Displays

Significant emission reductions have been observed in China recently, especially after the the ‘Air Pollution Prevention and Control Action Plan’ in 2013. Major air pollutants, such as NOx, CO, SO₂, are found to reach their peak in 2012 or 2013. Few studies attempted to investigate how the recent emission reductions in China will affect global air quality and climate change. Here, by using global climate-chemistry models and health impact functions, we investigate how the contrasting emission changes in China from 2010 to 2017 will affect global air quality, mortality burden and climate change. We calculate that compared with the year 2010, 4800 deaths were avoided due to ozone reductions in 2017 globally, while 65% of the avoided deaths happen in China, and the other 35% worldwide. In 2017, 109,000 deaths were avoided due to PM_2.5 reductions, while 92% of the avoided deaths happen in China, and the other 8% worldwide. We also find that the cooling effect from the emission reductions of SO₂ in China has been compensated by the warming effect from the emission reductions of black carbon at the same time in China, which is the opposite trend as found in the developed countries in previous decades.

How to cite: Zhang, Y., Shindell, D., Seltzer, K., Shen, L., Zhang, Q., Zheng, B., Xing, J., Jiang, Z., and Zhang, L.: Benefits of Recent Clean Air Actions in China on Global Air Quality and Climate Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-529, https://doi.org/10.5194/egusphere-egu2020-529, 2020.

As part of the Copernicus Atmosphere Monitoring Service (CAMS), operated by ECMWF on behalf of the European Commission, global analyses and forecasts of atmospheric composition have been produced operationally since 2015. These were built on many years of previous work under the GEMS and MACC projects, which began producing regular forecasts in 2007.

Since the transition to an operational service, there have continued to be many new developments and improvements to the system in five major upgrades, including increased horizontal and vertical resolution, updated emissions and paramterisations, additional species such as nitrate aerosol, as well as updates to the underlying meteorological model and data assimilation. The components of this system (aerosols, gas-phase chemistry, meteorology and the ocean) are also now coupled more tightly via active feedbacks then ever before.

In this interactive presentation, we will demonstrate the impact of a number of these developments on the performance of the resulting global air quality forecasts, alongside the continuing evolution of our approaches to assessing model improvement against independent in-situ and remote-sensing observations from a variety of platforms.

Because the continuing evolution of an operational system can make the analysis of long-term trends problematic, we will also contrast this with the CAMS global reanalysis product, which (while not using the very latest version of the model) do provide a consistent long-term dataset from 2003 onwards.

How to cite: Kipling, Z., Ades, M., Agusti-Panareda, A., Barré, J., Bousserez, N., Dominguez, J.-J., Engelen, R., Flemming, J., Garrigues, S., Huijnen, V., Inness, A., Jones, L., Parrington, M., Razinger, M., Peuch, V.-H., Rémy, S., Ribas, R., and Suttie, M.: Evolution of the CAMS global air quality forecasting system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17941, https://doi.org/10.5194/egusphere-egu2020-17941, 2020.

Under the Convention on Long-range Transboundary Air Pollution (CLRTAP), 51 northern hemisphere countries are obliged to regularly report their national emissions inventories for selected anthropogenic air pollutants to the United Nations Economic Commission for Europe (UNECE). The EMEP Centre on Emissions Inventories and Projections (CEIP) of the Convention is tasked with administering, archiving and reviewing these data and compiling the EMEP emissions dataset, a complete and gridded inventory for the area between 30 and 82 °N and 30 °W and 90°E.

The reported national emissions inventories and the EMEP emissions dataset are often used by the scientific community as input drivers of air pollution models or as priors for inverse estimation of emissions. However, interpreting model outputs, validation and uncertainties may be restricted by limited knowledge of the peculiarities of such reported data. The purpose of this conference contribution by CEIP is to provide atmospheric modellers with further insight into these reported emissions data. The presentation will introduce the Convention and discuss how complexities of this international agreement have led to diversified reporting requirements and heterogeneity in the frequency and quality of the reported inventories. Current issues with respect to emissions of particulate matter (e.g. reporting of condensable particulate matter and black carbon) will furthermore be discussed and the presentation will also provide perspectives on how the recently agreed long-term strategy for Convention may impact future emissions reporting over the next decade and beyond.

How to cite: Schindlbacher, S., Brendle, C., Mareckova, K., Matthews, B., Pinterits, M., Tista, M., Ullrich, B., and Wankmüller, R.: Insights into the EMEP emissions inventory dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6772, https://doi.org/10.5194/egusphere-egu2020-6772, 2020.

Biomass burning episodes are quite common in the central region of South America and represent the dominant aerosol sources during the dry/burning, between August and October. Large amounts of trace gases and aerosols injected into the atmosphere from these fire events can then be efficiently transported to urban areas in southeastern South America, thus affecting air quality over those areas. Observational data have been of fundamental importance to understand the evolution and interaction of biomass burning products with meteorology and chemistry. However, supplementing this information with the use of a comprehensive air quality modeling system in order to anticipate very acute air pollution episodes, and thus avoiding severe impacts on human health, is also required. Considering this, a new regional air pollution modeling framework for South America is being implemented by the Center for Weather Forecasting and Climate Studies (CPTEC), the National Weather Service of Brazil. This new system, based on the Weather Research and Forecasting with Chemistry model (WRF-Chem; Grell et al., 2005), is being run experimentally and its operational implementation is underway. The forecasts were driven by global forecast data from the GFS-FV3 model for meteorology and from the WACCM model for chemistry, both data sets provided every 6 hours. WACCM forecasts are employed to map gas and aerosol background concentrations to the WRF-Chem initial and boundary conditions, according to the MOZCART chemical mechanism. Two experiments of 48-hour real-time forecast simulations were performed, on a daily basis, during August and September of 2018 and 2019. The experiment for 2019 includes the very strong 3-week forest fire event when the Metropolitan Area of São Paulo, the largest metropolitan area in South America, plunged into darkness on August 19, with day turning into night. Model results are in good domain-wide agreement with satellite data and also with in situ measurements. Besides forecasts of meteorological parameters, this new system provides forecasts of regional distributions of primary chemical species (CO, SO2, NOx, particulate matter including black carbon), of secondary species (ozone, secondary organic aerosols) and air pollution related health indices, all parameters with a resolution of 20 km and for the next 72 hours.

How to cite: Vela, A., Alvim, D., Vendrasco, E., Herdies, D., Figueroa, N., and Penharkar, J.: A new modeling framework for air pollution forecasting in South America, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6445, https://doi.org/10.5194/egusphere-egu2020-6445, 2020.

An ensemble of 9 regional Air Quality models is being run operationally within CAMS-50 project providing the 3D fields of air-pollutant distribution over Europe. The models are initialized from their previous-day's forecasts for 00Z and run for 4 days forward. The same models are used for near-real-time reanalysis of the previous day involving the air-quality observations to adjust the modelled  fields via data assimilation methods, such as 3D-var or optimal-interpolation procedures.  In this set-up the observed near-real-time data do not affect the forecasts.  Development of a method to improve the forecast quality by using the assimilated fields from the previous-day analysis is one of the goals for the CAMS-61 project.

As a prototype evaluation for this study, we made several tests with SILAM model (http://silam.fmi.fi) initializing the simulations from the assimilated or non-assimilated states and evaluated the evolution of the model skill scores along the forecast lead time. The tests were made for summer and winter seasons and for initialization time of 00Z vs 12Z.  In order to generalize the results, and make them independent on particular implementation of 3D-VAR in SILAM, the tests were made also with initialization from the analyses made with other CAMS-50 models.  That experiment utilized the list of species and vertical available in the CAMS-50 product catalog. 

The results of the simulation corroborated with our earlier studies that showed a quite quick relaxation of the scores for runs initialized from analyses to the free-run state: with certain variability between the species, the runs converged to the free-run trajectory generally within several hours.  We also investigated the issues connected with initialization from the incomplete set of species and sparse vertical, which might make the scores of the forecast initialized from the incomplete assimilated model state being worse than the ones from the free-run model.

How to cite: Kouznetsov, R. and Sofiev, M.: On the initial-state assimilation for limited-area air-quality forecasts , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5191, https://doi.org/10.5194/egusphere-egu2020-5191, 2020.

The problem of health risks associated with poor air quality in cities and metropolitan areas is rising in the perception of society. In order to improve air quality, a thorough understanding of different emission sources concerning transport, origin and composition as well as the microphysical and chemical processes involved is of crucial importance.

To assess air quality related issues, we set up a simulation system using the global to regional model system MECO(n), which allows entangling of chemical and physical interactions using a dynamical coupling approach from a global to regional domains down to a resolution of ∼7km. This provides us with a detailed picture of air quality in urbanised regions whilst maintaining a consistent representation and implementation of processes across the scales.

The model setup is evaluated using measurement data from the aerosol robotic network (AERONET) and satellite data from VIIRS instrument on board the polarorbiting Suomi NPP satellite. Moreover we compare our model to ground based measurements of gas species and particulate matter, which are taken from the databases of the Environmental Protection Agency of Rhineland-Palatinate. In this context the limits of the model with respect to aerosol processes especially in the boundary layer are discussed and the resulting limitations in comparing our model output to ground based measurements of particulate matter, specifically PM_2.5 and PM₁₀ are shown.

To demonstrate the flexibility of the model system two model applications relevant for air pollution issues in the Rhine-Main region are presented. The first investigates the direct influence of a localised reduction in anthropogenic emissions on the surrounding regions and the reducing region itself. The second explores deposition regions of kerosene, which is released by aircrafts during emergency fuel dumping event.

How to cite: Barra, M., Fallmann, J., and Tost, H.: Air quality modelling studies in Germany and Europe across scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14913, https://doi.org/10.5194/egusphere-egu2020-14913, 2020.

Surface ozone is a criteria air pollutant, formed by photochemical reactions involving nitrogen oxides (NO_x) and volatile organic compounds (VOCs). Despite recent reductions in the surface ozone burden following precursor emission controls (predominantly concerning NOx) the recent European air quality report published by the European Environment Agency (EEA) highlights that to date still 17 EU member states are reporting ozone concentrations that exceed the target value set for the protection of human health (120 µg/m³, maximum daily 8-hour average (MDA8) not to be exceeded more than 25 times per year (3-year average)). In total, 20 percent of all ozone monitoring sites showed ozone concentrations exceeding the EU target value for the protection of human health, and only 5% of monitoring sites showed ozone concentrations in compliance with the more stringent WHO target value. Here we focus on past and future changes in European surface ozone abundances in a set of simulations performed with the Geophysical Fluid Dynamics Laboratory (GFDL) chemistry-climate model CM3. First, we evaluate the general model performance for the recent past by comparing model output to observations available from the EEA Airbase database. The evaluation is performed on the basis of interpolation of the historic site level observations to a grid of 2.5° x 2°, matching the dimensions of the CM3 model. Our results for the recent past show that the modelled ozone abundances are biased high compared to observations. Therefore, we apply a suite of correction techniques (quantile mapping, delta function) to obtain modelled ozone fields in agreement with observations. Emanating from remediated model data the correction functions derived are applied to transient (2006-2100) simulations following selected Representative Concentration Pathways (RCPs). Using these bias-corrected future simulations we illustrate next potential changes in future European surface ozone air pollution over the course of the 21^st century.

How to cite: Stähle, C., Rieder, H., and Mayer, M.: Changes in European surface ozone air quality over the 21st century, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9445, https://doi.org/10.5194/egusphere-egu2020-9445, 2020.

ZAMG runs two models for air-quality forecasts operationally: ALARO-CAMx and WRF-Chem.

ALARO-CAMx is a combination of the meteorological model ALARO and the photochemical dispersion model CAMx and is operated at ZAMG since 2005. The emphasis of this modeling system is to predict ozone peaks in the north-eastern Austrian flatlands. The outer model grid covers Central Europe with a resolution of 13.8 km, the inner domain is centered over Austria with a resolution of 4.6 km. The model runs twice per day for a period of 48 hours.

The second operational air quality model at ZAMG is the on-line coupled model WRF-Chem. Meteorology is simulated simultaneously with the emission, turbulent mixing, transport, transformation as well as the fate of trace gases and aerosols. Two modeling domains are used for these simulations. The mother domain covers Europe with a resolution of 12 km. The inner, nested domain covers the Alpine region with a horizontal resolution of 4 km. The model runs two times per day for a period of 72 hours and is initialized with ECMWF forecasts.

The evaluation of both models is conducted for the period from January to September 2019 with the focus on ozone. The summer 2019 was the 2^nd warmest summer since the beginning of the meteorological measurements in Austria more than 200 years ago. Although this summer had favorable conditions for Ozone production (sunny and hot weather, less rain), only a few air quality stations in Eastern Austria have measured exceedances of the ozone information threshold (180 µg/m³) on overall 5 days. The measurements of the air-quality stations are compared with the area forecasts for every province of Austria. Besides the evaluation, air quality forecasts of ALARO-CAMx and WRF-Chem are compared.

How to cite: Flandorfer, C., Hirtl, M., and Scherllin-Pirscher, B.: Evaluation of O3 forecasts of ALARO-CAMx and WRF-Chem, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13535, https://doi.org/10.5194/egusphere-egu2020-13535, 2020.

This study is part of the Mitigation of Urban Climate and Ozone Risks (MiSKOR) project. MiSKOR aims to use a collection of tools to mitigate the problems of the urban heat island effect and ozone (O3) pollution in and around medium sized cities in northern Bavaria (NB).  In this study, we developed modelling tools to estimate (hindcast), classify (O3 >= 120 ug/m3 or O3 < 120 ug/m3), and forecast hourly O3 concentrations at nine unmonitored sites in NB. Three machine learning algorithms (MLAs) including linear- and tree-based eXtreme Gradient Boosting Machines (MLR-XGBM and Tree-XGBM) and logistic regression (LR) are used for O3 modelling. MLAs are trained by using hourly observations of O3 and its chemical and meteorological precursors from seven monitored sites in NB. In addition, the daily average of surface O3 observations along 6-hour back trajectories, produced by HYSPLIT model, is fed into MLAs to provide a rough estimation of O3 transport in a local scale. MLAs are compared with two state of the art regional deterministic models (DMs) namely the ECMWF Copernicus Atmosphere Monitoring Service (CAMS) regional air quality model for Europe (CAMS-EU) and the DLR WRF-POLYPHEMUS air quality system (used only for O3 forecast purpose). Finally, we created a new hybrid model by combining the O3 estimations from the best MLA model and the regional air quality model CAMS-EU.

According to averaged metrics from leave-one-site-out cross-validation (LOOCV), MLR-XGBM outperformed other models in the estimation of O3. This model yielded summertime RMSE and Spearman correlation coefficient (SCC) of 13.6 µg/m3 and 0.91 respectively. Interestingly, the hybrid model significantly improved the accuracy of O3 estimations. It reduced the summertime seasonal RMSE to 11.4 µg/m3 and increased the lowest seasonal SCC to 0.95. MLR-XGBM also yielded the best performance in O3 forecast compared to CAMS-EU and WRF-POLYPHEMUS. With regard to O3 classification LR outperformed other models. We also found that using remotely sensed lower troposphere O3, from IASI/GOME2, improves the classification of high extreme O3 in summertime.

How to cite: nabavi, S. O., Nölscher, A., Haimberger, L., Cuesta, J., Thomas, C., Held, A., and Samimi, C.: Site-scale estimation of Ozone in Northern Bavaria using Gradient Boosting Machines, Deterministic Regional Air Quality Models and a Hybrid Model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11624, https://doi.org/10.5194/egusphere-egu2020-11624, 2020.

Comprehensive measurements were conducted at the summit of Mount (Mt.) Huang, a rural site located in eastern China during the summer of 2011. They observed that ozone showed pronounced diurnal variations with high concentrations at night and low values during daytime. The Weather Research and Forecasting with Chemistry (WRF-Chem) model was applied to simulate the ozone concentrations at Mt. Huang in June 2011. With processes analysis and online ozone tagging method we coupled into the model system, the causes of this diurnal pattern and the contributions from different source regions were investigated. Our results showed that boundary layer diurnal cycle played an important role in driving the ozone diurnal variation. Further analysis showed that the negative contribution of vertical mixing was significant, resulting in the ozone decrease during the daytime. In contrast, ozone increased at night owing to the significant positive contribution of advection. This shifting of major factor between vertical mixing and advection formed this diurnal variation. Ozone source apportionment results indicated that approximately half was provided by inflow effect of ozone from outside the model domain (O_3-INFLOW) and the other half was formed by ozone precursors (O_3-PBL) emitted in eastern, central, and southern China. In the O_3-PBL, 3.0% of the ozone was from Mt. Huang reflecting the small local contribution (O_3-LOC) and the non-local contributions (O_3-NLOC) accounted for 41.6%, in which ozone from the southerly regions contributed significantly, for example, 9.9% of the ozone originating from Jiangxi, representing the highest geographical contributor. Because the origin and variation of O_3-NLOC was highly related to the diurnal movements in boundary layer, the similar diurnal patterns between O_3-NLOC and total ozone both indicated the direct influence of O_3-NLOC and the importance of boundary layer diurnal variations in the formation of such distinct diurnal ozone variations at Mt. Huang.

How to cite: Gao, J.: Diurnal variations and source apportionment of ozone at the summit of Mount Huang, a rural site in Eastern China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8483, https://doi.org/10.5194/egusphere-egu2020-8483, 2020.

Regional chemistry-transport models typically simulate the physical and chemical state of the atmosphere at a high spatial resolution, e.g. of less than 7 km. At this relatively high spatial resolution, air quality and relevant processes within cities can be assessed to facilitate strategic mitigation planning. Comparison of regional models with satellite and ground-based observations helps validate the models and evaluate emission inventories, as well as satellite retrieval algorithms. For example, an underestimation of atmospheric trace gases (like often found for NO₂) by satellite observations can be improved by providing high-resolution input fields from regional models.

MECO(n), a global-to-regional chemistry climate modeling system, in which the finer resolved domains receive their initial and boundary conditions on-line from the next coarser model instance, was set-up with Germany as focus. 1-way nested MECO(3)  simulations were performed for May 2018 with spatial resolution up to ~2.2 km × 2.2 km in the finest domain. Model simulations accounting separately for both TNO MACC III and EDGAR 4.3.2 anthropogenic emissions are evaluated against TROPOMI observations. A diurnal factor was applied to road transport emissions to account for their temporal variation. For the comparison with TROPOMI data, we applied a novel method of online sampling of model fields along the satellite overpass by also accounting for the difference in local solar time across the swath width, which can be up to 90 minutes. Modified airmass factors in the TROPOMI data product, using the model calculated NO₂ a priori profiles and taking into account averaging kernels, resulted in an improved agreement of the spatial pattern of NO₂ vertical column density (VCD) between model and satellite.

NO₂ VCDs over Mainz, calculated using model output at the finest model resolution, were compared against MAX-DOAS observations for the simulation period. Vertical profiles of NO₂ were also retrieved in 4 azimuth directions around Mainz by profile inversion of MAX-DOAS measurements. The temporal (e.g. day-to-day and diurnal) variation of the 3-D NO₂ field derived from the model was evaluated against the MAX-DOAS observations. For the cloud-free days, the model is able to reproduce the temporal development with satisfactory temporal correlation (slope=0.7, r=0.5) of the NO₂ VCDs. For a direct comparison of measured slant column densities of NO₂, height-resolved 2-D box airmass factors were calculated using McArtim (Monte Carlo Atmospheric radiative transfer model) and applied to the modelled trace gas profiles along individual elevation angles of the measurements. This comparison procedure accounts for the complex dependency of the MAX-DOAS column densities on the 3D (vertical and horizontal) trace gas distribution in the measurement direction.

How to cite: Kumar, V., Remmers, J., Steil, B., Kerkweg, A., Lelieveld, J., Beirle, S., Wang, Y., Donner, S., Pozzer, A., and Wagner, T.: Comparison of regional chemistry-modelled NO2 tropospheric columns and profiles with TROPOMI observations and 4-azimuth MAX-DOAS measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1934, https://doi.org/10.5194/egusphere-egu2020-1934, 2020.

Rulan Verma¹,Salim Alam², William Bloss², Prashant Kumar³, Mukesh Khare^1*

 ¹ Department of Civil Engineering, Indian Institute of Technology Delhi, India

 ² School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom

³ Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, University of Surrey, Guilford, United Kingdom

                                                 mukeshk@civil.iitd.ac.in

ABSTRACT

The Delhi-National Capital Region of India is home to approximately 46 million people. With rapid development, this region is experiencing widespread urbanization and industrialization. It is expected to become the most populous region in the world by 2027 (World Population Prospects 2019, UN). With rapid growth, the region is facing severe challenges of air pollution. Delhi-NCR is amongst the most polluted regions in the world. PM_2.5 is recognized as a prominent pollutant in the region. Ambient air pollution is recognized as a class I carcinogen and is one of the highest risk factors for premature deaths worldwide.  Understanding the effects of local and global meteorology would help in the identification of source pathways and source areas of pollutant dispersion. This paper presents a methodology for modelling and assessment of PM_2.5 over the Indian subcontinent using NOAA’s Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). To develop the approach, PM_2.5 data collected over a period of 32 days at IIT Delhi supersite (28.54°N,77.19°E) were utilized.  PM_2.5 mass concentrations were monitored using PM_2.5 samplers and a TEOM (Tapered Element Oscillating Microbalance) monitor. Meteorological data were obtained through the Global Data Assimilation System (GDAS) which places observations into a gridded model space.770 air mass back trajectories were generated and clustered into mean trajectories using the cluster analysis function of HYSPLIT. PM_2.5 monitored during winters (15/01/2018-15/02/2018) was correlated with clustered back trajectories to understand the effect of local and global meteorology. The time-series of PM_2.5 were correlated with different clusters to understand the impact of winds coming from different regions and heights. Major advection source pathways for PM_2.5 were identified. The study found that 59% of the time, PM_2.5 transport was affected by wind movements from north-west of supersite moving through Pakistan-Punjab-Haryana-supersite. During this period PM_2.5 concentration at supersite were 169±73 μg/m³. The highest PM_2.5 concentration of 237±81 μg/m³ were observed when the winds were recirculating locally. Wind roses produced using meteorological data obtained from  Indian Meteorological Department stations conforms with the wind flow in GDAS. This methodology can be utilized in other regions for quantifying the major source pathways and source areas for air pollutant dispersion. This understanding would help in framing hotspot and airshed based interventions and mitigation strategies to control air pollution.

KEYWORDS: PM_2.5; AIR POLLUTION; HYSPLIT; MONITORING; DISPERSION; SOURCE PATHWAYS; METEOROLOGY; MODELLING; SOURCE REGIONS

How to cite: Verma, R.: HYSPLIT Modelling Approach for the Assessment of PM2.5 over Indian Subcontinent, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-574, https://doi.org/10.5194/egusphere-egu2020-574, 2020.

With the intensification of Chinese source control of air pollution, there is an urgent need for refined and rapid source apportionment techniques. A refined source apportionment method was constructed based on an off-line sampling dataset using a receptor model coupled with a source-oriented model, and the method was implemented in Shijiazhuang during the heating period. The refined results for source apportionment mainly included temporal, spatial, and source-category refinement data. The results indicated that the mean concentration of PM_2.5 during the heating period was 96 μg/m³. Organic carbon (OC) and NO₃^- were found to be the dominant species of PM_2.5 during the study. A high correlation was detected between elemental carbon (EC) and NO₃^– on polluted days, which was suggestive of the stagnant condition that accumulates EC and nitrate simultaneously. Secondary particle formation greatly promoted the occurrence of haze events. Secondary sources (34.9%), vehicle exhaust (18.6%), coal combustion (20.0%), industrial emissions (9.2%), crustal dust (9.7%), and biomass burning (7.6%) were the major sources during the heating period. The contributions of secondary sources and vehicle exhaust increased on polluted days, while those of coal combustion, industrial emissions and crustal dust decreased significantly. The contribution percentage of secondary sources from the southeast direction was basically the highest, while those of vehicle exhaust from the northwest or southeast directions were relatively higher as well, likely due to the distribution of traffic arteries. Based on the refined results for the source-category assessment, we found that the heating boilers (17.0%), non-road mobile (13.8%), diesel vehicles (10.4%), residential combustion (6.7%), road dust (5.5%), and architectural material industry (4.9%) were the major contributors to PM_2.5. There was some uncertainty in the distribution proportions of the refined results, which were derived based on the emission inventory and the results of CALPUFF model.

How to cite: Liu, B., Zhang, Y., Feng, Y., Dai, Q., and Song, C.: A refined source apportionment study of atmospheric PM2.5 during winter heating period in Shijiazhuang, China, using a receptor model coupled with a source-oriented model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2523, https://doi.org/10.5194/egusphere-egu2020-2523, 2020.

The Pearl River Delta (PRD) region is one of the most developed city clusters in China, and it is also the area that suffers from severe air pollution. A key problem in addressing pollution is to find out where the pollutants come and how to control them. Most of the previous studies focused on the source area, and source category contribution analysis, but fewer studies paid attention to the temporal contribution, which is also an important factor in policymaking. Therefore, in this study, based on the CAMx-PSAT model, we extended the model to track the contribution of the sources emitted at different periods. The updated PSAT can reflect the temporal correlation between the source and receptor and provide scientific support to efficient control policymaking. The simulation result of a high PM_2.5 episode shows that the emission outside the PRD region is the major contributor to PM_2.5 over the PRD region. PM_2.5 mainly comes from the emission within the current two days. Under the control of the high-pressure system, low wind speed hinders the diffusion of PM_2.5 and paves the way for the accumulation of the pollutants. The emission two days ago can still have a considerable contribution during the high concentration period. The results suggest that emission control measurements should be implemented in advance when adverse meteorology condition is predicted.

How to cite: Chen, Y., Fung, J. C.-H., and Lu, X.: PM2.5 temporal source apportionment analysis over the Pearl River Delta region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13076, https://doi.org/10.5194/egusphere-egu2020-13076, 2020.

Aiming at evaluating the impact of coal-fired power plants on urban air quality and human health, a one-month intensive observation campaign was conducted in a typical polluted city located in “2+26” city cluster in China North Plain in December 2017. The observation results illustrated that coal-fired plant can increase the PM_2.5 concentration by ~5% on monthly average in city scale. The impacts differed under various diffusion conditions. A three-dimensional Nested Air Quality Perdition Model (NAQPMS) with source apportionment was employed to reveal the impacts. The results indicated that the power plant had the greatest effect on regional air quality during severe pollution period while it was ignorable during the excellent dissipation period under the robust wind. PM_2.5 contributed by the power plant was below 150 m, 100 km far away, and about 5 μg m^-3 during light pollution period. When it came to accumulation period, the plume reached 500 m height, diffused to downwind area about 100 km away within half a day, and with a maximum contribution of 40 μg m^-3 to PM_2.5. The affected area extended further to 250 km in severe pollution period and the contribution to PM_2.5 was at least 10 μg m^-3 in different distances. The affected height was up to about 500 m with more than 10 μg m^-3 PM_2.5 mainly constrained below 150 meters. Overall, regional integrated control strategies should be taken for power plants in “2+26” city cluster during pollution episodes to further improve the air quality.

How to cite: Chen, X., Yang, T., Wang, Z., and He, L.: Investigating the impacts of coal-fired power plants on ambient PM2.5 by a combination of chemical transport model and receptor model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4031, https://doi.org/10.5194/egusphere-egu2020-4031, 2020.

High concentrations of atmospheric aerosols with aerodynamic diameter below 2.5 mm (PM2.5) are frequently observed in several Central European countries during the heating season (October – March). Poland belongs to a group of EU countries with the highest concentrations of PM2.5, according to the European Environmental Agency. Large exposure to atmospheric pollutants leads to significant number of premature deaths attributable to adverse air quality in Poland.

Coal combustion for residential heating is one of the main sources of PM2.5 in Poland. The quality of this fuel is often unknown, and this increases the uncertainty of national emission inventories and makes the modelling of PM2.5 concentrations challenging. Second, daily temporal emission profile (i.e. hours of the day when emission is released to the atmosphere) in residential heating sector is also rather uncertain. In this work, we developed a daily temporal emission profile using available measurements of PM2.5 and PM10 concentrations from the 2017-2018 heating season. The profile was compared with the existing profile proposed within the INERIS project. New profile has longer peak of afternoon and night time emission, if compared to INERIS, and the morning peak is significantly lower. It means that more emission is released to the atmosphere during unfavorable meteorological conditions such as calm winds and temperature inversions, which are frequently observed during the afternoon and night.

We have run two simulations using the EMEP4PL model with new and old (INERIS) emission profile. The simulations covered three heating seasons of 2015-2016, 2017-2018 and 2018-2019. Application of the new emission profile results in increased model – measurements correlation and reduced model bias.

How to cite: Kryza, M., Werner, M., and Dudek, J.: Improving PM2.5 modelling results through development of the new hourly temporal emission profile – a case study of Poland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4771, https://doi.org/10.5194/egusphere-egu2020-4771, 2020.

Some European countries in Eastern or Central Europe, such as Poland, have serious problems with air quality. High concentrations of particulate matter (PM) in winter are often related to high coal and wood combustion for residential heating. Meteorological conditions, i.e. low air temperature and anticyclones, provide favourable conditions for the accumulation of air pollution, rendering it harmful to people.  PM concentrations during the warmer period are much lower, however there are episodes with elevated concentrations related to e.g. long-range transport of pollutants from biomass burning areas. Policy makers in Poland put a lot of effort to improve air quality as well as inform and aware people on harmful effects of air pollution. One of the relevant tools which provides information on the past, current and future state of the air pollution are chemical transport models.

In this study we aim for validation of PM10 and PM2.5 concentrations from two different chemical transport models – WRF-Chem and EMEP4PL and two different emission databases – a) a regional EMEP database, and b) a local database provided by the Chief Inspectorate of Environmental Pollution. Modelled PM10 and PM2.5 concentrations were compared with observations from Polish stations for the year 2018. The results show a clear seasonal variation of the models performance with the lowest correlation coefficients in summer. Higher seasonal variability is observed for WRF-Chem than EMEP, which is probably related to differences in calculations of boundary layer height. Application of local database improves the results for both models. For several months, the performance of WRF-Chem and EMEP is clearly different, which shows that an ensemble approach with an application of these two models could improve the modelling results. The differences in the model performance significantly influence the results of the population exposure assessment.

How to cite: Werner, M., Kryza, M., and Dudek, J.: Two models and two emission databases – evaluation of the PM10 and PM2.5 concentrations modelled with WRF-Chem and EMEP4PL, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3768, https://doi.org/10.5194/egusphere-egu2020-3768, 2020.

How to cite: Varga-Balogh, A., Leelőssy, Á., Lagzi, I., and Mészáros, R.: A data fusion method to improve winter PM10 concentration predictions in Budapest based on the CAMS air quality models , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16133, https://doi.org/10.5194/egusphere-egu2020-16133, 2020.

In the past years, the PM2.5 concentration in Beijing decreases from 89 ug/m³ in 2013 to 42 ug/m³ in 2019, especially in the recent three years, that the PM2.5 concentration rapidly decreases from 73 ug/m³ in 2016 decreases to 42 ug/m³. An air quality modeling system, based on WRF-SMOKE-CMAQ model, was established before APEC 2014 to forecast daily air quality and assess future air quality improvement plans, which plan expects Beijing’s PM2.5 would reach to 53 ug/m³ in 2020, and reach to 35 ug/m³ in 2030. Actually, the PM2.5 concentration in Beijing has fallen faster than expected, that the annual PM2.5 concentration is 42 ug/m³ in 2019. So how much influence do meteorological factors and emission control have on the annual PM2.5 concentration? The WRF-SMOKE-CMAQ modeling system has been used to re-build the PM2.5 concentration characteristics of Beijing from 2013 to 2019 to distinguish these two factors. Preliminary results show that under the same emission scenarios, the annual average concentration of PM2.5 in Beijing in 2013 was 68.6 ug/m³, and the average annual concentration of PM2.5 in 2017 was 69.4 ug/m³. More detailed model results will be presented.

How to cite: Wu, Q. and Xu, Q.: Numerical Study of the impact of Meteorological and Emission control on the decreasing of PM2.5 concentration in Beijing by WRF-SMOKE-CMAQ model system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19694, https://doi.org/10.5194/egusphere-egu2020-19694, 2020.

Interannual variability in large circulations associated with climate connections, such as monsoon and El Niño, have a significant impact on winter PM_2.5 concentrations in East Asia. In this study, we use the global 3D chemical transport model (GEOS-Chem) over the last 35 years to investigate the relationship between major climate variability and winter PM_2.5 concentrations in East Asia. First, the model is evaluated by comparing the simulated and observed aerosol concentrations with the ground and satellite-based aerosol concentrations. The results indicate that this model well reproduces the variability and magnitude of aerosol concentrations observed in East Asia. Sensitivity simulations are then used with fixed anthropogenic emissions to investigate the effects of meteorological variability on changes in aerosol concentrations in East Asia. The variability of winter PM_2.5 concentrations in northern East Asia was found to be closely correlated with ENSO and Siberian high position. To predict PM_2.5 concentrations using key climate indices, we develop multiple linear regression models. As a result, the predicted winter PM_2.5 concentrations using the key climate index are well reproduced in the simulated PM_2.5 concentrations, especially in northern East Asia.

How to cite: Jeong, J., Park, R., Yeh, S.-W., and Roh, J.-W.: Estimation of winter PM2.5 concentrations in East Asia associated with climate variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13479, https://doi.org/10.5194/egusphere-egu2020-13479, 2020.

On June 24^th 2018 one of the largest UK wildfires in recent history broke out on Saddleworth Moor, close to Manchester, in north-west England. June 2018 was anomalously hot and dry across the UK which led to the peat on the moor drying out and becoming suscpetible to ignition. Since wildfires close to large populations in the UK have been relatively small and rare in the past, there is little knowledge about the impacts. This has prevented the development of effective strategies to reduce them. This paper uses a high-resolution coupled atmospheric-chemistry model to assess the impact of the fires on particulate matter with a diameter less than 2.5 µm (PM_2.5) air quality (AQ) across the north-west region and the subsequent impact on health from short-term exposure. We find that the fires substantially degraded AQ across the north-west. PM_2.5 concentrations increased by more than 300% in Oldham and Manchester and up to 50% in areas up to 80 km away such as Liverpool, Wigan and Warrington. This led to a third of the population (4.7 million people) in the simulation domain (-4.9-0.7°E and 53.0-54.4°N) being exposed to moderate PM_2.5 concentrations on at least one day, according to the Daily Air Quality Index (36-53 µg m^-3), between June 23^rd and 30^th 2018. This equates to 4.5 million people being exposed to PM_2.5 above the WHO 24-hour safe-limit exposure of 25 µg m^-3 on at least one day. Using a concentration-response function we calculate the short-term health impact which indicates that up to 60% of excess mortality between June 23^rd and 30^th 2018 was attributable to the fires. This represents up to a 165% increase in excess mortality across the region compared to a simulation with no fires. We find the impact of mortality due to PM_2.5 from the fires on the economy was also substantial (£5.5m). Thus, our results indicate the need to introduce legislation and education to both reduce the likelihood of wildfires and reduce the population’s exposure to harmful air pollutants during their occurrence. This is particularly relevant given that wildfires are projected to become more common in the future through climate change and land-use change.

How to cite: Graham, A., McQuaid, J., Arnold, S., Pringle, K., Pope, R., Chipperfield, M., Conibear, L., Butt, E., Kiely, L., and Knote, C.: Substantial degradation in Air Quality due to Saddleworth Moor Wildfire , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8290, https://doi.org/10.5194/egusphere-egu2020-8290, 2020.

Antalya is a city at the Turkish Riviera located on Mediterranean coast of southwestern Turkey and it is the fifth populated city in Turkey. The city has a downtown population of over 2 million. Agriculture and tourism activities are the most important sources of income in the region. Antalya is a very important tourism destination and welcomes more than 10 million tourists every year.

Nowadays, with the rapid increase in urbanization, air pollution has been one of the most important environmental problems especially in big cities. In order to solve the pollution problems as soon as possible, the largest air pollution sources must be determined first. Air quality models are used extensively in air quality studies as they allow these problems to be identified quickly, cheaply and effectively. The semi-parameterized Operational Street Pollution Model (OSPM^®) has been widely used around the globe to determine levels of air pollution on local or street-scale for urban street canyons (Berkowicz 2000, Ketzel et al. 2012).

For this study; four street canyons along the main roads in central Antalya were selected (100. Yıl Avenue, Yener Ulusoy Avenue, Adnan Menderes Avenue, Kızılırmak Street).  Modeling has been carried out for a period of one year (July 2014 – July 2015) for the pollutants PM2.5 and PM2.5-10.

The urban background concentrations for particulate matter (PM2.5 and PM2.5-10) were collected using stack filter unit system. Total of 169 samples were collected once in a two-day period between July 2014 and July 2015 (Tepe 2016). Meteorological parameters and traffic data used in this study were obtained from Turkish State Meteorological Service and Turkish Statistical Institute, respectively.

REFERENCES

Berkowicz, R. OSPM - A Parameterised Street Pollution Model. Environ. Monit. Assess. 65, 323 331 (2000)

Ketzel M, Jensen SS, Brandt J, Ellermann T, Olesen HR, Berkowicz R and Hertel O. Evaluation of the Street Pollution Model OSPM for Measurements at 12 Streets Stations Using a Newly Developed and Freely Available Evaluation Tool. J Civil Environ Eng, S1:004 (2012)

Tepe, A. Investigation of Concentrations and Source Apportionment of Metals Attached to PM2.5 and PM10 in Antalya Ambient Air (Unpublished master’s thesis). Akdeniz University, Antalya, Turkey (2016)

How to cite: tepe, A. M., Ketzel, M., Im, U., and Doğan, G.: Street Scale Air Pollution Modelling in Antalya on Mediterranean Coast of Turkey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14671, https://doi.org/10.5194/egusphere-egu2020-14671, 2020.

The organic aerosols (OA) contribute significantly to fine particulate mass in the atmosphere, however, most global climate models do not include elaborate treatment associated with the production of secondary organic aerosols (SOA) involving complex chemical processes to save computational time. As a result, the concentrations of SOA simulated by these climate models are often highly uncertain. Moreover, very limited research has been done on SOA and its precursors, particularly on the contribution of individual sources towards the SOA concentrations across India. In this study, we investigate the sensitivity of the production of SOA from different VOC sources and different atmospheric oxidants by the Community Atmospheric Model version 4 coupled with an extensive interactive atmospheric chemistry module (CAM4-Chem). The main objective of our present research is to understand the contribution of individual sources of VOCs towards the production and distribution of SOA across the Indian region. We carried out a series of systematically designed simulations using the CAM4-Chem model to understand the sensitivity of simulated SOA over the Indian region to changes in only emissions of VOCs from anthropogenic, biogenic, and biomass burning emissions from preindustrial (PI) to present-day (PD) period. In order to avoid the influence of changes in meteorology from PI to PD on the production of SOA, all simulations are performed for the same period from 2004 to 2014 with identical meteorology prescribed to the model based on MERRA2 data, while the VOC emissions from anthropogenic, biogenic, and biomass burning sources are allowed to change from PI to PD in different simulations. Our results show that the simulated distribution of SOA over the Indian region in PD is linked to the significant changes in the emissions of VOCs from anthropogenic, biogenic, and biomass burning emissions sources from PI to PD. We find that the changes in emissions of VOCs from biogenic sources from PI to PD associated with land use and land cover changes contribute significantly along with the changes in emissions from anthropogenic sources towards the total changes in SOA distribution over the Indian region over the same period.  The global annual mean burden of SOA from our sensitivity simulations vary in the range of 0.65Tg to 0.80Tg due to variations in emission of different VOCs that are precursors to the production of SOA in the atmosphere. These sensitivity simulations improve our understanding of atmospheric chemistry and specifically about the formation of SOA from different precursor gases originating from diverse anthropogenic, biogenic, and biomass burning emissions sources. More results with greater detail will be presented.

How to cite: Vats, P., Ganguly, D., and Biswas, A.: Investigating the sensitivity in production of SOA from its precursor VOCs with different sources of emissions using an interactive chemistry climate model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19207, https://doi.org/10.5194/egusphere-egu2020-19207, 2020.

Organic aerosol (OA) still remains one of the most difficult components of the aerosol to simulate, given the multitude of its formation precursors, the uncertainty of its formation pathways and the lack of measurements of its detailed composition. The LANDEX project (The LANDes Experiment), during its intensive field campaign in summer 2017, gives us the opportunity to compare a detailed list of measurements (VOC, NOx, radicals including NO₃, aerosol components, …) obtained within and above the Landes forest canopy, to simulations performed with CHIMERE, a regional Chemistry-Transport Model. The Landes forest is situated in the south-western part of France, and is one of the largest anthropized forest in Europe (1 million ha), composed by a majority of maritime pine trees, strong terpene emitters, providing a large potential for biogenic SOA formation.

In order to simulate organic aerosol build-up in this area, the set-up of a specific model configuration, adapted to local peculiarities, was necessary. As the forest is inhomogeneous, with interstitial agricultural fields, high-resolution 1 km simulations over the forest area were performed, imbedded into a 5 km resolved French and a 25 km resolved European domains. BVOC emissions were predicted by MEGAN, but specific land cover needed to be used, chosen from the comparison of several high-resolution land-cover databases. Also, the tree species distribution needed updated for the specific conditions of the Landes forest. In order to understand the canopy effect in the forest, sensitivity tests were also performed and the diffusivity between the first two layers were changed. The impact of each of these refinements with respect to the standard model set-up on the concentration changes of biogenic VOCs and organic aerosol was calculated and compared to observations. In addition, the sensitivity of SOA build-up with respect to the organic aerosol scheme (standard scheme within CHIMERE, VBS schemes with updated yields for OA formation from BVOCs, …) was assessed.

The ensemble of simulations allowed tracing back the origin of BSOA build-up within and above the Landes forest canopy. Above the canopy, the major simulated pathway of SOA formation is monoterpene oxidation by NO₃, while within the canopy, for sufficiently low mixing during nighttime, the NO₃ radical is suppressed and only little contributes to SOA build-up. This is in accordance to observations and reactivity considerations which show that within the canopy, ozone attack on sesquiterpenes is the major nighttime SOA source.  

How to cite: Cholakian, A., Beekmann, M., Coll, I., Flaud, P.-M., Perraudin, E., and Villenave, E.: Simulation of SOA formation in the Landes pine forest in south-western France, relative weight of initial ozone, NO3 and OH attack ?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19763, https://doi.org/10.5194/egusphere-egu2020-19763, 2020.

Benzo[a]pyrene is relatively stable in the atmosphere and can be transported on a regional scale. Benzo[a]pyrene concentrations exceed standard limits in many regions of the world. It is proved that this compound is harmful to the environment and human health.

According to the CAFÉ Directive (2008/50/EC), the objective is to achieve a concentration of B[a]P below 1ng/m3 in PM10 aerosol. Observed B[a]P concentration in Poland is among the highest in Europe. These exceedances are attributed to the emission from individual heating, where many old installations are still in operation. Major B[a]P emissions are due to low-quality fuels and non-reported municipal waste burning.

To support the Chief Inspectorate of Environmental Protection in the frame of the annual assessment for 2018 and five-year assessment for the period 2014-2018, the spatial distribution of B[a]P was calculated using the GEM-AQ model (Kaminski et al. 2008). A new national high-resolution bottom-up emission inventory was used for the entire area of Poland. The results at the resolution of 2.5 km were compared with observations from over 100 stations from the National Measurement Network. We will discuss the spatial and seasonal variability od B[a]P concentrations as well as year-to-year changes related to meteorological conditions.

How to cite: Kaminski, J. W., Struzewska, J., Durka, P., Jeleniewicz, G., and Kawka, M.: Spatial and temporal variability of benzo[a]pyrene over Poland based on modelling and observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16878, https://doi.org/10.5194/egusphere-egu2020-16878, 2020.

Aerosol concentrations over Europe and Germany were simulated for the years 1985 and 2013 using the aerosol-chemistry transport model COSMO-MUSCAT. The aerosol fields from the two simulations were used in a high-resolution meteorological model for a sensitivity study on cloud properties. The modelled aerosol and cloud variables were compared to a variety of available observations, including satellites, remote sensing and in-situ observations. Finally, the radiative forcing of the aerosol could be estimated from the different sensitivity simulations.

Due to reduction of emissions the ambient aerosol mass and number in Europe was strongly decreased since the 1980s. Hence, today’s number of particles in the CCN size range is smaller. The HD(CP)² (High Definition Clouds and Precipitation for Climate Prediction) project amongst others aimed at analysing the effect of the emission reduction on cloud properties.

As a pre-requiste, the aerosol mass, number, and composition over Germany were simulated for 1985 and 2013 using the regional chemistry-transport-model COSMO-MUSCAT. The EDGAR emission inventory was used for both years.

The model results were compared to observations from the two HD(CP)² campaigns that took place in 2013 (HOPE, HOPE-Melpitz) as well as the AVHRR aerosol optical thickness product, which is available from 1981 onwards. Despite the fact, that emissions of the 1980s are very uncertain, the modelled AOD is in good agreement with observations. The modelled mean CCN number concentration in 1985 is a factor of 2-4 higher than in 2013.

Within HD(CP)², the ICON weather forecast model was applied in a configuration allowing for large-eddy simulations. In these simulations, the time-varying CCN fields for the year 1985 and 2013 calculated with COSMO-MUSCAT were used as input for ICON-LEM. In the present-day simulation, the cloud droplet number agrees with observations, whereas the perturbed (1985) simulation does not with droplet numbers about twice as high as in 2013. Also, for other cloud variables systematic changes between the two scenarios were observed.

How to cite: Schrödner, R., Genz, C., Heinold, B., Baars, H., Henning, S., Costa Surós, M., Sourdeval, O., Carbajal Henken, C., Madenach, N., Tegen, I., and Quaas, J.: Air pollution and cloud-interaction over Europe in 1985 and today, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13640, https://doi.org/10.5194/egusphere-egu2020-13640, 2020.

In December 2015, the Beijing-Tianjin-Hebei (BTH) region in China experienced several episodes of heavy air pollution. The government issued emergency control measures immediately to reduce the pollution, which provided a good opportunity to explore impact of emission reduction on aerosol-radiation interaction. In this study, four tests were conducted, including the BASE1 simulation with emission reduction and aerosol-radiation interaction on, BASE2 simulation with emission reduction and aerosol-radiation interaction off, SEN1 simulation without emission reduction and aerosol-radiation interaction on and SEN2 simulation without emission reduction and aerosol-radiation interaction off. Results show that the aerosol-radiation interaction reduced downward shortwave radiation, temperature at 2 m and boundary layer height in region, but increased the relative humidity at 2 m, which were favorable for pollution accumulation. The interaction effect due to emission reductions increased downward shortwave radiation by 0~5 W/m² on average, leading to a weak decrease of surface temperature by 0~0.05 °C, a weak decrease of the daytime boundary layer height by 0~8 m, and a weak increase of daytime mean relative humidity at 2m by 0.5%. If there were with aerosol-radiation interaction, it would enhance the effectiveness of emission control measures on air pollution control. The enhancement of PM_2.5, PM₁₀, and NO₂ emission reduction effects reaches by 7.62%, 6.90%, 11.62% over region, respectively.

How to cite: Guo, C. and Wen, W.: Impact of emission reduction on aerosol-radiation interaction during heavy pollution periods over Beijing-Tianjin-Hebei region in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-68, https://doi.org/10.5194/egusphere-egu2020-68, 2020.

In order to manage fine particulate matter, class 1 carcinogen, various policies are being prepared by the government. The government announced a set a policy measures to confront pollution issues in November 2019. Diesel cars classified as grade 5 will be banned and maximum 27 coal power plants would be plugged off from December to March when fine particulate matter usually worsen to curtail air pollution by more than 20 percent. Despite such efforts, however, it is difficult to improve the concentration of fine particulate matter. In particular, as fine particulate matter management policies are biased toward the management of coal power plants or diesel cars, port and ship emissions management are relatively insufficient.

In the case of major Korea’s port cities such as Busan and Incheon, the impacts of fine particulate matter from ship emissions are analyzed to be significant. In particular, the use of low-grade fuel such as bunker C oil, which has high sulfur content, generates a large amount of fine particulate matter and other air pollutants. As such, for fine particulate matter management in port areas, the impact of ships, cargo handling equipment and cargo trucks, which are major sources of emissions, needs to be quantitatively understood.

Under this background, the emission characteristics of ship emissions were identified by using national air pollutants emissions data in 2015, which improved the calculation method of ship emission sources and the contribution concentration of PM2.5 was analyzed using WRF and CMAQ/BFM. The modelling period is one year in 2016, and the resolution of 9km modeling was applied to Korea.

As one of the main results, the annual mean PM2.5 contribution concentration from domestic ship emission sources was analyzed to be 0.57μg/㎥, and the PM2.5 contribution concentration by local governments was calculated to be most affected by the 1.39μg/㎥ in Busan. The results of this study have not taken into account additional sources of emissions such as cargo handling equipment and cargo trucks using ports, and if this is taken into account, the actual contribution concentration of PM2.5 in port areas is expected to be higher.

The results of this research can be used as basic data when establishing policies for reducing fine particulate matter by major emission sources by local governments.

How to cite: Seo, J. and Moon, N.: Estimation of Contribution to PM2.5 from Ship Emissions over Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17502, https://doi.org/10.5194/egusphere-egu2020-17502, 2020.

International ship traffic is steadily increasing since many years. The associated emission of pollutants like sulphur and nitrogen compounds has strong effects on the coastal air quality and the environment. For instance, investigations of Sofiev et al. (2018) show that the ships contribute about 20 % to the sulphur dioxide and 9 % to the global emission of nitrogen oxides. Thus, shipping is also important for climate change  through emissions of greenhouse gases and aerosol particles and the input of acidifying and eutrophying substances into coastal waters.

Therefore, an accurate estimation of ship emissions and their spatio temporal distribution is an important key to understand and investigate coastal ecosystems. The major prerequisite is a precise record of ship movements and related pollutant emissions. In our contribution we present an intercomparison between different ship emission data models for the North and Baltic Sea region. That is the inventory of the Bundesamt für Schiffahrt und Hydrography (EMMA) and the inventory of the HZG (HiMEMO-Ship, Aulinger 2016) are compared against a reference inventory from the Finnish Meteorological institute (STEAM, Jalkanen 2012). The HiMEMO-Ship is a highly flexible tool under ongoing development and allows for temporally and spatially highly-resolved ship emission data (>=30min and >=500 m) of 9 chemical species including aerosols. The tool is designed to consider also adaptation scenarios (e.g. MARPOL Annex VI regulation).
The uncertainty of the derived emissions are discussed on the basis of two means: a) a multi-parameter ensemble generated with the HZG-model and b) a multi-model ensemble using the 3 afore-mentioned approaches (“EMMA”, ”STEAM” and “HiMEMO-Ship”). The results imply that a large portion of emissions are related to ships with actually only insufficiently known characteristics, which thus cause a large range of uncertainty regarding their emission factors. Moreover, a large spread for mean NOx emissions is detected between inventories for the North Sea region. Because of complex manoeuvers and machine handling in the busy port areas, we also observe significant differences in emissions in that regions. Finally, a strategy is presented for treating the afore-mentioned issues with ship emission data in the framework of atmospheric chemistry transport modelling, i.e. deposition of pollutants
from the air.

How to cite: Petrik, R., Deichnik, K., Schwarzkopf, D., Matthias, V., and Aulinger, A.: Intercomparison of ship emission data models for the North and Baltic Sea region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21786, https://doi.org/10.5194/egusphere-egu2020-21786, 2020.

Accurate modeling of ship emissions is a topic of increasing interest due to the ever-growing global fleet and its emission of air pollutants. With the increasing calculation power of modern computers, numerical grid models can nowadays be used to analyze effects of shipping emissions from global to local scales. However, modeling entire ports and larger domains still requires a good representation for the vertical concentration profile of single ship plumes. As the shape of the plume strongly varies depending on parameters like plume temperature, ship-induced turbulence and meteorological conditions, the plume dilution does not always appear to be represented by a simple Gaussian distribution. In this work, the microscale model MITRAS is used to calculate vertical concentration profiles of ship plumes under varying technical and meteorological scenarios. The resulting curves are fitted with different mathematical curves (e.g. Gaussian, Polynomial and Gamma distribution) by a least square minimization approach and the best representations for individual scenarios are discussed.

How to cite: Badeke, R., Matthias, V., Grawe, D., and Schlünzen, H.: Characterizing the vertical concentration profiles of ship plumes with a microscale model - is it all Gaussian?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2905, https://doi.org/10.5194/egusphere-egu2020-2905, 2020.