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The European Copernicus programme plans to install a constellation of multiple polar orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We explore the impact of potential CO2M observations of column-averaged CO2 (XCO2), nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 domain around Berlin. For the quantification of anticipated XCO2 random and systematic errors we developed and applied new error parameterisation formulae based on artificial neural networks. For the interpretation of these data, we further established a CCFFDAS modelling chain from parameters of emission models to XCO2 and NO2 observations to simulate the 24 h periods preceeding simulated CO2M overpasses over the study area. For one overpass in winter and one in summer, we present a number of assessments of observation impact in terms of the posterior uncertainty in fossil fuel emissions on scales ranging from 2 to 200 km. This means the assessments include temporal and spatial scales typically not covered by inventories. The assessments differentiate the fossil fuel CO2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector.” We find that combined measurements of XCO2 and aerosols provide a powerful constraint on emissions from larger power plants; the uncertainty in fossil fuel emissions from the largest three power plants in the domain was reduced by 60%–90% after assimilating the observations. Likewise, these measurements achieve an uncertainty reduction for the other sector that increases when aggregated to larger spatial scales. When aggregated over Berlin the uncertainty reduction for the other sector varies between 28% and 48%. Our assessments show a considerable contribution of aerosol observations onboard CO2M to the constraint of the XCO2 measurements on emissions from all power plants and for the ot

De fijnstofnorm die we momenteel in Nederland gebruiken, is gebaseerd op de massa van alle fijnstofdeeltjes die in een kubieke meter lucht aanwezig zijn. Dat kan beter. Niet elk fijnstofdeeltje heeft namelijk dezelfde impact op de gezondheid. De gezondheidsimpact hangt samen met de reactiviteit van de deeltjes in het fijnstof, en deze wordt weer bepaald door andere eigenschappen zoals de chemische samenstelling en de grootte van de deeltjes. Zo zullen bijvoorbeeld de heel kleine deeltjes, die dus nauwelijks iets wegen, dieper in de longen terechtkomen.

The annual carbon dioxide fluxes used to create all graphs in the main text of McGrath et al, "European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020", submitted to Earth System Science Data.

Global trends in tropospheric nitrogen dioxide (NO2) have changed dramatically in the past decade. Here, we investigate tropospheric NO2 variabilities over United States, Europe, and E. China in 2005–2018 to explore the mechanisms governing the variation of this critical pollutant. We found large uncertainties in the trends of anthropogenic nitrogen oxides (NOx) emissions, for example, the reductions of NOx emissions, derived with different approaches and data sets, are in the range of 35%–50% over the United States and 15%–45% over Europe in 2005–2018. By contrast, the analysis in this work indicates declines of anthropogenic NOx emissions by about 40% and 25% over the United States and Europe, respectively, in 2005–2018, and about 20% over E. China in 2012–2018. However, the shift of major NOx sources from power generation to industrial and transportation sectors has led to noticeable diminishing effects in emission controls. Furthermore, satellite measurements exhibit the influence of NO2 background levels over the United States and Europe, which offset the impacts of anthropogenic emission declines, resulting in flatter trends of tropospheric NO2 over the United States and Europe. Our analysis further reveals underestimation of background NO2 by chemical transport models, which can lead to inaccurate interpretations of satellite measurements. We use surface in-situ NO2 observations to diagnose the satellite-observed NO2 trends and find top-down NOx emissions over urban grids represent the changes in anthropogenic NOx emissions better. This work highlights the importance of comprehensive applications of different analysis approaches to better characterizing atmospheric composition evolution.

We use the OMI-QA4ECV-v1.1 NO2 tropospheric columns over the 10-year 2008–2017 period to confront satellite-based trends in NO2 concentrations to those from the state-of-the-art regional chemistry-transport model CHIMERE and to evaluate the bottom-up anthropogenic and biogenic NOx emissions in Europe. A focus is made for the 30 most populated urban areas in Europe. Over urban areas in Western Europe, except for coastal cities, OMI confirms the drop in the simulated CHIMERE NO2 tropospheric columns based on the latest country emission official reporting. OMI hardly shows significant negative trends over Central and Eastern Europe urban areas. Increasing biogenic emissions helps reconciling CHIMERE and OMI trends over urban areas in Central Europe and over rural areas, confirming the importance of accounting for non-anthropogenic emissions to assess long-term trends. Over Eastern Europe, our results question emission reductions estimated for particular sectors and in particular the road transport, public power, and industrial emissions.

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs va

The changing composition of the atmosphere, driven by anthropogenic emissions, is the cause of anthropogenic climate change as well as deteriorating air quality. Emission inventories are essential to understand the contribution of various human activities, model and predict the changing atmospheric composition, and design cost-effective mitigation measures. At present, national emission inventories in South America (SA) focus on Greenhouse Gases (GHG) as part of their obligation to the United Nations Framework Convention for Climate Change (UNFCC) within the framework of their national communications. Emission inventories other than GHG in SA focus mainly on growing urban areas and megacities. Therefore, studies examining air quality at national, regional or continental scales in SA depend on (down-scaled) global emission inventories. This paper examines the emission estimates of air pollutants from various global inventories for five SA countries, namely Argentina, Brazil, Chile, Colombia and Peru. A more detailed analysis is conducted for the EDGAR and ECLIPSE emission inventories, in particular comparing local city-scale inventories of a major city in each country. Although total emissions between down-scaled global inventories and local city inventories are often comparable, large discrepancies exist between the sectoral contributions. This is critical, as the mitigation of poor air quality will depend on addressing the right sources. Potential sources of discrepancies between global and local inventories include the spatial distribution proxies, difference in emission factors used and/or the use of generic statistical country data when estimating emissions. This highlights the importance of using local information when generating national emission inventories, especially for air quality modeling and development of effective mitigation measures. This study represents the first step towards an increased understanding of the strengths and weaknesses of emissions i

Nitrogen dioxide (NO2) is a regulated air pollutant that is of particular concern in many cities, where concentrations are high. Emissions of nitrogen oxides to the atmosphere lead to the formation of ozone and particulate matter, with adverse impacts on human health and ecosystems. The effects of emissions are often assessed through modeling based on inventories relying on indirect information that is often outdated or incomplete. Here we show that NO2 measurements from the new, high-resolution TROPOMI satellite sensor can directly determine the strength and distribution of emissions from Paris. From the observed build-up of NO2 pollution, we find highest emissions on cold weekdays in February 2018, and lowest emissions on warm weekend days in spring 2018. The new measurements provide information on the spatio-temporal distribution of emissions within a large city, and suggest that Paris emissions in 2018 are only 5–15% below inventory estimates for 2011–2012, reflecting the difficulty of meeting NOx emission reduction targets., Atmospheric Remote Sensing

Climate models are important tools that are used for generating climate change projections, in which aerosol–climate interactions are one of the main sources of uncertainties. In order to quantify aerosol–radiation and aerosol–cloud interactions, detailed input of anthropogenic aerosol number emissions is necessary. However, the anthropogenic aerosol number emissions are usually converted from the corresponding mass emissions in pre-compiled emission inventories through a very simplistic method depending uniquely on chemical composition, particle size and density, which are defined for a few, very wide main source sectors. In this work, the anthropogenic particle number emissions converted from the AeroCom mass in the ECHAM-HAM climate model were replaced with the recently formulated number emissions from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model. In the GAINS model the emission number size distributions vary, for example, with respect to the fuel and technology. Special attention was paid to accumulation mode particles (particle diameter dp > 100nm) because of (i) their capability of acting as cloud condensation nuclei (CCN), thus forming cloud droplets and affecting Earth's radiation budget, and (ii) their dominant role in forming the coagulation sink and thus limiting the concentration of sub-100nm particles. In addition, the estimates of anthropogenic CCN formation, and thus the forcing from aerosol–climate interactions, are expected to be affected. Analysis of global particle number concentrations and size distributions reveals that GAINS implementation increases CCN concentration compared with AeroCom, with regional enhancement factors reaching values as high as 10. A comparison between modeled and observed concentrations shows that the increase in number concentration for accumulation mode particles agrees well with measurements, but it leads to a consistent underestimation of both nucleation mode and Aitken mode (dp

Santiago (33.5°S, 70.5°W), the capital of Chile, is frequently affected by extreme air pollution events during wintertime deteriorating air quality (AQ) and thus affecting the health of its population. Intense residential heating and on-road transport emissions combined with poor circulation and vertical mixing are the main factors responsible for these events. A modelling system composed of a chemistry-transport model (CHIMERE) and a meteorological model (WRF) was implemented to assess the AQ impacts of residential and transportation sources in the Santiago basin. A two-week period of July 2015 with various days with poor AQ was simulated focusing on the impact on AQ with respect to fully inhalable particles (PM2.5) and nitrogen oxides (NOX). Three emission scenarios, within the range of targeted reductions of the decontamination plan of Santiago, were tested; namely 50% reduction of residential emission, 50% reduction of transport emissions and the combination of both. An additional scenario decreasing transport emissions in 10% was carried out to examine whether a linear dependence of surface concentrations on changes in emissions exists. The system was validated against surface and vertically resolved meteorological measurements. The model reproduces the daily surface concentration variability from the AQ monitoring network of Santiago. However, the model not fully captures the emissions variations inferred from the observations which may be due to missing sources such as resuspension of dust. Results show that, during the period studied, although both residential and transportation sources contribute to observed AQ levels in Santiago, reducing transport emissions is more effective in terms of reducing the number of days with pollution events than decreasing residential combustion. This difference in impact is largely due to the spatial distribution of the emission sources. While most of the residential combustion is emitted in the outskirts of the city, most of