Improvements of gap-filled and gridded data for modelers

Fourth joint session of the Steering Body to EMEP and the Working Group on Effects

10-14 September 2018, Geneva

Robert Wankmüller, Melanie Tista, Katarina Mareckova

Grid and LPS reporting in 2017 and 2018• 30 countries reported

sectoral griddedemissions in 0.1°x0.1°long-lat resolution in 2017 and/or 2018

• 40 countries reportedsectoral LPS emissionsin 2017 and/or 2018

Main HMs, POPs

Main

PM

HMs

POPs

2018 Gap-filling and improvements• Gap-filling 2018:

• Main pollutants and PMs: Time series 2000 – 2016• HMs and POPs: 2016

• 62 % of the data entries (NT, GNFR) have been reported,whereof 4 % have been replaced

• 38 % of the data entries have been gap-filled (expert estimates)

• New tool: simplify comparisons of emission data with other countries, expert data and previously reported and gap-filled data (National Totals, sector data, and data in relation to population and GDP data)

• In case of several estimates to fill a gap, a method to detect the best estimate was applied by using plausible reported data of other countries in comparison with population and GDP data

• Additional data sources and advanced calculations

2018 Gap-filling and improvements

Example: Malta NOx

2018 Gap-filling and improvements

Example: Mercury

Gridding: improvements and changes in 2018

• Gridding of

• the whole timeseries from 2000 to 2016 for all main pollutants(NOx, NMVOC, SOx, NH3, CO) and PM (PM2.5, PM10, PMcoarse) in 0.1°x 0.1° resolution on GNFR sector level

• heavy metals (Cd, Hg, Pb) and POPs (PCDD/PCDF, BaP, BbF, BkF, IP, HCB) for 2016 in 0.1° x 0.1° resolution on GNFR sector level

• Update of the proxy datasets based on EDGAR for theemission distribution in countries/areas with no grid reporting(from EDGAR 4.2 to EDGAR 4.3.1)

• Update of the ruleset for automatized base grid allocation

Example of gridded data: NOx

Gridding: Shipping proxies based on FMI data for 2015

• Global shipping emissions from FMI (Finish Meteorological Institute) for the year 2015 (and also for 2011 in case of NOx and SOx in Baltic and North Sea)

• Based on AIS (Automatic Identification System) tracking data• For the year 2016 a copy of the FMI emission values for 2015 was used• For historical shipping emissions (2000 to 2014) the FMI data was adjusted

regarding trends from data developed within the EU Horizon2020 project MACC-III and the ICCT Report (Olmer et al. 2017)

Gridding: Estimates for areas with no reporting obligations

In areas with no reporting obligations, emissions were estimated by adding up theemission values of the corresponding grid cells from EDGAR 4.3.1.

Areas with no reporting obligations:

• North Afrika

• The Russian Federation in the extended EMEP domain

• Turkmenistan

• Tajikistan

• Uzbekistan

• Remaining Asian Areas in the former official EMEP domain

• Remaining Asian Areas in the extended EMEP domain

Gap-filling and gridding: Plans for2018/2019• Gap-filling: Further data sources for comparison, as available

• Comparison with gridded emission data from the CAMS project to further improve distribution by focusing on identified differences

• Use CAMS data to update and improve the proxy datasets

• Upload the current EMEP gridded emission data to ECCAD(http://eccad.aeris-data.fr) to be able to analyse it with theprovided tools and to compare it with other gridded datasets.

• Further improvements of the gridding system

Documentationwww.ceip.at

Thank you