Annual source-receptor information for major …...CAMS_50 regional production, however with a lower horizontal resolution, 0.2x0.125 degrees, providing a reference run for the SR

ECMWF COPERNICUS NOTE

Annual source-receptor information for major European cities – 2019

Issued by: Met Norway / Michael Schulz, Augustin Mortier, Svetlana Tsyro Date: 06/07/2020 Ref: CAMS71_2019SC1_D3.1.5-2020_202007_AnnualSR2019_v2

Copernicus Atmosphere Monitoring Service

Contributors MetNo-CAMS71: Michael Schulz, Augustin Mortier, Svetlana Tsyro, Anna Benedictow, Alvaro Benedito, Hilde Fagerli TNO-CAMS71: Richard Kranenburg, Martijn Schaap INERIS-CAMS71: Laurence Rouil

CAMS71_2019SC1 D3.1.5 - Schulz et al. Page 2 of 16


Summary The annual report of source receptor (SR) analysis for major European city areas provides insights in the origin of PM10 concentrations in these areas. It builds upon the routine operations of the source- receptor calculations in the CAMS_71 service. For each of the 38 cities covered by the service, the results from daily operations are averaged over seasonal (DJF, MAM, JJA, SON) and annual timescales. This allows to show the local/non-local/natural sources, PM10 geographical origin, and the chemical speciation of PM10. This annual SR report covers 2019 and complements other annual reports on air quality published in other scientific and policy frameworks such as the air quality report from the European Environment Agency, the EMEP annual report established for the Convention on Long Range Transboundary air Pollution and the CAMS_71 interim annual assessment report. The full data and its visualization is available on dedicated CAMS_71 web pages, which contain all the information for all the cities. This report gathers exemplary findings and explains how to interpret results from the web pages. It is important to be reminded that this service is based on two chemistry-transport models ; EMEP developed and run by Met Norway (NO) and LOTOS-EUROS developed and run by TNO (NL). Equivalent results from the TNO and EMEP SR model results are displayed on the web pages, allowing the user to inspect the robustness of the SR analysis developed within CAMS policy applications, since the two models use independent and different methods to compute source-receptor attributions. The results show considerable long range transport affecting most of European large city areas during air pollution episodes. Long range transported PM10 from the domestic country and neighbouring countries outside of the city area contribute 10-20 µg/m3 PM10 to average European city air quality. On days with highest concentrations this can rise to 30-50 µg/m3, contributing thus to exceedances of the PM10 daily limit value, 50 µg/m3, according to the EU air quality Directive 2008/50/EC occurring in the city area. Ammonium nitrate has become in many cities a large part of PM10, in particular in pollution episodes.



Table of content

Summary 3

1 Methodology SR model simulations 4

1.1 Setup for model simulations 4

1.2 Setup for source-receptor analysis 5

2 Web interface to annual SR results 5

3 Results 6

3.1 Exemplary SR analysis for Brussels 6

3.2 SR approach consistency between EMEP and LOTOS-EUROS 7

References 8

ANNEX 10

EMEP model description 10

Lotos Euros model description 12

1 Methodology SR model simulations

1.1 Setup for model simulations The EMEP and LOTOS-EUROS models have been operated with the same setup as the operational CAMS_50 regional production, however with a lower horizontal resolution, 0.2x0.125 degrees, providing a reference run for the SR analysis. The modelling domain covers the greater Europe at 20km resolution. The meteorological and chemical boundary conditions are obtained from the IFS model of ECMWF. Daily 4 day forecasts have been made every day, initialised from the model fields from yesterday. The first day analysis from these forecasts are reassembled to a continuous time series for a given episode for the affected cities. The output was stored for all EU capitals and additional selected major cities. Hourly surface concentrations of PM10 and chemical aerosol speciation was averaged for a ‘city area’, represented by a rectangle of 3x3 cells centered on each city, which has a spatial extension of ca. 60x60 km around each city center. Results are available as in json file format and visualisation via the CAMS_71 SR web pageshttps://policy.atmosphere.copernicus.eu/DailySourceAllocation.php.


https://policy.atmosphere.copernicus.eu/DailySourceAllocation.php


1.2 Setup for source-receptor analysis

The source-receptor (SR) analysis service is based on multiple additional daily simulations with the EMEP and Lotos-EUROS models with an emission perturbation and labeling approaches respectively (see annex). It describes, for a selection of European cities (amongst which the capitals), the main source regions that contribute to PM levels in those cities. In particular, contributions of emissions in the city area versus those from outside and contributions from foreign countries are quantified. The local contribution may be underestimated because it is averaged over the city area, and because local dispersion might be too effective on the scale of the model. However, the SR analysis provides valuable and useful evaluation of the impact of long-range transport in the European cities. This contribution is very difficult to assess by local authorities although it is essential for them to know about it for defining the most efficient short term and long term air pollution control measures. T he SR analysis is averaged for the period from 1 January to 31 December 2019, in 38 large city areas in Europe.

2 Web interface to annual SR results The interactive web pageto be used along with this report is showing data from one year of daily SR analysis of PM concentrations and is hosted at: https://policy.atmosphere.copernicus.eu/YearlyStatistics.php. The functionality can be summarized as follows:

● Main menu choices allow to choose one of the 38 cities and either the EMEP or the TNO (LOTOS_EUROS) model (as year and pollutant are at this time available 2019 and PM10)

● The source regions tab opens a view to the main contributors to the annual PM10 concentrations, including those from natural sources, the domestic country, the estimated (minimum) local contribution and those from the surrounding countries. Other contributions contain all other sources and countries as well as boundary condition contributions. A pie visualizes the average annual source attribution.

○ A time series shows the daily source region split. The user can zoom into a period by marking a period of interest on the graph. The average source attribution is then shown for the period of interest

○ Seasonal source region attribution is shown for the meteorological seasons. ○ Daily concentrations are binned in 8 classes, and the source attribution is shown per

concentration class. ● The chemical species tab opens a view to chemical speciation of PM10 in a similar way.

Selected chemical species include sulfate, nitrate, ammonium, elemental carbon, primary organic matter, sea salts and dusts. Vizualisation is consistent with the source region tab with average annual pie chart completed by:

○ zoomable annual time series showing the chemical composition of PM10 on a daily basis,

○ Pie charts showing seasonal averages,


https://policy.atmosphere.copernicus.eu/YearlyStatistics.php


○ chemical speciation for the 8 concentration classes. ● The observation tab shows preliminary observation data for 2019 in the city areas from all

stations lying in the city area. A zoomable map shows where the observations are taken. u ○ The distribution of model and station data are shown on a histogram and as a table

indicating the number of days where concentrations were above 50 µg/m3 (daily limit value).

○ A time series shown allows also zoomed inspections of model versus observations.

3 Results

3.1 Exemplary SR analysis for Brussels Figure 1: upper panel) EMEP model time series of PM10 concentration in city area of Brussels. Exceedances are visible in spring time. middle panel) Seasonal averages of chemical speciation is shown, indicating a maximum of ammonium nitrate and sulfate in spring (AMJ=April May June). lower panel) Daily concentrations binned in concentration classes. Most days exhibit PM10 concentrations between 10 and 20 µg/m3. Days with higher concentrations (40-60) have more ammonium nitrate and sulfate then those with lower concentrations. Secondary aerosol formation from NOx and SOx emissions dominate the pollution episodes.



Figure 2: The annual SR analysis for Brussels is illustrated in a pie chart, indicating major sources to PM10. Large contributions of 17% come from both Belgium and France. Natural PM sources constitute 20%, mainly due to sea salt transported from the North Sea and Atlantic to the inner Belgium areas. Germany and Great Britain are for each ca 10% responsible. The local contribution from the Brussels city area to the city area itself is at least 9% on average, likely higher due to model imperfections on city area scale.

3.2 SR approach consistency between EMEP and LOTOS-EUROS The two modelling approaches can be compared for all cities analysed on the web page. Exemple in Figure 3 shows the comparison of the SR analysis calculated by both models for Paris. The domestic contribution to PM10 concentrations in the city area is in both models very consistent (38.2+20.8=59% in EMEP model and 60% in LOTOS-EUROS model). The contributions from natural sources are higher in the LOTOS-EUROS model due to larger dust conditions. Other contributions are fairly consistent, with differences of a few percent. Domestic contributions are generally consistent with a bias of up to 10% (eg Berlin 61% / 42% ; Copenhagen 18% / 20%; London 47% / 43%; Rome 46% / 32% for EMEP / Lotos-Euros respectively).

SR analysis EMEP model SR analysis LOTOS-EUROS model



Figure 3: SR contributions to annual PM10 concentrations in Paris, from surrounding countries, natural sources, and a minimum estimate of local contributions (only EMEP model). (FRA= France, GBR= Great Britain, DEU=Germany, NLD=Netherlands, BEL=Belgium, ESP=Spain, POL=Poland, ITA=Italy, CZE=Chech Republic).



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ANNEX

EMEP model description The EMEP MSC-W model is a chemical transport model developed at the Norwegian Meteorological Institute under the EMEP programme (UN Convention on Long-range Trans-boundary Air Pollution). This Eulerian model is developed to be concerned with the regional atmospheric dispersion and deposition of acidifying and eutrophying compounds (S, N), ground level ozone (O3) and particulate matter (PM2.5, PM10). The EMEP MSC-W model system allows several options with regard to the chemical schemes used and the possibility of including aerosol dynamics. Simpson et al. (2012) describes the EMEP MSC-W model in detail as well as the main model updates since 2006. The forecast version of the EMEP MSC-W model (EMEP-CWF) is in operation since June 2006. The model version at the start of CAMS-71 is the official EMEP Open Source version that was released in October 2015 at www.emep.int. On a general note, the EMEP-CWF results might differ from those presented in EMEP Status Reports 2008-2015 for the years 2006-2013 (see EMEP Status Reports in list of references) because of different input data (emissions and meteorological driver) and model run modes (Forecast versus Hindcast in EMEP Status Reports). The numerical solution of the advection terms of the continuity equation is based on the scheme of (Bott, 1989). The fourth order scheme is utilised in the horizontal directions. In the vertical direction a second order version applicable to variable grid distances is employed. The turbulent diffusion coefficients (Kz) are first calculated for the whole 3D mode domain on the basis of local Richardson numbers. The planetary boundary layer (PBL) height is then calculated using methods described in (Simpson et al., 2003a). For stable conditions, Kz values are retained. For unstable situations, new Kz values are calculated for layers below the mixing height using the O'Brien interpolation (Simpson et al., 2003a). Parameterisation of dry deposition is based on a resistance formulation, fully described in EMEP Status Report 1/2003 (Simpson et al., 2003a). The deposition module makes use of a stomatal conductance algorithm which was originally developed for ozone fluxes, but which is now


http://www.emep.int/


applied to all gaseous pollutants when stomatal control is important (Emberson et al., 2000; Simpson et al., 2003b; Tuovinen et al., 2004). Non-stomatal deposition for NH3is parameterised as a function of temperature, humidity, and the molar ratio SO2/NH3. The chemical scheme couples the sulphur and nitrogen chemistry to the photochemistry using about 140 reactions between 70 species (Andersson-Sköld and Simpson, 1999; Simpson et al. 2012). The chemical mechanism is based on the “EMEP scheme” described in Simpson et al., 2012 and references therein. Reactions to cover acidification, eutrophication and ammonium chemistry are fully documented in EMEP Status Report 1/2003 and Fagerli et al. (2004). The standard model version distinguishes two size fractions for aerosols, fine aerosol (PM2.5) and coarse aerosol (PM2.5-10). The aerosol components presently accounted for are SO4, NO3, NH4, anthropogenic primary PM and sea salt. Also aerosol water is calculated. Dry deposition parameterisation for aerosols follows standard resistance-formulations, accounting for diffusion, impaction, interception, and sedimentation. Wet scavenging is treated with simple scavenging ratios, taking into account in-cloud and sub-cloud processes. For secondary organic aerosol (SOA) the so-called EmChem09soa scheme is used, which is a somewhat simplified version of the mechanisms discussed in detail by Bergström et al. (2012). The EMEP-CWF covers the European domain [32°N-76°N] x [28°W-36°E] on a geographic projection with a horizontal resolution of 0.25° x 0.125° (longitude-latitude). Vertically the model uses 20 levels defined as sigma coordinates. The 10 lowest model levels are within the PBL, and the top of the model domain is at 100 hPa. Four-day meteorological forecasts from the IFS system of the ECMWF are retrieved daily around 18:15 UTC (12 UTC forecast) and at 06:15 UTC (00 UTC forecast). The ECMWF forecasts do not include 3D precipitation, which is needed by the EMEP-CWF model. Therefore, a 3D precipitation estimate is derived from large scale precipitation and convective precipitation (surface variables). Currently the 12 UTC forecast from yesterday’s forecast is used, so that there is sufficient time to run the EMEP-CWF well before the deadline for delivery. If available at the start of the forecast run, boundary conditions are taken from the C-IFS. Default BCs are specified for O3, CO, NO, NO2, CH4, HNO3, PAN, SO2, ISOP, C2H6, some VOCs, Sea salt, Saharan dust and SO4. Concentrations are specified by annual mean concentrations and a set of parameters for each species describing seasonal, latitudinal and vertical distributions. The Norwegian Meteorological Institute has several decades of experience in calculating country-to-country source-receptor relationships for the UN Convention on Long-Range transboundary Air Pollution (LRTAP). The contributions of one country (emitter) to air pollution and deposition in another country (receptor), integrated over one year, are listed in the annual EMEP status reports for every country pair within the EMEP region as a table. These tables answer the question of how much sulfur deposition in Country A was caused by emissions of sulfur in Country B during Year Y. Year Y is usually 2 years ago, as 2 years is the time it takes for emission data and validated observations to be ready for model simulations and evaluations within the EMEP program. Similarly to the regional & city source-receptor calculations, the experimental setup for a country source-receptor product depends on the question asked. The quick response question is about what can be done now to prevent (or mitigate) a forecasted air pollution event. For this question the model simulations have to start ‘today’ with meteorological forecast data for the next few days. The attribution question, which in most cases will be more relevant for country-to-country source-receptor calculations, answers the question as to where a forecasted air pollution episode



has originated. For this question, the model simulations need to be started from several days before the activation time, in order to account for contributions that are transported from remote countries. Emissions of several chemical species are addressed. The following simulations are done: 1: “Base”: Reference simulation, where ALL emissions are included 2: “Perturbation_XX”: Five Perturbation runs for each of the selected country-emitters (see in the end of this section), where emissions from every considered country are reduced by 15% (this simulation is done for all emitter countries of interest and for the receptor country). The perturbation runs are done for emissions of CO, SOx, NOx, NH3, NMVOC and PPM (primary particulate matter). To calculate the contribution of SOx emissions in country A to e.g. SOx deposition in country B, the SOx deposition is integrated over the area of country B in the Base simulation and in the Perturbation_SOx simulation for country A, and the difference is calculated as follows: (Base minus Perturbation_SOx for country A ) times 100/15. The factor 100/15 factor is used to scale the effect of a 15% perturbation to 100%, i.e. an assumed full contribution. The reason why emissions should not perturbed by 100% in the model simulations is to stay within the linear regime of involved chemistry. Similarly the contributions are calculated for other countries, other emissions (e.g. NOx, NH3, …) and other quantities of interest (e.g. surface PM, ozone concentrations, reduced N deposition). For computational efficiency, in the perturbation calculations all anthropogenic emissions in the perturbation run (i.e. NOx, SOx, etc.) may be reduced by 15% simultaneously. First tests will show if this is needed in the operational CAMS environment and if per component perturbations are run with a small delay. The total contributions from the various countries to the quantity of interest (e.g. ozone concentration) are then normalized (and plotted) such that their sum scaled up to 100%. When a request for contributions to air pollution in a specific country is made, the EMEP source-receptor tables are consulted to identify a shortlist of typical countries (typically 5 to 10) that contribute the most in the assumed receptor country for which the request is sent for. Perturbation runs and the analyses are then done for this shortlist of countries (country A, B, C, etc.) and all other regions combined (“other”).

Lotos Euros model description

LOTOS-EUROS is a 3D chemistry transport model. The off-line Eulerian grid model simulates air pollution concentrations in the lower troposphere solving the advection-diffusion equation on a regular lat-lon-grid with variable resolution over Europe (Schaap et al., 2008). The vertical transport and diffusion scheme accounts for atmospheric density variations in space and time and for all vertical flux components. The vertical grid is based on terrain following vertical coordinates and extends to 5 km above sea level. The model uses a dynamic mixing layer approach to determine the vertical structure, meaning that the vertical layers vary in space and time. The



layer on top of a 25 m surface layer follows the mixing layer height, which is obtained from the ECMWF meteorological input data that is used to force the model. The height of the two reservoir layers is determined by the difference between 3.5 km and the mixing layer height. Both layers are equally thick with a minimum of 50 m. When the mixing layer extends near or above 3.5 km, the top of the second reservoir layerexceeds 3.5 km according to the above-mentioned description. The top layer extends to 5 km above sea level and have a thickness of 1.5 km. The horizontal advection of pollutants is calculated applying a monotonic advection scheme developed by Walcek et al. (1998). Gas-phase chemistry is simulated using the TNO CBM-IV scheme, which is a condensed version of the original scheme (Whitten et al, 1980). Hydrolysis of N2O5 is explicitly described following Schaap et al. (2004). LOTOS-EUROS explicitly accounts for cloud chemistry computing sulphate formation as a function of cloud liquid water content and cloud droplet pH as described in Banzhaf et al. (2011). For aerosol chemistry the thermodynamic equilibrium module ISORROPIA2 is used (Fountoukis and Nenes, 2007). Dry Deposition fluxes are calculated using the resistance approach as implemented in the DEPAC (DEPosition of Acidifying Compounds) module (van Zanten et al., 2011). Furthermore, a compensation point approach for ammonia is included in the dry deposition module (Wichink Kruit et al., 2012). The wet deposition module accounts for droplet saturation following Banzhaf et al. (2013). In LOTOS-EUROS, the temporal variation of the non-agricultural emissions is represented by monthly, daily and hourly time factors that break down the annual totals for each source category. The biogenic emission routine is based on detailed information on tree species over Europe (Koeble and Seufert, 2001). The emission algorithm is described in Schaap et al. (2009) and is very similar to the simultaneously developed routine by Steinbrecher et al. (2009). Sea salt emissions are described using Martensson et al. (2003) for the fine mode and Monahan et al. (1986) for the coarse mode. Dust emissions from agricultural activities and resuspension of particles from traffic are included following Schaap et al., (2009). The model performance was subject to numerous peer-reviewed publications (recently Schaap et al., 2015) and reports. For an overview we refer to the model’s website: www.lotos-euros.nl. The LOTOS-EUROS model is applied respecting the CAMS specifications, what is also warranted through the participation to CAMS_50. Those specifications are : § The domain covered is 25°W-45°E, 30°N-70°N; resolution 0.25° by 0.125; § Meteorological input is obtained from ECMWF’s high-resolution operational meteorological forecasts § Anthropogenic emissions are taken from the TNO-MACC-III inventory for 2011. This inventory will be replaced by the CAMS_81 emission dataset once it becomes available; § fire emissions are obtained from the CAMS fire product; § chemical boundary conditions are taken from the C-IFS. Exception is the exclusion of C-IFS sea salt boundary conditions as these are shown to be way to high, negatively impacting SRC over continental Europe;



Within the FP7-project EnerGEO TNO has developed a system to track the impact of emission categories within a LOTOS-EUROS simulation based on a labelling technique (Kranenburg et al., 2013). This technique provides more accurate information about the source contributions than using a brute force approach with scenario runs as the chemical regime remains unchanged. Another important advantage is the reduction of computation costs and analysis work associated with the calculations. The source apportionment technique has been previously used to investigate the origin of particulate matter (episodes), nitrogen dioxide, nitrogen deposition Besides the concentrations of all species the contributions of a number of sources to all components are calculated. The labelling routine is only implemented for primary, inert aerosol tracers as well as chemically active tracers containing a C, N (reduced and oxidised) or S atom, as these are conserved and traceable. This technique is therefore not suitable to investigate the origin of e.g. O3 and H2O2, as they do not contain a traceable atom. The source apportionment module for LOTOS-EUROS provides a source attribution valid for current atmospheric conditions as all chemical conversions occur under the same oxidant levels. For details and validation of this source apportionment module we refer to Kranenburg et al. (2013). To avoid violating the memory size and avoid excessive computation times it was chosen to trace the countries of the EU-28, supplemented by Norway and Switzerland. The selected countries are listed in Table 1. For convenience, a small number of countries were combined. For example, Switzerland and Liechtenstein as well as Luxemburg and Belgium were combined. The same was applied to all sea areas. To be mass consistent, all non-selected anthropogenic emissions, natural emissions and as well as the combined impact of initial conditions and boundary conditions were tracked as well.


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Annual source-receptor information for major …...CAMS_50 regional production, however with a lower horizontal resolution, 0.2x0.125 degrees, providing a reference run for the SR