from wood burning, α-pinene and the scooter emissions. Grouping the fragment ions revealed that the SOA source with the highest O:C ratio had the largest fraction of small ions. The HR data of the four sources could be clustered and separated using principal component analysis (PCA). The model showed a significant separation of the four SOA types and clustering of the duplicate experiments on the first two principal components (PCs), which explained 79% of the total variance. Projection of ambient SV-OOA spectra resolved by positive matrix factorization (PMF) showed that this approach could be useful to identify large contributions of the tested SOA sources to SV-OOA. The first results from this study indicate that the SV-OOA in Barcelona is strongly influenced by diesel emissions in winter while in summer at SIRTA at the southwestern edge of Paris SV-OOA is more similar to alpha-pinene SOA. However, contributions to the ambient SV-OOA from SOA sources that are not covered by the model can cause major interference and therefore future expansions of the PCA model with additional SOA sources is recommended.

PM1 (particulate matter with an aerodynamic diameter This paper presents a method for estimating cooking organic aerosol in ambient datasets based on the fractions of organic mass fragments at m/z 55 and 57: their data points fall into a V-shape in a scatter plot, with strongly influenced HOA data aligned to the right arm and strongly influenced COA data points aligned to the left arm. HR data show that this differentiation is mainly driven by the oxygen-containing ions C3H3O+ and C3H5O+, even though their contributions to m/z 55 and 57 are low compared to the reduced ions C4H7+ and C4H9+. A simple estimation method based on the markers m/z 55, 57, and 44 is developed here and allows for a first-order-estimation of COA in urban air. This study emphasizes the importance of cooking activities for ambient air quality and confirms the importance of chemical composition measurements with a high mass and time resolution.