Large parts of the troposphere are affected by clouds, whose aqueous-phase chemistry differs significantly from gas-phase chemistry. Box-model studies have demonstrated that clouds influence the tropospheric oxidation capacity. However, most global atmospheric models do not represent this chemistry reasonably well and are largely limited to sulfur oxidation. Therefore, we have developed the Jülich Aqueous-phase Mechanism of Organic Chemistry (JAMOC), making a detailed in-cloud oxidation model of oxygenated volatile organic compounds (OVOCs) readily available for box as well as for regional and global simulations that are affordable with modern supercomputers. JAMOC includes the phase transfer of species containing up to ten carbon atoms, and the aqueous-phase reactions of a selection of species containing up to four carbon atoms, e.g., ethanol, acetaldehyde, glyoxal. The impact of in-cloud chemistry on tropospheric composition is assessed on a regional and global scale by performing a combination of box-model studies using the Chemistry As A Boxmodel Application (CAABA) and the global atmospheric model ECHAM/MESSy (EMAC). These models are capable to represent the described processes explicitly and integrate the corresponding ODE system with a Rosenbrock solver. 

Overall, the explicit in-cloud oxidation leads to a reduction of predicted OVOCs levels. By comparing EMAC's prediction of methanol abundance to spaceborne retrievals from the Infrared Atmospheric Sounding Interferometer (IASI), a reduction in EMAC's overestimation is observed in the tropics. Further, the in-cloud OVOC oxidation shifts the hydroperoxyl radicals (HO₂) production from the gas- to the aqueous-phase. As a result, the in-cloud destruction (scavenging) of ozone (O₃) by the superoxide anion (O₂^-) is enhanced and accompanied by a reduction in both sources and sinks of tropospheric O₃ in the gas phase. By considering only the in-cloud sulfur oxidation by O₃, about 13 Tg a^-1 of O₃ are scavenged, which increases to 336 Tg a^-1 when JAMOC is used. With the full oxidation scheme, the highest O₃ reduction of 12 % is predicted in the upper troposphere/lower stratosphere (UTLS). Based on the IASI O₃ retrievals, it is demonstrated that these changes in the free troposphere significantly reduce the modelled tropospheric O₃ columns, which are known to be generally overestimated by global atmospheric models. Finally, the relevance of aqueous-phase oxidation of organics for ozone in hazy polluted regions will be presented.  

How to cite: Rosanka, S., Sander, R., Franco, B., Wespes, C., Wahner, A., and Taraborrelli, D.: Influence of in-cloud oxidation of organic compounds on tropospheric ozone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10276, https://doi.org/10.5194/egusphere-egu21-10276, 2021.

Secondary organic aerosol (SOA) constitutes a major fraction of the total organic aerosol (OA) in the atmosphere. SOA is formed by the partitioning onto pre-existent particles of low vapor pressure products of the oxidation of volatile, intermediate volatility, and semivolatile organic compounds. Oxidation of the precursor molecules results a myriad of organic products making the detailed analysis of smog chamber experiments difficult and the incorporation of the corresponding results into chemical transport models (CTMs) challenging. The volatility basis set (VBS) is a framework that has been designed to help bridge the gap between laboratory measurements and CTMs. It describes the volatility distribution of the OA and the SOA. The parametrization of SOA formation for the VBS has been traditionally based on fitting yield measurements of smog chamber experiments. To reduce the uncertainty of this approach we developed an algorithm to estimate parameters such as volatility product distribution, effective vaporization enthalpy, and accommodation coefficient combining SOA yield measurements with thermograms (from thermodenuders) and areograms (from isothermal dilution chambers) from different experiments and laboratories. The algorithm was first evaluated with “pseudo-data” produced from the simulation of the corresponding processes assuming SOA with known properties. The results showed excellent agreement and low uncertainties when the volatility range and the mass loadings range of the yield measurements coincide. One of the major features of our approach is that it estimates the uncertainty of the resulting parameterization for different atmospheric conditions (temperature, concentration levels, etc.). In the last step of the work, the use of the algorithm with realistic smog laboratory data is demonstrated.

How to cite: Uruci, P., Drosatou, A. D., Sippial, D., and Pandis, S. N.: Estimation of secondary organic aerosol formation parameters for the Volatility Basis Set combining thermodenuder, isothermal dilution and yields measurements , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10088, https://doi.org/10.5194/egusphere-egu21-10088, 2021.

Biomass burning (BB) is a significant contributor to air pollution on global, regional and local scale with impacts on air quality, public health and climate. Anhydrosugars (levoglucosan, mannosan and galactocan) and methoxyphenols (guaiacol, creosol, etc.) are important tracer compounds emitted through biomass burning. Once emitted, they can undergo complex multiphase chemistry in the atmosphere contributing to secondary organic aerosol formation. However, their chemical multiphase processing is not yet well understood and investigated by models. Therefore, the present study aimed at a better understanding of the multiphase chemistry of these BB trace species by means of detailed model studies with a new developed detailed chemical CAPRAM biomass burning module (CAPRAM-BB). This module was developed based on the kinetic data from the laser flash photolysis measurements in our lab at TROPOS and other literature studies. The developed CAPRAM-BB module includes 2991 reactions (thereof 9 phase transfers and 2982 aqueous-phase reactions). By coupling with the multiphase chemistry mechanism MCMv3.2/CAPRAM4.0 and the extended CAPRAM aromatics (CAPRAM-AM1.0) and halogen modules (CAPRAM-HM3.0), it is being applied for some residential wood burning cases in Europe and wildfire cases in the US. Our model results show that the BB chemistry could significantly affect the budgets of important atmospheric oxidants such as H₂O₂ and HONO, and contribute to the SOA formation especially the fraction of brown carbon and substituted organic acids.

How to cite: He, L., Hoffmann, E. H., Tilgner, A., and Herrmann, H.: Modelling the Tropospheric Multiphase Chemistry of Biomass Burning Trace Compounds Using the Chemical Aqueous Phase Radical Mechanism (CAPRAM), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10731, https://doi.org/10.5194/egusphere-egu21-10731, 2021.

Photochemistry of tropospheric CS₂, a new chemical pathway

Yuanzhe Li¹, Kazuki Kamezaki¹ and Sebastian Danielache¹

¹ Faculty of Science and Technology, Sophia University

Abstract

Carbon disulfide (CS₂) is an atmospheric trace gas and is mainly produced by anthropogenic emissions. Its oxidation end-products in the atmosphere are carbonyl sulfide (OCS) and sulfur dioxide (SO₂). Therefore, CS₂ indirectly contributes to the production of sulfate aerosol, which influences atmospheric radiative properties and stratospheric ozone depletion.

Current understanding suggests that the main sink of CS₂ is the reaction with the OH radical which shares of 75-88% CS₂ global removal (Khan et al., 2017). This reaction pathway generates an adduct SCSOH, followed by oxidation with O₂ to form OCS and SO₂. UV induced processes are usually considered irrelevant in the troposphere. Tropospheric CS₂ photo-oxidation mechanism was first suggested by Wine et al. (1981). The CS₂ UV-absorption spectrum has a strong absorption band (280-360 nm), which generates a photo-excited (CS₂(³A₂) often presented as CS₂^* state) fragment, which gets further oxidized by O₂ to produce OCS and SO₂. The solar flux spectrum in the troposphere satisfies conditions for a CS₂ photo-excitation, enabling a potential CS₂ photo-oxidation pathway in the troposphere.

In this study, CS₂ photochemistry is revised and studied by a 1-D atmospheric model (PATMO) capable of handling photochemistry with a high-resolution spectrum. Simulated main reduced sulfur species (CS₂, OCS and SO₂) reproduce field measurements. Under strong light conditions, the CS₂ photo-excitation reaction is followed by two CS₂^* excited state quenching reactions. The reaction rate r for the net CS₂ photo-induced oxidation and CS₂ + OH reactions at 1 km are 71 and 26 molecule cm^-3 s^-1 respectively. These results indicate that, under favorable light conditions photochemistry is a relevant tropospheric sink of CS₂.

References

Khan, A., Razis, B., Gillespie, S., Percival, C., Shallcross, D., Global analysis of carbon disulfide (CS₂) using the 3-D chemistry transport model STOCHEM, Aims Environ. Sci. 2017, 4, 484–501.

Wine, P. H., Chameides, W. L., Ravishankara, A. R., Potential role of CS₂ photooxidation in tropospheric sulfur chemistry, Geophys. Res. Lett. 1981, 8, 543-546.

How to cite: Li, Y., Kamezaki, K., and Danielache, S.: Photochemistry of tropospheric CS2, a new chemical pathway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4062, https://doi.org/10.5194/egusphere-egu21-4062, 2021.

Secondary organic aerosol (SOA) can be formed in the atmosphere through oxidation of volatile (VOCs), intermediate volatility (IVOCs) and semivolatile organic compounds (SVOCs), and condensation of their less volatile products to the particulate phase. While there has been a lot of progress with the simulation of the VOC chemistry, the simulation of the IVOCs remains challenging. In this study, we develop a new approach for the treatment of these compounds in chemical transport models, treating them as lumped species, similar to the VOCs. The new species are implemented in the SAPRC gas-phase chemical mechanism. We introduce four new lumped species representing larger alkanes, two species for polyaromatic hydrocarbons (PAHs) and one new lumped species representing aromatics, all in the IVOC volatility range. Their gas-phase chemistry is assumed to be analogous to that of the large alkanes and aromatics currently in the SAPRC mechanism but with appropriate parameters. The SOA yields for these additional species were estimated for low and high-NOx conditions following the Volatility Basis Set framework and using the available results of smog chamber studies. As most emission inventories do not include IVOCs, we estimated their emissions starting from road transport using existing non-methane hydrocarbons emissions and emission factors of individual IVOCs from laboratory studies. The total IVOC emissions from diesel vehicles for Europe were significantly higher than those coming from gasoline vehicles. The emissions and extended mechanism were implemented in PMCAMx and were used to simulate the EUCAARI intensive period. Cyclic alkanes, which have both high SOA yields and high emissions, were a major SOA precursor group. The contribution of the various IVOCs to SOA formation, and their overall role is discussed. Significant remaining uncertainties are summarized.

How to cite: Manavi, S.-E. and Pandis, S.: Improving the simulation of Intermediate Volatility Compounds (IVOCs) in chemical transport models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11474, https://doi.org/10.5194/egusphere-egu21-11474, 2021.

The COVID-19 pandemic struck China in January 2020 and the rest of the world from February 2020 onwards. Public authorities enforced several kinds of lockdowns in order to limit the spread of the pandemic and reduce its impact on the health system: at the height of the first wave of the pandemic, more than one human in two was subjected to a lockdown, with associated disruption in local and international travel, industry, tourism etc. These lockdowns had a profound effect on anthropogenic emissions of aerosol, trace gases and greenhouse gases; in this work we focus on aerosols and a selection of trace gases.

The Integrated Forecasting System (IFS) of ECMWF is core of the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including reactive gases, as well as aerosol and greenhouse gases. In this work, we use two emission reduction scenario with an experimental version of the IFS in its CAMS configuration: a global and a European one.  Global simulations of aerosols were carried out with these two scenarii and compared to a reference simulation without any COVID-19 impact, and to worldwide observations of PM2.5, AOD and trace gases.

The simulated PM2.5 using the global emission reduction scenario were found to reproduce quite accurately the observed evolution over China, India and United States. Over Europe, the simulated PM2.5 using the European reduction scenario were closer to observations and appeared more realistic. India was the only place where a significant impact on AOD and on temperature and radiation from the COVID-19 lockdowns was simulated. These simulations also provided information on how the aerosol speciation was altered by the COVID-19 lockdowns: over Europe and the U.S., most of the decrease in surface aerosols was simulated to come from nitrate aerosols. Over the U.S., this matched well with observations of speciated aerosols at surface.

How to cite: Pelletier, S., Rémy, S., Kipling, Z., Vilardell, M. G., Bouarar, I., Engelen, R., Flemming, J., Huijnen, V., and Meziane, M.: Simulation of the impact of COVID-19 lockdowns on aerosols and radiation at a global and European scale in CAMS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16450, https://doi.org/10.5194/egusphere-egu21-16450, 2021.

To hinder the circulation of the COVID-19 virus, European governments implemented emergency measures going from light social distancing to strict lockdowns, depending on the country. As a consequence, many industries, businesses and transport networks were forced to either close down or drastically reduce their activity, which resulted in an unprecedented drop of anthropogenic emissions. This work presents the Copernicus Atmosphere Monitoring Service (CAMS) European regional emission reduction factors associated to the COVID-19 mobility restrictions (CAMS-REG_ERF-COVID19), an open source dataset of daily-, sector-, pollutant- and country-dependent emission reduction factors for Europe linked to the COVID-19 pandemic. The resulting dataset covers a total of six emission sectors, including: road transport, energy industry, manufacturing industry, residential and commercial combustion, aviation and shipping. The time period covered by the dataset includes the first and second waves of the disease ocurred during 2020, starting from 21 February, when the first European localised lockdown was implemented in the region of Lombardy (Italy), until 31 December, when COVID-19 transmission remained widespread and several countries had nationwide restrictions still in place. The CAMS-REG_ERF-COVID19 dataset is based on a wide range of information sources and approaches, including open access and measured activity data and meteorological data, as well as the use of machine learning techniques. We combined the computed emission reduction factors with the Copernicus CAMS European gridded emission inventory to spatially (0.1x0.05 degrees) and temporally (daily) quantify reductions in 2020 primary emissions from both criteria pollutants (NO_x, SO₂, NMVOC, NH₃, CO, PM₁₀ and PM_2.5) and greenhouse gases (CO₂ fossil fuel, CO₂ biofuel and CH₄), as well as to assess the contribution of each pollutant sector and country to the overall reductions. The resulting gridded and time-resolved emission reductions suggest an heterogeneous impact of the COVID-19 across pollutants, sectors and countries.

How to cite: Guevara, M., Jorba, O., Petetin, H., Denier Van Der Gon, H., Kuenen, J., Super, I., Peuch, V.-H., and Pérez García-Pando, C.: Quantification of the Emission Changes in Europe During 2020 Due to the COVID-19 Mobility Restrictions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5894, https://doi.org/10.5194/egusphere-egu21-5894, 2021.

Corona lockdown measures caused unprecedented emission reductions in many parts of world. However, this does not linearly translate into improved air quality, since weather phenomena like precipitation, wind and solar radiation also show a significant impact on pollutant concentration patterns. The aim of this study is to disentangle effects of emission reduction and meteorology on the air quality in Central Europe during the first major lockdown from March to June 2020. For this purpose, the Community Multiscale Air Quality Modeling System (CMAQ) was used with updated emission data for the year 2020, including time profiles for sectors and countries that approximate the lockdown emission reductions. The contributions of street traffic, air traffic, ship traffic, residential heating and industry to NO₂, O₃ and PM_2.5 concentrations were investigated. Meteorological data was derived from the regional COSMO model in CLimate Mode (COSMO-CLM). Additional city scale measurements were used to account for exceptional weather conditions as well as emission reduction effects at hotspots like traffic stations. Therefore, selected air pollutant and meteorological measurement data in the cities of Hamburg, Liége and Marseille are compared against the statistical trend of 2015 to 2019.

How to cite: Badeke, R., Matthias, V., Quante, M., Petrik, R., Arndt, J., Ramacher, M., Schwarzkopf, D., Fink, L., Feldner, J., and Link, E.-M.: Air quality improvements caused by COVID-19 lockdown measures in Central Europe – contributions of emission sectors and the meteorological situation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12394, https://doi.org/10.5194/egusphere-egu21-12394, 2021.

The mobility restrictions implemented to slow down the transmission of the new coronavirus disease (COVID-19) drastically altered Spanish anthropogenic emissions in several sectors, leading to substantial impacts on air pollutant concentrations. In order to reliably quantify these changes, the confounding effects of meteorological variability need to be properly taken into account. We thus designed an innovative methodology relying on the use of machine learning (ML) models fed with ERA5 meteorological reanalysis data and other time features, to estimate more accurately the so-called business-as-usual (BAU) pollutant concentrations that would have been observed in the absence of lockdown (Petetin et al., 2020). The difference with concentrations actually observed during the lockdown give meteorology-normalized estimates of the AQ changes due to the altered anthropogenic emission forcing, independently from the meteorological variability. Importantly, our methodology includes a conservative estimation of the uncertainties, which allows to highlight statistically significant changes. This study focuses on NO2 and O3. We applied this analysis for a selection of urban background and traffic stations covering more than 50 Spanish provinces and islands. Validation results indicate that the method usually performs well for estimating BAU concentrations (mean absolute bias below +6%, root mean square error around 25-30% and correlation above 0.80).

The COVID-19-related lockdown has induced a strong reduction (-50% on average) of NO2 concentrations in Spanish urban areas, although with some spatial variability among the provinces. In largest cities, stronger reductions were found at traffic stations compared to urban background ones, reflecting the major impact of the lockdown on traffic emissions. Substantial discrepancies with changes obtained considering a climatological averaged NO2 concentrations were found, highlighting the interest of such ML-based weather-normalization method. Compared to NO2, the impact on O3 is lower and more heterogeneous. In many cities, O3 levels slightly increased (likely due to a reduced titration by NO), but these increments often remain within the (95% confidence level) uncertainties of our methodology. However, during the most stringent phase of the lockdown (beginning of April and the few following days), a clearer O3 increase is found, reaching the statistical significance in several Spanish cities (e.g. Albacete, Barcelona, Castellón, Mallorca, Murcia, Málaga).

These results are of strong interest for quantifying the corresponding health impacts of these AQ changes, especially for showing the potential trade-offs between health benefits induced by the reduction of NO2 and enhanced mortality due to higher O3.

Petetin, H., Bowdalo, D., Soret, A., Guevara, M., Jorba, O., Serradell, K., and Pérez García-Pando, C.: Meteorology-normalized impact of the COVID-19 lockdown upon NO₂ pollution in Spain, Atmos. Chem. Phys., 20, 11119–11141, https://doi.org/10.5194/acp-20-11119-2020, 2020.

How to cite: Petetin, H., Bowdalo, D., Achebak, H., Soret, A., Guevara, M., Jorba, O., Serradell, K., Quijal-Zamorano, M., Ballester, J., and Pérez García-Pando, C.: Meteorology-normalized impact of COVID-19 lockdown upon NO2 and O3 in Spain , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14422, https://doi.org/10.5194/egusphere-egu21-14422, 2021.

In response to changes in environmental conditions (e.g., temperature, radiation, soil moisture), plants emit biogenic volatile organic compounds (BVOCs). In the large family of BVOCs, isoprene dominates and plays an important role in atmospheric chemistry. Once released in the atmosphere, isoprene influences levels of ozone, thus affecting both climate and air quality. In turn, climate change may alter isoprene emissions by increasing the occurrence and intensity of severe thermal and water stresses that alter plant functioning. To better constrain the evolution of isoprene emissions under future climates, it is critical to reduce the uncertainties in global and regional estimates of isoprene under present climate. Part of these uncertainties is related to the impact of water stress on isoprene. Recently, the BVOC emission model MEGAN has adopted a more sophisticated soil moisture activity factor γ_sm which does not only account, as previously, for soil moisture available to plants but also links isoprene emissions to photosynthesis and plant water stress.

To assess the effects of soil moisture on isoprene emissions and, lastly, on ozone levels in the Euro-Mediterranean region, we use the regional climate model RegCM4.7, coupled to the land surface model CLM4.5, MEGAN2.1 and a chemistry module (RegCM4.7chem-CLM4.5-MEGAN2.1). We have performed a control experiment over 1987-2016 (with a 5-yr spin-up) at a horizontal resolution of 0.22°. Model output from the control experiment is used to initialize RegCM4.7chem-CLM4.5-MEGAN2.1 for the 10 most dry/wet summers (May-August) selected referring to the 1970-2016 precipitation climatology. Each May-August experiment is run with the old and with the new MEGAN soil moisture activity factor γ_sm.  The results are then compared with a simulation whit no soil moisture activity factor. Both activity factors γ_sm reduce isoprene emissions under water deficit.

During dry summers, the old soil moisture activity factor reduces isoprene emissions homogeneously over the model domain by nearly 100%, while ozone levels decrease by around 10%. When the new γ_sm is used,isoprene emissions are reduced with a patchy pattern by 10-20%, while ground-surface ozone levels diminish homogeneously by few percent over the southern part of the model domain.

How to cite: Strada, S., Pozzer, A., Giuliani, G., Coppola, E., Solmon, F., and Giorgi, F.: Sensitivities of isoprene emissions to soil moisture and impacts on surface ozone levels as simulated over the Euro-Mediterranean region by the regional climate model RegCM4.7chem-CLM4.5-MEGAN2.1, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3005, https://doi.org/10.5194/egusphere-egu21-3005, 2021.

Shipping has major contribution to emissions of air pollutants like NOx and SO2 and the global maritime transport volumes are projected to increase significantly. The Mediterranean Sea is a region with dense ship traffic. Air quality observations in many cities along the Mediterranean coast indicate high levels of NO2 and particulate matter with significant contributions from ship emissions.
To quantify the current impact of shipping on air pollution, models for ship emissions and atmospheric transport can be applied, but model predictions may differ from observational data. To determine how well regional scale chemistry transport models simulate pollutant concentrations, the model outputs from several regional scale models were compared against each other and to measured data.
In the framework of the EU H2020 project SCIPPER, ship emission model STEAM and the regional scale models CMAQ and CHIMERE model were applied on a modelling domain covering the Mediterranean Sea. Modeling results were compared to air quality observations at coastal locations. The impact of shipping in the Mediterranean Sea was extracted from the model excluding shipping emissions.

How to cite: Fink, L., Matthias, V., Karl, M., Petrik, R., Majamäki, E., Jalkanen, J.-P., Oppo, S., and Kranenburg, R.: The contribution of shipping to air pollution in the Mediterranean region – a model evaluation study , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8344, https://doi.org/10.5194/egusphere-egu21-8344, 2021.

Air quality is a key aspect of present environmental discussions with nitrogen oxides (NO_X = NO + NO₂) has become a decisive element and impact factor for air quality planning. Millions of people are exposed by NO_2, especially in urban areas near traffic sites, leading to increased mortality rates. As the annual limit value of 40 μg/m³, introduced by the European Ambient Air Quality Directive (EC, 2008), is currently exceeded by about 39 % (UBA, 2019), in Germany an estimated number of 13.100 premature deaths are caused by NO₂ (EEA, 2018). The origin and formation processes of NO_X are well documented in literature for long: NO mainly originates from incomplete combustion (Granier et al., 2011; Vestreng et al., 2009), with NO₂ formed as a photochemical reaction product (Finlayson-Pitts and Pitts, 2000; Leighton, 1961). Therefore, to further improve the ambient air quality using cost-effective mitigation strategies, this requires for quantifying the contribution of the ambient air pollution by source sectors and regions of their origin (Belis et al., 2020).

Applying chemical transport models (CTMs) for source attribution (SA), one can distinguish between contributions and impacts. Methods to estimate contributions are known as labeling (Kranenburg et al., 2013) or tagging (Wang et al., 2009; Wagstrom et al., 2008) approaches and are based on conservation of mass. In contrast, sensitivity simulations, such as the top-down brute force (BF) technique, can be used to quantify the impact to different emission reductions (Clappier et al., 2017; Thunis et al., 2019). As the BF approach in theory is only designed for impact studies, the calculation of contributions can result in incorrect estimates which is dependent on the linearity of the considered component (Clappier et al., 2017; Thunis et al., 2019). Therefore, impact studies can only be employed under certain restrictions and their application range needs to be predefined first (Thunis et al., 2020).

Previous studies primarily focused on PM when comparing different approaches for SA. Therefore, we conducted a SA study by performing air pollution simulations using the LOTOS-EUROS CTM across Germany of January 1^st to December 31^st, 2018 for NO_X. We enhanced the understanding of limitations to non-linear interaction terms and defined the potential application range for SA purposes using impact studies of NO_X, by comparing the labeling approach implemented in the LOTOS-EUROS CTM to the BF technique.

First results indicate that impact studies cannot be used to estimate contributions of NO due to their non-linear relations and inconsistent mass conservation. Even though differences for NO₂ are smaller, it is not recommended to apply the BF technique here either. However, considering that non-emission sources cannot be separated from each other in impact studies, it is further advised not to apply this method for NO_X.

How to cite: Thürkow, M., Pültz, J., and Schaap, M.: A mitigation study for air pollution management across Germany for NOX (NO + NO2) with the LOTOS-EUROS CTM – Part I: Comparing the labeling and brute force technique for source attribution., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5862, https://doi.org/10.5194/egusphere-egu21-5862, 2021.

Air Quality in Berlin is a particular problem during winter episodes. During this episodes, local emissions are only one factor contributing to the high concentrations. The other factors are the lowered height of the planetary boundary layer and the advection of pollutants, some of which are produced in Eastern Europe. To trace the share of total pollution in Berlin for 2016-18 back to its origins, the Chemistry Transport Model (CTM) LOTOS-EUROS v2.1 (LOng Term Ozone Simulation EURopean Operational Smog, invented by TNO, Netherlands) is used, which also provides a labelling approach. Some specifications were made for the emission datasets used to drive the model, including emission dependencies on temperature (e.g. cold engine starts and heating degree-days for households).

The model results are evaluated using the German AirBase monitoring sites. An incremental approach (Lenschow et al., 2001) is used for the evaluation and estimation of the urban share of Berlin. The focus is on Particulate Matter (PM): PM10, PM2.5, and the coarse-mode fraction (PM10-PM2.5). Due to the seasonal variability of PM and its composition, seasonal differentiation is investigated. The labelling approach provided in LOTOS-EUROS allows to distinguish between the sources relevant for Berlin’s PM pollution, with the focus of this work on local contributions such as households and traffic on the one hand and regional contributions from Berlin itself and Germany’s Eastern European neighbors (Poland and the Czech Republic) on the other hand.

This study is in relation to the “Berliner Luftreinhalteplan” (Berlin Clean Air Plan).

How to cite: Pültz, J., Thürkow, M., Banzhaf, S., Kranenburg, R., and Schaap, M.: Source attribution of Particulate Matter for Berlin 2016-18, a study using the LOTOS-EUROS CTM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12991, https://doi.org/10.5194/egusphere-egu21-12991, 2021.

The second most important greenhouse gas in atmosphere after carbon dioxide (CO₂) is methane, CH₄. The limited data of surface methane observations in Arctic makes it difficult to quantify the impact of methane emissions from major regional anthropogenic and biogenic sources on this region. This gap is partially filled by long-term observations at arctic and subarctic stations. According to these observations, since 2005, there has been a noticeable increase in the surface methane concentration. The reasons of this increase are still not fully understood. This work provides quantitative estimates of possible contribution into surface CH₄ observed long-term variability from the most important regional sources of methane emissions.

To analyze variations in surface methane concentration was used the data from observations at background monitoring stations, as well as numerical calculations performed by GEOS-Chem chemical-transport model, which is widely used in international community for calculating the fields of chemically active and greenhouse gases.

How to cite: Shtabkin, Y., Moiseenko, K., and Skorokhod, A.: Sources of atmospheric methane in Arctic: observations and model simulation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13688, https://doi.org/10.5194/egusphere-egu21-13688, 2021.

Expediting urbanization has triggered an increase in cardiopulmonary diseases attributable to fine-particulate air pollution. Air Quality models simulate the dilution and dispersion of air pollutants that affect the atmosphere, contributing crucially to the comprehension of its processes. Air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) provide open access to accurate and reliable information but in a coarse resolution. Data-driven models can downscale the forecasts from deterministic air quality models on the basis of reliable measurements. Low-cost air quality sensors are widely known for their increased spatial coverage and economic operational costs, but usually, their reliability is in dispute. In this study, a dense network of calibrated PM2.5 measurements installed in the city of Patras is combined with CAMS forecasts and statistical approaches to generate 24h forecasts of PM_2.5 concentrations in an urban area of Greece. The implemented techniques are the analog ensemble (AnEn) and the Long Short-Term Memory (LSTM) network. Auxiliary variables of meteorological origin were also utilized. The required forecasts were retrieved from the European Center for Medium-Range Weather Forecasts (ECMWF), and were pin-pointed to the location of the air quality monitoring stations. The results showed that both methods had comparable performance, with low bias and relatively small errors. In the stations with high PM2.5 levels, AnEn performed better, whereas in the stations and seasons with moderate concentrations LSTM outperformed. A comprehensive validation is presented and discussed. AnEn and LSTM methods were proved reliable tools for air pollution forecasting and can be used for other regions with small modifications.

How to cite: Pappa, A. and Kioutsioukis, I.: High-resolution PM2.5 forecasting using CAMS predictions, low-cost sensors and ensemble techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15960, https://doi.org/10.5194/egusphere-egu21-15960, 2021.

In order to reach legal air quality limits, several municipalities in Germany have decided to take actions if concentrations of NO₂ and Particulate Matter (PM) exceed certain thresholds. The decision for concrete measures is usually based on observations or use the Direct Model Output (DMO) of air quality models. However, due to large biases of state-of-the-art numerical air quality models, the skill of DMO forecasts to predict periods of polluted air up to four days ahead is very limited.

The project LQ-WARN aims to develop a system for warning of poor air quality based on Model Output Statistics (MOS). Therefore, air quality related observations, model results provided by the Copernicus Atmosphere Monitoring Service (CAMS) and meteorological parameters from the ECMWF numerical weather prediction model are used as predictors to forecast the air quality by applying Multiple Linear Regression (MLR). In this way MOS equations are calculated for four seasons. The final forecast product will comprise post-processed probabilistic as well as deterministic (e.g. mass concentration) parameters for the species NO₂, O₃, PM₁₀ and PM_2.5. Forecasts will be available for several hundred German locations and cover lead times up to 96 hours.

Here, we show first results of our phase 1 MOS prototype, for which observational, meteorological and empirical predictors are applied. Despite of the preliminary exclusion of CAMS predictors, the verifications of the MOS equations imply a considerable reduction of variance and a significant reduction of RMSE (Root Mean Square Error) compared to the climatological values for all four species. Hence, the MOS system can already provide a reasonably good air quality forecast. Furthermore, our analysis of used meteorological predictors, enables a detailed analysis of the importance of specific meteorological parameters for improved statistical air quality forecasts.  As an outlook we will provide detailed information about the final phase 2 LQ-WARN product, which will also include the MOS predictors of CAMS and is expected to be launched in pre-operational mode by 2022.

How to cite: Robrecht, S., Lambert, A., and Gilge, S.: The LQ-WARN Project – Development of a Model Output Statistics Product for Air Quality Warnings, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-882, https://doi.org/10.5194/egusphere-egu21-882, 2021.

Emission data of trace gases and aerosols are crucial for atmospheric chemistry models. Since in general emissions cannot be measured directly, they are estimated using various proxy data. Available inventories  contain annual  values of trace gas and aerosol emissions within   given areas, and  further split into polluter groups such as road traffic or industry. This separation  does not take current meteorological and societal effects into account. Thus, the emission data is known to include possibly large uncertainties.

In this work, we develop a system to assess the contribution and their uncertainties of  different source categories toe air pollution. As observations of pollutants cannot be directly  linked to their source, the four-dimensional variational data assimilation system of the EURopean Air pollution Dispersion – Inverse Model (EURAD-IM) is extended towards a polluter source specific emission correction method. Therefore, the possibility of exploiting different spatial distributions, diurnal cycles, and chemical compositions of the polluter groups is investigated on the model domain of North Rhine-Westfalia, Germany, with 1km x 1km horizontal resolution, where emission by road traffic and industry are the dominant sources for most trace gases and aerosol. As a first approach, we rely on the assumption that pollutants of the same emission sector can be  assigned to the same correction factor. From the simulations, separation criteria between different pollution sources are derived as a basis of a decision algorithm applying a random forest method. We found that this system is able to separate emissions between important polluter groups like traffic, industry, and agriculture at least in the cases of high emissions, in well observed areas and during suitable meteorological situations. This means the system performes best when assimilating observations from measurement stations leeward of emission sources and thus integrating sufficient information content to characterize the polluter.

How to cite: Backes, P., Franke, P., Lange, A. C., Hendrik, E., and Astrid, K.-S.: Towards polluter group specific emission corrections with 4D-Var data assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-689, https://doi.org/10.5194/egusphere-egu21-689, 2021.

Awareness of air pollution impacts on public health is quickly increasing, especially in urban areas where legal air quality (AQ) limits are often exceeded. This awareness has driven policymakers to minimize citizens' exposure not only by direct legislative control in emissions (i.e., the application of a Low Emission Zone), but also by applying mobility restrictions to modify traffic patterns, and by the use of forecasted warnings to alert citizens of air pollution episodes. The European AQ directives encourage the use of numerical models to support the design and evaluation of such strategies.

In this framework, we present a versatile AQ model, CALIOPE-Urban (Benavides et al., 2019), able to address the threefold objectives to (i) compute urban air quality forecast at the street-scale resolution; (ii) to perform reanalysis studies of historical periods using a bias correction methodology that preserves the model spatial variability; and (iii) to simulate the traffic flow response to the application of different traffic restrictions and their effect on urban AQ.

In this contribution, we discuss two specific applications. On the one hand, CALIOPE-Urban is used to estimate the NO2 levels in the city of Barcelona (Spain) during the entire year of 2019. To do so, we report accurate maps of NO2 levels during the whole year by consistently integrating the AQ model data with publicly available observations from the official monitoring network in Catalonia (XVPCA) available in Barcelona by means of a bias correction method. On the other hand, the macroscopic traffic simulator BCN-VML (Rodriguez-Rey et al. 2021) coupled with CALIOPE-Urban is used to assess the AQ impact of the traffic flow-induced changes after the application of a traffic restriction policy. 

References

Benavides, J., Snyder, M., Guevara, M., Soret, A., Pérez García-Pando, C., Amato, F., Querol, X., and Jorba, O.: CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain), Geosci. Model Dev., 12, 2811–2835, https://doi.org/10.5194/gmd-12-2811-2019, 2019.

Rodriguez-Rey, D., Guevara, M., Linares, MP., Casanovas, J., Salmerón, J., Soret, A., Jorba, O., Tena, C., Pérez García-Pando, C.: A coupled macroscopic traffic and pollutant emission modelling system for Barcelona, Transportation Research Part D, accepted for publication.

How to cite: Mateu Armengol, J., Rodriguez-Rey, D., Benavides, J., Jorba, O., Guevara, M., Pérez García-Pando, C., and Soret Miravet, A.: Advances on urban air quality modeling: bias correction approach for estimated annual NO2 levels and macroscopic traffic simulators for scenario planning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14178, https://doi.org/10.5194/egusphere-egu21-14178, 2021.

This study presents the evaluation of the high-resolution air pollution model UBMv10, which has been set-up for a 2,900,000 km² domain covering Norway, Sweden, Finland and Denmark with a 1 km x 1 km resolution and run for the time period 1979-2018. The UBMv10 is coupled to a long-range transport-chemistry model, DEHM, for boundary conditions. High-resolution emission data input and measurements of urban and rural air pollution concentrations have been obtained within the NordicWelfAir project from the four countries, in order to provide input and basis for evaluation of the UBM model.

In the NordicWelfAir project, the modelled hourly mean concentrations of air pollutants for the 40 year time period on this high resolution are applied in various epidemiological studies of the link between air pollution and health effects. The model results represent concentrations at the rural and urban background local scale level which in this study are evaluated for the components NO₂, O₃ and PM_2.5, which are the most important components to address when studying health effects of air pollution.

The simplicity of the model makes it possible to perform model runs for a combination of large domains with high resolution and long time periods that is currently very difficult to obtain with more comprehensive Eulerian high-resolution models, which take much longer time to run, since they are limited by the Courant–Friedrichs–Lewy (CFL) stability criteria. When studying the long-term effects of air pollution components, e.g. with the home address of individuals in a cohort as proxy, these high-resolution model runs are required.

The evaluation is part of a study with the aim to investigate, how well the UBM model with its relatively simple description of atmospheric dispersion and chemistry captures the temporal and spatial variations in the four Nordic countries. In general, the model performs relatively well for describing the temporal variations with correlation coefficients around 0.5-0.8. The model has a tendency to overestimate NO₂ levels with a few µg for all four countries, and overestimate PM_2.5 with for Norway and Sweden with 3-5 µg across all stations.

The coupled model setup will be presented together with examples of 40 years of high-resolution model results for the four Nordic countries as well as the results of the model evaluation against measurements in the domain.

How to cite: Frohn, L. M., Brandt, J., Andersson, C., Anderssen, C., Bennet, C., Christensen, J. H., Im, U., Karvosenoja, N., Kukkonen, J., Lopez-Aparicio, S., Nielsen, O.-K., Palamarchuk, Y., Paunu, V.-V., Smith Plejdrup, M., Segersson, D., Sofiev, M., and Geels, C.: Evaluation of high-resolution air pollution modelling for the continental Nordic countries, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15634, https://doi.org/10.5194/egusphere-egu21-15634, 2021.

Regional atmospheric chemistry models are adopted for simulating concentrations of atmospheric components at high resolution and quantifying the impact of localized emissions (e.g. industrial and urban clusters) on the non-linear chemical processes, e.g. ozone production. However, their evaluation is challenging due to the limited availability of high spatiotemporally resolved reference datasets. For the same reason, the vertical distribution of pollutants simulated by the model is especially arduous to assess.

Here, we present regional atmospheric chemistry model studies with spatial resolution up to 2.2 × 2.2 km² focused around Germany for May 2018 using the MECO(n) model system. Using a network of surface concentration measurements at background, near traffic and industrial locations, we evaluate the spatial distribution of NO₂ simulated by the model. The highly resolved model together with a comparable resolution and up-to-date input emissions inventory, was found to perform best in reproducing the spatial distribution of NO₂ surface volume mixing ratios (VMRs). We propose a computationally efficient approach to account for the diurnal and day of the week variability of input anthropogenic emissions (e.g. from road transport), which proved to be crucial for resolving the temporal variability of NO₂ surface VMRs.

The simulated NO₂ tropospheric vertical column densities were evaluated by employing the measurements of a 4-azimuth MAX-DOAS instrument in Mainz. Generally, such comparisons do not account for the spatial sensitivity volume of the MAX-DOAS measurements, the change of sensitivity within this volume and the spatial heterogeneity of NO₂. We therefore apply a consistent approach of comparison of the differential slant column densities (dSCDs), which overcomes these limitations. Moreover, the dSCDs are obtained for several elevation and azimuth angles, which are characterized by distinctive sensitivity for different vertical levels within the boundary layer and different horizontal representativeness. Hence, also an evaluation of the model in simulating the vertical distribution of NO₂ can be performed with this approach using continuous MAX-DOAS measurements spanning long time periods. We found that the model performs well with respect to the measured dSCDs at low elevation angles (< 8°) with an overall bias between +14 and -9%, and Pearson correlation coefficients between 0.5 and 0.8 for the different azimuth viewing directions.

How to cite: Kumar, V., Remmers, J., Beirle, S., Kerkweg, A., Lelieveld, J., Mertens, M., Pozzer, A., Steil, B., and Wagner, T.: Evaluation of a high resolution regional atmospheric chemistry model using MAX-DOAS and in situ NO2 measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4989, https://doi.org/10.5194/egusphere-egu21-4989, 2021.

Reducing air pollution, which is the world's largest single environmental health risk, demands better-informed air quality policies. Consequently, multi-scale air quality models are being developed with the goal to resolve cities. One of the major challenges in such model systems is to accurately represent all large- and regional-scale processes that may critically determine the background concentration levels over a given city. This is particularly true for longer-lived species such as aerosols, for which background levels often dominate the concentration levels, even within the city. Furthermore, the heterogeneous local emissions, and complex dispersion in the city have to be considered carefully.

In this study, the impact of processes across a wide range of scales on background concentrations over Switzerland and the city of Zurich was modelled by performing one year of nested European and Swiss national COSMO-ART simulations to obtain adequate boundary conditions for gas-phase chemical, aerosol and meteorological conditions for city-resolving simulations. The regional climate chemistry model COSMO-ART (Vogel et al. 2009) was used in a 1-way coupled mode. The outer, European, domain, which was driven by chemical boundary conditions from the global MOZART model, had a 6.6 km horizontal resolution and the inner, Swiss, domain one of 2.2 km. For the city scale, a catalogue of more than 1000 mesoscale flow patterns with 100 m resolution was created with the model GRAMM, based on a discrete set of atmospheric stabilities, wind speeds and directions, accounting for the influence of land-use and topography. Finally, the flow around buildings was solved with the CFD model GRAL forced at the boundaries by GRAMM. Subsequently, Lagrangian dispersion simulations for a set of air pollutants and emission sectors (traffic, industry, ...) based on extremely detailed building and emission data was performed in GRAL. The result of this nested procedure is a library of 3-dimensional air pollution maps representative of hourly situations in Zurich (Berchet et al. 2017). From these pre-computed situations, time-series and concentration maps can be obtained by selecting situations according to observed or modelled meteorological conditions.

The results were compared to measurements from air quality monitoring network stations. Modelled concentrations of NO_x and PM compared well to measurements across multiple locations, provided background conditions were considered carefully. The nested multi-scale modelling system COSMO-ART/GRAMM/GRAL can adequately reproduce local air quality and help understanding the relative contributions of local versus distant emissions, as well as fill the space between precise point measurements from monitoring sites. This information is useful for research, policy-making, and epidemiological studies particularly under the assumption that exceedingly high concentrations become more and more localised phenomenon in the future.

How to cite: Suter, I., Emmenegger, L., and Brunner, D.: NESTED HIGH-RESOLUTION NOx AND PM SIMULATIONS OVER ZÜRICH, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6202, https://doi.org/10.5194/egusphere-egu21-6202, 2021.

High spatial resolution (<100m) mapping of NO₂ at various temporal scales (e.g., hours of the week, month, or year) provides opportunities to study the relationship between personal air pollution exposure and health over large populations. Statistical modelling of NO₂ at the global scale provides high-resolution estimations for countries with deficient ground station measurements and provides air pollution maps and human exposures with consistent uncertainties for global health studies. Our objective is to develop spatiotemporally-resolved statistical learning models, understand the temporal dynamics of NO₂ and the contributing sources, and open-source our global NO₂ prediction maps at 100 m resolution. The global maps are provided at various temporal aggregations (e.g. separating between weekdays and weekends, day and night) and spatial aggregations (e.g. multiple gridded resolutions, administrative units) to facilitate global exposure assessment. To create these maps, we compiled from multiple sources a dataset of hourly NO₂ measurements from more than 7000 ground stations over the globe, considerably larger in size and spatiotemporal coverage than used in recent high-resolution NO₂ mapping studies. For statistical modelling, geospatial predictors include Sentinel-5 satellite (Tropomi instrument) measurements, variables relating to the emission sources (e.g., road network), dispersion processes (e.g., meteorological variables), elevation and Earth nightlights (from VIIRS nightlight data). We evaluate various statistical models including linear models, ensemble tree-based models, deep convolution models, stacked models with regularisation, and hierarchical modelling strategies and select the optimal model for mapping. Evaluation of models included uncertainty assessment as well as spatial validation methods.

How to cite: Lu, M., Schmitz, O., de Hoogh, K., Hystad, P., Knibbs, L., Kai, Q., and Karssenberg, D.: Global, high-resolution statistical modelling of NO2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6355, https://doi.org/10.5194/egusphere-egu21-6355, 2021.

Atmospheric chemistry is critical in determining air quality and thus impacts climate change. Anthropogenic species are released into the atmosphere, and undergo complex photochemical transformations leading to the production of secondary pollutants, among which ozone and particulate matter. This can induce adverse effects on human health, visibility, ecosystems and local meteorology.  The combination of state-of-the-art atmospheric models with accurate atmospheric measurements of atmospheric species abundances is needed to evaluate whether atmospheric models can successfully simulate the chemical and physical processes occurring, and hopefully monitor the emissions of anthropogenic compounds and help in the implementation and verification of abatement policies.

In this work, ground-based, airborne and spaceborne measuring techniques are used to evaluate the performance of the full chemistry on-line WRF-Chem model over Antwerp in Flanders, Belgium, one of the areas with the highest NO2 pollution in the world. The model is configured to allow two nested domains with spatial resolution changing from 5 to 1km, so as to pinpoint the most pollutant sources in the region, and applied to simulate the urban air quality over the Antwerp agglomeration.

We will briefly discuss the choices and adaptations made regarding the physical parameterizations, emission inventories and chemical mechanism. The model performance is evaluated through comparison with various observation types. The physics parameterizations in WRF model  are evaluated through comparison against ground-based data from two meteorological stations in the Antwerp region. The WRF-Chem NO2 distributions are evaluated against (1) hourly measured concentration values from monitoring stations in Flanders, (2) vertical columns measured by an airborne hyperspectral imager APEX, providing a 2-dimensional spatial mapping, on 27 and 29 June 2019, and (3) spaceborne NO2 columns over Belgium obtained from the high-resolution TROPOMI instrument aboard S5p. The consistency of the model biases across the three datasets will be discussed, and recommendations will be made for improving model performance in this region.

How to cite: Poraicu, C., Müller, J.-F., Stavrakou, T., Fonteyn, D., Tack, F., and Veldeman, N.: Developing high-resolution simulations of tropospheric NO2 over Flanders using WRF-Chem, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2063, https://doi.org/10.5194/egusphere-egu21-2063, 2021.

Cooking operations can be an important fine PM source for urban areas. Cooking emissions are a source of pollution that has been often ignored and are not included or are seriously underestimated in urban emission inventories. However, several field studies in cities all over Europe suggest that cooking organic aerosol (COA) can be an important component of the total organic PM. In this study we propose and evaluate a methodology for the simulation of the COA concentration and its variability in space and time in an urban area. The city of Patras, the third biggest in Greece is used for this first application for a typical late summer period. The spatial distribution of COA emissions is based on the exact location of restaurants and grills, while the emissions on the meat consumption in Greece. We estimated COA emissions of 150 kg d^-1 that corresponds to 0.6 g d^-1 per person. The temporal distribution of COA was based on the known cooking times and the results of the past field studies in the area. Half of the daily COA is emitted during dinner time (21:00-0:00 LT), while approximately 25% during lunch time (13:00-16:00 LT). The COA is simulated using the Volatility Basis Set with a volatility distribution measured in the laboratory and is treated as semivolatile and reactive. The maximum average COA concentration during the simulation period is predicted to be 1.3 μg m^-3 in a mainly pedestrian area with a high density of restaurants. Peak hourly COA concentrations in this area exceed 10 μg m^-3 during several nights. The local production of secondary COA is predicted to be slow and it represents just a few percent of the total COA.

How to cite: Siouti, E., Skyllakou, K., Kioutsioukis, I., Ciarelli, G., and Pandis, S. N.: Simulation of the Cooking Organic Aerosol Concentration Variability in an Urban Area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10090, https://doi.org/10.5194/egusphere-egu21-10090, 2021.

Given the steep trajectory of the global climate crisis, current emission allowances following the 2015 Paris Agreement require that national GHG budgets (sources/sinks) are quantified more accurately and more timely. Large cities also play a key role in achieving the national objectives of emission reduction as urbanization reaches unprecedented levels. Atmospheric inversion approaches have the potential to produce a semi-independent assessment of these fluxes by combining atmospheric data and high-resolution inventories.  However, these approaches only provide an estimate of the total city flux, with no information on the per sector distribution, a major shortcoming for policy makers.

Multiple emission datasets have been developed worldwide at various spatial scales in order to provide a better understanding of the global carbon cycle, but also more locally for large cities and emission hot-spots. Due to the different methodologies and the quality of the surrogate data, large discrepancies are observed between these datasets, especially at the sectoral level. To allow for sectoral attribution in GHG inversions, we investigate Air Quality (AQ) data as additional information assimilated jointly with GHG’s to attribute atmospheric information to specific sectors of activity.

We focus here on the Paris metropolitan area and analyze ground-based observations as well as high-resolution emission inventory estimates for both GHG’s and other reactive pollutants. The observations were acquired by the ICOS GHG monitoring network and the Airparif AQMN. Bottom-up emission estimates were provided by three different emission products for CO₂, CO, NO_X.

We analyzed the atmospheric signals using a backward-in-time Lagrangian Particle Dispersion Model (LPDM) driven by meteorological variables from mesoscale simulations (WRF-FDDA) at 1-km resolution to represent the origin of the emissions (so-called tower footprints).

The modelled concentrations were compared to observations from March to June for the year 2019 to assess the validity of the temporal variations, for each emissions dataset and for both weekly and diurnal cycles. Furthermore, we estimated a correction factor for the modelled NO_X, CO, and CO₂ concentrations using a Monte-Carlo approach that optimizes the three inter-species ratios (NO_X/CO, NO_X/CO₂, and CO/CO₂) to quantify the actual emissions for these three species. We further look into the first Covid-19 lockdown period of 2020 to evaluate the applicability of the method, a first step toward providing process-based information from atmospheric observations, and determine the sectoral contributions to observed emissions changes.

How to cite: Abdallah, C., Lauvaux, T., Gros, V., jinghui, L., Bréon, F.-M., Ramonet, M., Ciais, P., Denier van der Gon, H. A. C., Perussel, O., Baudic, A., and Laurent, O.: From fluxes to signals: A joint analysis of GHG and Air Quality over the Paris Megacity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11915, https://doi.org/10.5194/egusphere-egu21-11915, 2021.

Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution – especially with respect to spatial and temporally variability – measurement data with high spatial and temporal resolution are critical.

Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O₃) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].

After having conducted a measurement campaign in 2016 to create a high-resolution NO₂ concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O₃ and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.

[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018

[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018

[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, 2020

[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020

How to cite: Wenzel, A., Chen, J., Dietrich, F., Thekkekara, S. T., Zollitsch, D., Voggenreiter, B., Setili, L., Wenig, M., and Keutsch, F. N.: Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15182, https://doi.org/10.5194/egusphere-egu21-15182, 2021.

Climate regulations and satellite monitoring on increasingly high resolution creates a demand for an insight into emissions on an urban scale. The aim of the Ruisdael Observatory (www.ruisdaelobservatory.nl) is to provide just that: detailed and high-resolution modelling and measurements of weather and air quality in a domain covering the Netherlands.

The Ruisdael Observatory created a renewed impulse in the developments of the DALES Large-eddy simulation (LES) model (Heus et al., 2010, Ouwersloot et al. 2016) to find and push the limits of atmospheric modelling. Typical simulations with DALES will use a spatial resolution in the order of 100m in domain sizes spanning over 100x100 km. This high resolution justifies the complexity and the multitude of emission sources and resulting transport of pollutants in the atmospheric boundary layer.

The combination of high resolution and large domain sizes allows us to investigate how emissions disperse in a turbulent environment which is forced by large-scale flow at the same time. Parameterizations are no longer needed to calculate horizontal or vertical transport in the boundary-layer. This way, we can provide new insight into the transport of emissions in the boundary layer and the detrainment of gases out of the boundary layer into the free atmosphere.

We will discuss the construction of our emission database for the Netherlands with a 100-meter and 1-hourly resolution. For this, we started from the official E-PRTR reported emission inventories (www.emissieregistratie.nl) and enriched with high resolution activity data from mostly open-source datasets. Moreover, large emissions sources (accounting for e.g. >80% of CO2 emissions) are subject to mandatory registration and their locations are known exactly. Emissions from different source categories can be tracked individually and compared to measurements from the Ruisdael Observatory measurement sites. Examples of simulations of fair-weather summer days will be compared to surface measurements and showcase the data richness of our new model and combination to measurements from our network.

How to cite: de Bruine, M., Jansson, F., van Stratum, B., Rijsdijk, P., and Houweling, S.: Simulating the emission and transport of gases on 100-meter resolution in a 100-kilometer domain., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13055, https://doi.org/10.5194/egusphere-egu21-13055, 2021.

A street network box model has been designed to assess how connected road links interact and influence nitrogen dioxide (NO₂) concentrations at the neighbourhood scale. The overall aim of developing this model is to investigate both theoretical and applied questions, including how social policy and neighbourhood-level action can improve air quality in cities.

The computationally lightweight model presented here relies on component boxes connected at intersections and along road links to mix pollutant concentrations throughout the network. It is therefore similar in architecture and theory to the operational French network model, SIRANE. Mass is conserved so that the sum of vehicle emissions and pollutants advected into each box are equal to the sum of turbulent exchanges at the top of the box with overlying urban canopy background air and pollutants advected out of each box. Fast NO_x-O₃ reactions are fully integrated and simulated at every time step (1 second),  which will enable high temporal resolution traffic emissions to be integrated in the future. The model, implemented in R, enables mixing between two boxes (simple road link), three boxes (‘t-junction’), and four boxes (cross roads), with the capability to automatically determine the direction of mixing based on the overlying wind direction. The network in turn consists of multiple boxes and connections, all of which can be oriented in any direction with respect to the wind direction.

Low traffic neighbourhoods (LTNs) have become a popular tool for UK urban planners to attempt to reduce population exposure to NO₂ and particulate matter, and encourage active travel. Inspired by Dutch city designs and Barcelona’s ‘superblocks’, LTNs typically take the form of blocking vehicle access to minor residential roads, while keeping them open for residents and active transport users. The model described above has been applied to an area of approximately 1 km² in the Kings Heath area of Birmingham (UK) where an LTN has been proposed. The network consists of 29 boxes and each box represents a road link, including major road links surrounding the residential area. Background concentrations for NO, NO₂ and O₃ were obtained from a nearby AURN site maintained by Defra. Meteorological conditions were measured at Birmingham City airport and a mean wind speed for each 10-degree wind direction sector was determined. One model was run for each wind direction and corresponding speed, enabling the relative contribution of local emissions and transported pollutants for each box to be assessed. Subsequently, the frequency of occurrence was used to weight each model’s outputs to produce a simulated annual average concentration map.

Initial results are promising; when compared to the output from traditional air quality modelling software, the spatial distribution of NO₂ concentrations are in agreement. The impact of pollutant redistribution throughout the network under prevailing wind conditions and the potential impact of a LTN on NO₂ concentrations inside and outside the designated area will also be presented.

How to cite: Pearce, H., Bloss, W., Cai, X., and Gong, Z.: A street network box model to assess emission reduction policies at the neighbourhood scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2492, https://doi.org/10.5194/egusphere-egu21-2492, 2021.

Currently a strong discussion is ongoing in Germany and Europe whether to ban vehicles from downtown areas in order to lower particle concentrations of e.g. PM₁₀ and NO₂. As often only few measurements exist inside city centers, little to nothing is known about the horizontal and vertical distributions of air pollutants. Within the EU demonstration project Open Forecast (https://open-forecast.eu/), we applied the WRF-Chem model system version 4.0.3 in order to close this knowledge gap. We zoom in the urban area of Stuttgart, a hot spot of air pollution in Germany. The outermost domain with convection-permitting resolution of 1.25 km encompasses parts of Central Europe in order to provide boundary conditions for the inner two domains.

The model system was improved in many ways, e.g., with respect to the representation of land cover, urban canopy, and soil properties, which turned out to be key for an acceptable performance. Furthermore, we developed a sophisticated infrastructure to ingest the required high-resolution emission data, which turned out to be very challenging.

We show that this model approach is likely the best means to understand and to predict air pollution, as the distribution of their constituents depends strongly and simultaneously on the vertical mixing by turbulence, the mesoscale circulation in the complex urban environment, and orographic environment.

The model system was operated and investigated for a case study of January 21, 2019 during which an alert with respect to the exceedance of PM₁₀ was issued. We present the simulations of meteorological variables as well as PM₁₀ and NO₂ and show the complexity of their distribution in the nighttime stable and daytime shallow boundary layer in dependence of the temporal variability of the traffic in the Stuttgart metropolitan area.

To the best of our knowledge, the results reveal for the first time the complex dynamics of air pollution in complex urban space of Stuttgart at a very high spatial and temporal resolution that cannot currently be achieved with measurements.

How to cite: Schwitalla, T., Warrach-Sagi, K., Bauer, H.-S., and Wulfmeyer, V.: Turbulence permitting air pollution simulation for the Stuttgart metropolitan area - A winter case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7044, https://doi.org/10.5194/egusphere-egu21-7044, 2021.

High air pollution levels pose a threat to both human health and ecosystem vitality in Hebei Province, NE China. Although air quality changes are monitored hourly with high-end equipment at the provincial scale (197 stations for 187,693 km²) it is difficult for individual counties or cities to improve local air quality based on regional-scale information. The Sino-Dutch Technology Transfer & Training Project established a monitoring network of 43 low-cost air-boxes and 11 standard meteorological stations in Shexian county, Handan city (~ 1500 km²) to measure atmospheric concentrations of PM₁₀, PM_2.5, CO, SO₂, NO₂ and O₃ at 1-min intervals from January 2020 onwards. Data from these stations were evaluated in real time using the TNO Gaussian plume model. The model provides point emission levels of PM₁₀, PM_2.5 and CO at 10-min intervals after calibration against measured concentrations. Based on a 2019 pollution source inventory, 21 major source areas were identified and used to derive an optimized source map for model input – including a large steel company, a coal-fueled power plant, different industrial complexes (cement, coking plant for ore smelting), as well as the densely populated city centre, rural residential areas, and a busy highway. The model performs source optimization using concentration data for all 43 stations and subsequently calculates the contributions of individual sources for each monitoring station to see to what extent the source map explained observed concentrations. Full network operation started in July 2020. Based on a one-month test period (August 2020), the steel company and coking plant were estimated to contribute ~25% of the total area’s PM-emissions. The central city area contributed ~10% and 17% of total PM- and CO-emissions, respectively, mostly due to construction activity and traffic. Repeating the exercise for the two provincial monitoring stations that also had high-end equipment in place in the downtown area gave inferred average urban contributions to measured concentrations as high as 60–62.5% for PM₁₀ and PM_2.5 versus 48% for CO. The steel factory contributed an estimated 9–11% for PM₁₀ and PM_2.5 at these locations and a cement factory 13% for CO. The combined results underline the importance of taking spatial variability of emission sources into explicit account in complex industrialized cities. Moreover, the combination of a low-cost airbox real-time monitoring network with emission apportionment modeling will allow local policy-makers to take proper actions towards reducing air pollution levels at the local scale.

How to cite: Zhang, J., Hensen, A., Seignette, P., and Yu, D.: Low-cost airbox network and Gaussian plume modelling to assist air quality policy-making at the local scale: Shexian County, Hebei Province, China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7296, https://doi.org/10.5194/egusphere-egu21-7296, 2021.

How to cite: Benavides, J., Guevara, M., Snyder, M. G., Rodríguez-Rey, D., Soret, A., Pérez García-Pando, C., and Jorba, O.: Impact of excess diesel NOx emisions upon NO2 pollution in a compact city: the role of model resolution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8970, https://doi.org/10.5194/egusphere-egu21-8970, 2021.

Despite efforts to reduce pollutant emissions in the UK, between 28,000 and 36,000 deaths a year are attributable to poor air quality and ambient air pollution is considered the UK’s biggest environmental threat to health. Characterising, quantifying and understanding air quality variability and the importance of different drivers is essential to guide policies to address the issue and its risks, for both the short and long term. Here we investigate a statistical modelling approach to characterise air quality variability and its key drivers, using Kalman filters. Kalman filters are a commonly used tool in air quality modelling but are seldom used in a statistic framework that accounts for uncertainty in a principled way. Kalman filtering allows us to take data which is noisy or partially recorded, such as air quality data, and help reveal the true underlying trends and dynamics of the data. This allows us to combine measurement information with the statistical model to obtain an air quality forecast, using the measurement information to reduce the statistical model errors and improve model results. We explore this approach using air quality monitoring data from the UK Automatic Urban and Rural Network (AURN), which consists of 150 sites focussed mainly in populated areas, leaving large areas unmonitored. AURN is primarily used for compliance reporting against national and European air quality standards and targets. Eventually, our aim is to provide short-term forecasts of pollutant levels from AURN, comparing this against process model forecasts and ultimately providing an optimised combination of process model, statistical model and measurement.   

How to cite: Duncan, R., Young, P., and Nemeth, C.: Forecasting UK site level air quality with a Kalman filtering approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11249, https://doi.org/10.5194/egusphere-egu21-11249, 2021.

Only a few studies have reported sources, characteristics, and strategies for controlling severe air pollution events frequently occurring in several urban areas in India. For a detailed analysis of particulate matter (PM) and gaseous species for their temporal and spatial distribution, a high-resolution simulation through Weather Research and Forecasting with Chemistry (WRF-Chem) model was undertaken for the entire India. Emission Database for Global Atmospheric Research (EDGAR v2.2) was used. WRF-Chem model was used for predicting concentrations of NO₂, O₃, CO, SO₂, and PM_2.5 along with its components in major cities (Delhi, Lucknow, Patna, Kolkata, Ahmedabad, Mumbai, Hyderabad, Bangalore, Chennai) spread all over India. The model's performance was validated against observations that were available for a few large cities from national ambient air quality monitoring stations. Generally, O₃ predictions did not show an acceptable association with the measurements, but PM_2.5 predictions did meet the model performance criteria (root mean square error (RMSE), normalized mean bias (NMB), normalized mean error (NME), mean fractional bias (MFB) and mean fractional error (MFE)). Model performance was better for days with higher levels of PM_2.5. PM_2.5 showed the highest concentration levels for India's Northern and Eastern parts and a major portion of the Indo-Gangetic Plain (IGP). Concentrations of PM_2.5 were observed to be lower during monsoon and higher during the winter seasons. Nitrate levels were found to be 150–240% higher in winter than the yearly average. However, a decrease in solar radiation intensity and temperature during the winter season showed sulfate levels to be much lower than in other seasons. Except for South India, Primary Organic Aerosol (POA) contribution to PM_2.5 was highest for regional analysis. Analysis of model concentrations indicates the importance of controlling precursor gases for secondary pollutants in India. Conclusively, WRF-Chem predicted particulate and gaseous air pollutant levels can be used to develop control strategies for large regions that are part of the same airshed.

How to cite: Azmi, S., Nagar, P. K., and Sharma, M.: Regional emission loading of particulate and gaseous air pollutants over India using fine resolution WRF-Chem simulation technique, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1135, https://doi.org/10.5194/egusphere-egu21-1135, 2021.

Due to fast industrialization and urbanization, air pollution is more and more serious in Taiwan. Generally, many anthropogenic factors can affect air quality; for example,  exhaust gas from automobiles and motorcycles, factory emissions, fossil fuels, burning straw, incinerators, etc. The factors are highly associated with land use. Previous studies typically used multiple linear regression model to analyze the relationships between air quality and land use. This study adopts multi-threshold land use logistic regression (LULR) models with several continuous and categorical variables to assess different levels of fine particulate matters (PM_2.5) in Taiwan and to determine key land-use factors controlling various levels of air PM_2.5 pollution. First, data on annual air PM_2.5 pollution in the Taiwan Island are collected in 2017. Four thresholds of 16.37, 18.68, 21.83, 25.83 µg/m³ are determined based on the 20th, 40th, 60th, and 80th percentiles, respectively, of observed data. Geographical information system is then adopted to analyze data on 29 environmental variables obtained from the three main dimensions–information of land-use categories, amounts of specified pollution sources in townships, and geographical locations adjacent to monitoring stations of air quality. Finally, data in 2017 are employed to establish the LULR model and significant land-use factors causing air PM_2.5 pollution are determined using stepwise LULR models for various levels of air PM_2.5 pollution. Moreover, data in 2018 are used to verify the established LULR models. The analyzed results reveal that correct responses of the LULR models range from 83.6% to 100%. For the 20th-percentile threshold, locations and the industry land-use area are positively contributed to air pollution, while tempt densities and building, agriculture, forest land-use areas are negatively contributed to air pollution. For the 40th-percentile threshold, locations, plains with an elevation of less than 150 m, and agriculture land-use areas are related to air pollution. For the 60th-percentile threshold, locations are positively related to air pollution, while forest land-use areas are negatively related to air pollution. For the 80th-percentile threshold, locations and industry park areas associated with air pollution. According to the research results, a feasible strategy of environmental management and outdoor activities is proposed.

How to cite: Jang, C.-S.: Applying land use logistic regression models to assess different levels of air quality and to determine key environmental factors in Taiwan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1182, https://doi.org/10.5194/egusphere-egu21-1182, 2021.

The online-coupled Weather Research and Forecasting model with Chemistry (WRF-Chem v4.0), was applied to evaluate the impact of using different anthropogenic emissions inventories on regional air quality in Argentina. For this purpose, we couple the Argentinian high-resolution emissions inventory (GEAA-AHRI) and the Emissions Database for Global Atmospheric Research – Hemispheric Transport of Air Pollution (EDGAR-HTAP) and introduce them into the model, with a local optimized configuration considering 3 nested domains with a horizontal grid size of 20 x 20 km, 4 x 4 km, and 1.3 x 1.3 km and the MOZART chemical scheme. The model output for NO2, PM10, PM2.5, and O3 concentrations over the innermost domain was compared against the existing surface and satellite-derived observations for the Buenos Aires Metropolitan Area (AMBA) during austral fall 2018. We found an overall good model performance for all simulations, and large discrepancies between the emission inventories, obtaining an improved urban-scale spatio-temporal representation when the high resolution GEAA-AHRI dataset is considered. Our results show that the daytime concentrations of air pollutants are strongly influenced by the shape and shift of the hourly emissions profile before sunrise and after sunset, especially for NO2 where the inclusion of the temporal profile decreased the mean bias by ~80%. Performance criteria for modeled PM10 and PM2.5 were in general satisfied, despite having an average underestimation of observations. When compared to NO2 tropospheric columns derived from TROPOMI, The general magnitude and spatial pattern of the NO2 tropospheric column is in agreement with the mean TROPOMI columns during the modeled period, obtaining correlation coefficients higher than 0.6 for all simulations. Our results highlight the benefits of using a time-dependent and high-resolution local inventory for addressing the background air quality in AMBA. The implementation and validation of local emissions and static fields with high spatial and temporal resolution carried out in this work, establishes a benchmark for forthcoming studies in other regions of South America where different modeling tools for air quality analysis are currently being used to complement the usually sparse and discontinuous air quality networks.

How to cite: Lopez-Noreña, A. I., Berná, L., Tames, M. F., Millán, E., Puliafito, E., and Fernandez, R. P.: Evaluation of NO2, O3, PM10, and PM2.5 in the city of Buenos Aires, Argentina using WRF-Chem model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1514, https://doi.org/10.5194/egusphere-egu21-1514, 2021.

Estimating personal exposure to air pollution is important in investigating the impact of air pollution on chronic diseases such as diabetes or cardiovascular disease. Long-term personal exposures estimates from large cohorts are required to reliably identify the relation between chronic air pollution exposure and non-communicable disease outcomes. Using e.g. yearly averaged concentrations at fixed locations such as the home address may result in incomplete quantification of personal exposure as persons move in space and time. An appropriate estimation involves mapping of space-time variation of concentrations as well as incorporating several activities of individuals at different locations and the mobility of individuals along their space-time paths. While for small surveys detailed information is often available (e.g. home and work address, GPS tracking data and travel mode), this abundance of data is not available for large-scale personal exposure assessment. Thus, for large-scale exposure assessment the first challenge is the design of model representations of individual mobility for which parameters can be identified with relatively limited observational data on individual mobility. The second challenge is the execution of such large-scale models over large populations.

We address these challenges by developing a modelling framework on top of Campo (https://campo.computationalgeography.org) that combines the space-time mapping of pollution and activity-based mobility simulation of individuals. To represent data sparse information on individuals, we use personal activity schedules. Air pollution is based on land use regression models. Our modelling approach contains the following key components: a) an activity schedule generator allowing to express the type, location and duration of an individual's activity as a function of a person's profile defined by e.g. age, gender or occupation, and b) a spatial context generator providing the location of an individual during a particular activity. Activities cover residence in certain areas (home, work, leisure) or along routes using different travel modes (car, bicycle, on foot), and c) an exposure estimator. Exposure estimation is subsequently the combination of the spatial contexts for each activity with air pollution concentrations at corresponding times.

Using these decoupled but interacting components provides the flexibility to express a broad range of representative time spans and spatial residences, required e.g. to represent uncertainty of unknown work locations or travelled routes. We present concepts and the model using a nationwide cohort from Switzerland.

How to cite: Schmitz, O., Lu, M., de Hoogh, K., Probst-Hensch, N., Jeong, A., Flückiger, B., Vienneau, D., Hoek, G., Kyriakou, K., Vermeulen, R. C. H., and Karssenberg, D.: Nationwide estimation of personal exposure to air pollution using activity-based field-agent modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7081, https://doi.org/10.5194/egusphere-egu21-7081, 2021.

Air pollution has a serious impact on health and this problem will be aggravated under the action of climate change. This climate penalty can play an important role when trying to assess future impacts of air pollution on several pathologies. Among these diseases, the scientific literature is scarce when referring to the influence of atmospheric pollutants on neurodegenerative diseases for future climate change scenarios. Under this framework, this contribution evaluates the incidence of dementia (Alzheimer's disease and vascular dementia) occurring in Europe due to exposure of air pollution (essentially NO₂ and PM2.5) for the present climatic period (1991-2010) and for a future climate change scenario (RCP8.5, 2031-2050). The GEMM methodology has been applied to climatic air pollution simulations using the chemistry/climate regional model WRF-Chem. Present population data were obtained from NASA's Center for Socioeconomic Data and Applications (SEDAC); while future population projections for the year 2050 were derived from the United Nations (UN) Department of Economic and Social Affairs-Population Dynamics.

Overall, the estimated incidence of Alzheimer's disease and vascular dementia associated to air pollution over Europe is 498,000 [95% confidence interval (95% CI) 348,600-647,400] and 314,000 (95% CI 257,500-401,900) new cases per year, respectively. An important increase in the future incidence is projected (around 72% for both types of dementia) when considering the effect of climate change together with the foreseen changes in the dynamics of population (expected aging of European population). The climate penalty has a limited effect on the total changes of Alzheimer's disease and vascular dementia (approx. 0.5%), since the large increase in new annual cases over southern Europe is offset by the decrease of the incidence associated to these pathologies over more northern countries, favored by an improvement of air pollution caused by the projected enhancement of rainfall.

How to cite: Jiménez-Guerrero, P., Guzmán, P., Tarín-Carrasco, P., and Morales-Suarez-Varela, M.: Effects of air pollution on neurodegenerative diseases (Alzheimer's disease and vascular dementia) over Europe for present and future climate change scenarios, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14494, https://doi.org/10.5194/egusphere-egu21-14494, 2021.

Warm summer temperatures provide ideal conditions for the occurrence of extreme ground level ozone pollution episodes. Given the well-established negative impacts of ozone on human and plant health, understanding and attributing these extreme events is of importance to the scientific and wider community, particularly as heatwaves may become more frequent due to climate change. Extreme Value Analysis provides a powerful and flexible framework in which to statistically model unusually large observed values of ozone extracted from historical data. Here, a temperature dependent Peaks-Over-Threshold method based upon the Generalised Pareto Distribution is used to carry out a regional comparison of extreme ozone pollution episodes within the UK. Our analysis uses surface ozone observations from the UK’s extensive Automatic Urban and Rural Network. The statistical model was used to quantify the frequency and magnitude of extreme ozone events, including a probabilistic assessment of exceeding UK public health thresholds, conditional on temperature. Return levels are provided for each monitoring site demonstrating the expected future projections of extreme ozone pollution events across the UK. We find that across UK rural background sites, return periods for a daily maximum 8-hr ozone level of 100 ug/m3 (a 'moderate' level of air pollution in the UK's Air Quality Index) range from 32-147 days, based on analysis of the data in the decade 2010-2019. Similarly, for urban background sites the range is 36-869 days. An analysis of the spatio temporal variability in UK ozone extremes, along with their temperature dependence, will be presented.

How to cite: Gouldsbrough, L., Hossaini, R., Eastoe, E., and Young, P. J.: Investigating the occurrence, likelihood and regional variability of extreme ozone pollution episodes across the UK, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7653, https://doi.org/10.5194/egusphere-egu21-7653, 2021.

The fifth version of the Emission Database for Global Atmospheric Research (EDGAR 5.0) provides an impressive inventory of various pollutants. Pollutants from different emission sectors are available with daily, monthly and yearly temporal profiles at a high global resolution of 0.1°×0.1°. Although this resolution has been sufficient for regional air quality studies, the emissions appeared to be too coarse for local air quality studies in areas with complex topography. With Switzerland as a case study, we present our approach for downscaling EDGAR emission data to a much finer resolution of 0.02°×0.02° with the aim of modelling local air quality.

We downscaled the EDGAR emissions using a combination of GIS tools including QGIS, ArcGIS, and a series of python scripts. We obtained the surface coverage of different land use features within the defined EDGAR emission sectors from Open Street Map (OSM) using the QuickOSM tool in QGIS. With the calculated local surface area coverage of the emissions sectors, we downscaled the EDGAR inventory data within ArcGIS using a set of developed Arcpy script tools.

The outcome was a much finer resolved emission dataset which we fed into the WRF-CHEM air quality model within a pilot project. A comparison of the modelled pollutant concentrations using the two datasets (original EDGAR data and the downscaled data) shows an improved agreement between the downscaled dataset and the measurement data.

Studies investigating the impact of urbanization, land use change or traffic pattern on air quality may benefit from our downscaling solution, which, thanks to the global coverage of OSM, can be globally applied.

How to cite: Edebeli, J., Spirig, C., and Anet, J.: Downscaling EDGAR emissions to local emission sectors for Switzerland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8208, https://doi.org/10.5194/egusphere-egu21-8208, 2021.

The upper Hunter Valley region in New South Wales (NSW), Australia has several open-cast coal mines, which supply coal to two large thermal power plants (TPPs) in the area, beside the export market. Long-term Particulate Matter (PM) pollutants and meteorological measurements are recorded by a network of 13 NSW government-owned continuous monitoring stations in the upper Hunter Valley region. The Ramagundam area in the state of Telangana, India has similar pollution source characteristics (coal mines and TPPs), but PM pollutant measurements are largely carried out with manual monitoring stations at 24-hour intervals, not more than twice a week. As the coal and overburden excavation from open-cast coal mines and stack emissions from TPPs lead to local PM pollution, we have used MODIS-MAIAC Aerosol Optical Depth (AOD) at 550 nm and Normalized Difference Vegetation Index (NDVI) along with the local meteorological data such as ambient temperature, relative humidity, wind speed and direction to model PM10 and PM2.5 at the upper Hunter Valley and Ramagundam regions. Our model can explain about 60% of variation in PM10 (p-value < 0.0001), while a similar model is able to explain about 75% of the variation in the PM2.5 (p-value < 0.0001). We will extend our model results from Hunter Valley to Ramagundam area and comment on the potential of using geospatial products such as AOD as a proxy to ground-based pollution measurements in developing countries such as India, where pollution data is scarce.

How to cite: Kamath, H., Chauhan, C., Mishra, S., Ahmed, A., and Srikanth, R.: Air quality at open-cast coal mining regions at Hunter Valley, Australia and Ramagundam, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9933, https://doi.org/10.5194/egusphere-egu21-9933, 2021.

Quantification of the spatial and temporal variations in the sources of air pollutants, especially PM_2.5, can inform control strategies and, potentially, the understanding of PM_2.5 health effects. Three-dimensional chemical transport models (CTMs) are well suited to help address this problem, since they simulate all the major processes that impact PM_2.5 concentrations and transport. In this study we quantify the changes in the concentration, exposure, composition, and sources of PM_2.5 in the US from the early 1990s to the early 2010s. Significant reductions of emissions of SO₂, NO_x, VOCs and primary PM have taken place in the US during the last 20 years. We evaluate our understanding of the links between these emissions and concentration changes combining a chemical transport model (PMCAMx) with the Particle Source Apportionment Algorithm (PSAT) (Skyllakou et al., 2017). Results for 1990, 2001 and 2010 are presented. The reductions in SO₂ emissions (64% mainly from electrical generation units) during these 20 years have dominated the reductions in PM_2.5 leading to a 45% reduction of the sulfate levels. The predicted sulfate reductions were in excellent agreement with the available measurements. Also, the reductions in elemental carbon (EC) emissions (mainly from transportation) have led to a 30% reduction of EC concentrations. The most important source of OA through the years according to PMCAMx is biomass burning followed by biogenic SOA. OA from on-road transport has been reduced by more than a factor of 3, on the other hand changes in biomass burning OA and biogenic SOA have been modest. In 1990 90% of the US population was exposed to PM_2.5 concentrations to equal and higher than the suggested annual mean by the WHO (10 μg m^-3), but this reduced to 70% in 2010. Also, the predicted changes in concentrations were evaluated against the observed changes for 1990, 2001 and 2010, in order to understand if the model represents well the changes through the years.

Skyllakou, K., Fountoukis, C., Charalampidis, P., and Pandis, S.N. (2017). Volatility-resolved source apportionment of primary and secondary organic aerosol over Europe, Atmos. Environ., 167, 1–10.

How to cite: Skyllakou, K., Rivera, P. G., Dinkelacker, B., Karnezi, E., Kioutsioukis, I., Hernandez, C., Adams, P., and Pandis, S.: Changes of PM2.5 concentrations and their sources in the US from 1990 to 2010, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10567, https://doi.org/10.5194/egusphere-egu21-10567, 2021.

Tropospheric O₃ pollution notably contributes to the deterioration of air quality in many metropolitan regions, resulting in detrimental effects on human health and ecosystem. Due to the moderate atmospheric lifetimes of O₃, horizontal transport, exchange between atmospheric boundary layer (ABL) and free troposphere (FT), and chemical process within the ABL all potentially play important roles in regional O₃ pollution. In this study, we developed a post-calculation tool to quantify the hourly contributions of these processes to the regional budget of O₃ mass and concentration variations within the ABL based on the modelling results of the Community Multiscale Air Quality (CMAQ) model. The new features of this tool include: (1) the contributions of ABL-FT exchange on O₃ pollution can be quantified; (2) horizontally, the targeted region can be freely defined by users and vertically, the volumes are non-fixed owing to the diurnal variations of ABL; and (3) the budgets of O₃ mass and concentration variations are separately calculated and analysed. The Pearl River Delta (PRD) region, located in the South China and faced with severe O₃ pollution, was selected as the target region in this study. Results show that the variations of total O₃ mass within the ABL of the PRD were controlled by ABL-FT exchange, that is, the increase (decrease) of O₃ mass in the morning (afternoon) was driven by O₃ inflow (outflow) through ABL-FT exchange. By contrast, it was the chemical process that drove the variations of regional-mean O₃ concentrations. Except that ABL-FT exchange contributed to the rise of O₃ concentrations in several hours after sunrise, O₃ transport did not lead to the notable variation of O₃ concentration in the remaining hours of the day. Combining source apportionment methods, we found that outside O₃ (including O₃ produced by emissions within the East and Central China and background O₃) entered the PRD mainly through ABL-FT exchange. For chemical process, local sources played a major part, but the contributions of outside emissions cannot be neglected, suggesting the contributions of precursor transport. The effects of typhoon periphery, the weather system most related to O₃ pollution in the PRD, were also examined by comparing the budget results on O₃ pollution days with and without the occurrence of typhoons. The usage of this tool will help to comprehensively understand the influence of transport and chemical process in O₃ pollution on the regional scale, which is crucial for effective and strategic O₃ control.

Acknowledgement. This work is sponsored by the National Key Research and Development Program of China (Grant No. 2018YFC0213204, 2018YFC0213506) and the National Science and Technology Pillar Program of China (Grant No. 2014BAC21B01).

How to cite: Qu, K., Wang, X., Cai, X., Yan, Y., Jin, X., Shen, J., Xiao, T., Zeng, L., and Zhang, Y.: The regional budget of O3 mass and concentration variations within the atmospheric boundary layer using the CMAQ model: An example from the Pearl River Delta, China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11180, https://doi.org/10.5194/egusphere-egu21-11180, 2021.

Methane (CH₄) is a powerful greenhouse gas which plays a major role in climate change. The accurate monitoring of emissions from industrial facilities is needed to ensure efficient emission mitigation strategies. Local-scale atmospheric inversions are increasingly being used to provide estimates of the rates and/or locations of CH₄ sources from industrial sites. They rely on local-scale atmospheric dispersion models, CH₄ measurements and inversion approaches. Gaussian plume models have often been used for local-scale atmospheric dispersion modelling and inversions of emissions, because of their simplicity and good performance when used in a flat terrain and relatively constant mean wind conditions. However, even in such conditions, failure to account for wind and mole fraction variability can limit the ability to exploit the full potential of these measurements at high frequency.

We study whether the accuracy of inversions can be increased by the use of more complex dispersion models. Our assessments are based on the analysis of 25 to 75-min CH₄ controlled releases during a one-week campaign in October 2019 at the TOTAL’s TADI operative platform in Lacq, France (in a flat area). During this campaign, for each controlled release, we conducted near-surface in situ measurements of CH₄ mole fraction from both a mobile vehicle and a circle of fixed points around the emission area. Our inversions based on a Gaussian model and either the mobile or fixed-point measurements both provided estimates of the release rates with 20-30% precision.  

Here we focus on comparisons between modeling and inversion results when using this Gaussian plume model, a Lagrangian model “GRAL” and a Gaussian puff model. The parameters for the three models are based on high-frequency meteorological values from a single stationary 3D sonic anemometer. GRAL should have relatively good skills under low-wind speed conditions. The Gaussian puff is a light implementation of time-dependent modeling and can be driven by high-frequency meteorological data. The performance of these dispersion models is evaluated with various metrics from the observation field that are relevant for the inversion. These analyses lead to the exploration of new types of definitions of the observational constraint for the inversions with the Gaussian puff model, when using the timeseries from fixed measurement points. The definitions explore a range of metrics in the time domain as well as in the frequency domain.

Eventually, the Lagrangian model does not outperform the Gaussian plume model in these experiments, its application being notably limited by the short scales of the transport characteristics. On the other hand, the Gaussian puff model provides promising results for the inversion, in particular, in terms of comparison between the simulated and observed timeseries for fixed stations. Its performance when driven by a spatially uniform wind field is an incentive to explore the use of meteorological data from several sonic stations to parameterize its configuration. The fixed-point measurements are shown to allow for more robust inversions of the source location than the mobile measurements, with an average source localization error of the order of 10 m.

How to cite: Fontanier, B., Kumar, P., Broquet, G., Caldow, C., Laurent, O., Yver-Kwok, C., Cropley, F., Shah, A., Lozano, M., Defratyka, S., Gichuki, S., Lauvaux, T., Berthe, G., Martin, F., Noirez, S., Duclaux, O., Juery, C., Bouchet, C., and Ciais, P. and the TRACE team: Evaluation of the performance of different short-range atmospheric dispersion models for the monitoring of CH4 emissions from industrial facilities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13010, https://doi.org/10.5194/egusphere-egu21-13010, 2021.

Due to its complex topography (many islands) and extended coastline, Greece has numerous ports (about 200) which serve both commercial and touristic purposes. Almost 85 of them were in use in 2017. According to the FEI-GREGAA emissions inventory, navigation accounted for the 12% to the annual NOx emissions and by 3% to the PM₁₀ emissions in 2017. Consequently, it is an important source of emissions especially for areas which are close to major ports, such as the Athens basin; because it affects the local air quality (almost 32% of total NOx emissions in Athens for the year 2017 came from shipping).

In this study a comprehensive emissions inventory for the navigation sector was developed covering the period 2006 – 2017 and used as input to a photochemical model study.  The shipping emissions were calculated for each Greek port and ship type based on the ship arrivals. The relevant data for each ship type were provided by Eurostat in seasonal basis. The methodology followed was the Tier 2 approach suggested by the EMEP/EAA emissions inventory guidebook. Harbour (hotelling and manoeuvring) and cruise emissions of both the main and auxiliary engine were calculated for the main pollutants (such as NOx, NMVOCs, CO, etc), particulates (PM₁₀, PM_2.5), heavy metals (e.g. Pb, As, Cr, Zn), PCB and HCB.

In Greece the movement of passenger ships is very frequent. Consequently the spatial disaggregation of emissions was carried out with two different methodologies. Emissions from passenger ships were distributed on the ferry lines, as these have been recorded by OpenStreetMap, in which the necessary completion was made in order to cover the itineraries of the ships in all the Greek islands. The emissions from the other ship categories were distributed in the coastal zones around the respective ports, considering the probability of being in the specific zones significant. Finally, a part of the total emissions (10%) was placed in the ports.

Results revealed that in 2017 NOx emissions (27.5 ktonnes) prevailed among other pollutants contributing by 69% to the total maritime emissions, while SOx emissions followed (16%). This is due to the use of diesel fuel. Concerning the annual variation of pollutants for the period 2006 – 2017, it was found that in 2011 there was a significant reduction of emissions compared to 2010 (9,921 ktonnes for NOx and 3,913 ktonnes for sulfur oxides - SOx) while the decrease was lower for the rest pollutants. From 2012 onwards, the results showed a stabilization trend. The majority of pollutant emissions are attributed to the port of Piraeus (3704.7 ktonnes NOx emissions from passenger ships), which is the busiest passenger and commercial port in Greece (20228 passenger ships and 3168 container ships arrived in 2017).

How to cite: Fameli, K.-M., Papadopoulos, E., and Assimakopoulos, V.: Navigation in Greece: Developing a methodology for the spatial allocation of emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16008, https://doi.org/10.5194/egusphere-egu21-16008, 2021.

During the Air Quality and Climate Change in the Arabian Basin (AQABA) campaign a MAX-DOAS instrument was set up on board of the Kommandor Iona. The ship route covered a variety of regions with different atmospheric compositions: Clean air in the Mediterranean and the Arabian Sea, anthropogenic air pollution near the oil fields in the Arabian Gulf or in areas of dense ship traffic like the Suez Channel or the dust clouds of the nearby deserts in the Red sea. The measured spectra in the UV/VIS spectral range (302 to 467nm) provide sufficient information for the retrieval of aerosol and trace gas profiles. In this study, we focus on evidences of direct nitrous acid emission sources in harbor areas around Jeddah and Kuwait. Since HONO daytime chemistry is debated in recent literature and missing sources are being discussed, we compared the results of the MAX DOAS measurements to WRF-Chem model output in order to identify potential daytime sources in maritime/harbor regions.

How to cite: Dörner, S., Donner, S., Behrens, L., Beirle, S., Osipov, S., and Wagner, T.: Enhanced levels of nitrous acid during daytime derived from MAX-DOAS measurements during the AQABA campaign in late summer 2017, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2571, https://doi.org/10.5194/egusphere-egu21-2571, 2021.

• Scenario-1 - emission reduction for all administrative units in Poland. 
• Scenario-2 - emission reduction for administrative units were the annual average PM2.5 concentrations in 2019 exceeded the threshold of 20 µg/m3

How to cite: Durka, P., Kaminski, J. W., Jeleniewicz, G., Struzewska, J., Kawka, M., Jagiello, P., Gienibor, A., Norowski, A., Szymankiewicz, K., and Gawuc, L.: Effectiveness of residential heating emission reduction scenarios in Poland , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16085, https://doi.org/10.5194/egusphere-egu21-16085, 2021.