Influence of Improved Methodology and Increased Spatial Resolution on Gridded Emissions

Influence of Improved Methodology and Increased Spatial Resolution on Gridded Emissions

M. S. Plejdrup O.-K. Nielsen H. G. Bruun

Department of Environmental Science, Aarhus University, Denmark

Page: 
161-173
|
DOI: 
https://doi.org/10.2495/EI-V2-N2-161-173
Received: 
N/A
|
Revised: 
N/A
|
Accepted: 
N/A
|
Available online: 
N/A
| Citation

OPEN ACCESS

Abstract: 

Spatial distribution of emissions is a key element in assessing human exposure to air pollution through the use of dispersion modelling. The quality of the spatial emission mapping is crucial for the quality, applicability and reliability of modelled air pollution levels, estimated human exposure and incurred health effects and related costs, all very important information for policymakers in decisions of implementation of environmental policies and measures. Detailed information on spatial distribution of emissions allows for a more targeted regulation, implementing measures focussing on areas where emissions are highest, allowing for more cost-effective initiatives on local, regional and national scale. The purpose of the MapEIre project, funded by Ireland’s Environmental Protection Agency, is to develop a high-resolution spatial mapping of the Irish emission inventory. The work is state-of-the-art and combines a large amount of statistical data with detailed spatial information to allow for a complete spatial emission mapping on a 1 km by 1 km resolution.

When comparing the results from the MapEIre project with those of the previous studies, the impact of both methodological refinements and higher spatial resolution becomes very visible. A low resolution, such as the 50 × 50 km used in the official reporting, causes important variations to be obfuscated and, if used for air quality modelling, would introduce significant uncertainty. Methodological simplifi- cations can also have significant influence on the results, which has been illustrated in this paper using specific examples comparing the detailed MapEIre methodology with less detailed methodologies used in the previous studies.

The results from MapEIre represent a significant improvement over previous methodologies and will be a strong input for future air quality modelling.

Keywords: 

air pollution, emission inventory, emission mapping, GeoKey, gridding, spatial emissions, spatial resolution

  References

[1] World Health Organization, 9 Out of 10 People Worldwide Breathe Polluted Air, but More Countries Are Taking Action, 2018, available at http://www.who.int/news-room/detail/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action (accessed 6 December 2018).

[2] Brandt, J., Silver, J.D., Christensen, J.H., Andersen, M.S., Bønløkke, J., Sigsgaard, T., Geels, C., Gross, A., Hansen, A.B., Hansen, K.M., Hedegaard, G.B., Kaas, E. & Frohn, L.M., Contribution from the ten major emission sectors in Europe and Denmark to the health-cost externalities of air pollution using the EVA model system – an integrated modelling approach. Atmospheric Chemistry and Physics, 13(15), pp. 7725–7746, 2013. https://doi.org/10.5194/acp-13-7725-2013.

[3] European Environment Agency (EEA), European Union Emission Inventory Report 1990–2015 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP). EEA Report No 9/2017, European Environment Agency: Kongens Nytorv 6, 1050 Copenhagen K, Denmark, 2017.

[4] EDGAR – Emissions Database for Global Atmospheric Research. European Commission, 2017, available at http://edgar.jrc.ec.europa.eu/index.php (accessed 14 February 2018).

[5] Umweltbundesamt, A High Resolution European Emission Data Base for the Year 2005, Federal Environment Agency (Umweltbundesamt): Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany, 2013.

[6] UNECE, Guidelines for Reporting Emissions and Projections Data under the Convention on Long-range Transboundary Air Pollution, ECE/EB.AIR/125, UNITED NATIONS PUBLICATION Copyright®: United Nations, 2015.

[7] Bo, Y., Cai, H. & Xie, S.D., Spatial and temporal variation of historical anthropogenic NMVOCs emission inventories in China. Atmospheric Chemistry and Physics, 8, pp. 7297–7316, 2008. https://doi.org/10.5194/acp-8-7297-2008.

[8] Sahu, S.K., Beig, G. & Parkhi, N.S., Emissions inventory of anthropogenic PM2.5 and PM10 in Delhi during Commonwealth Games 2010. Atmospheric Environment, 45, pp. 6180–6190, 2011. https://doi.org/10.1016/j.atmosenv.2011.08.014.

[9] Dalvi, M., Beig, G., Patil, U., Kaginalkar, A., Sharma, C. & Mitra, A.P., A GIS based methodology for gridding of large-scale emission inventories: application to carbonmonoxide emissions over Indian region. Atmospheric Environment, 40, pp. 2995–3007, 2006. https://doi.org/10.1016/j.atmosenv.2006.01.013.

[10] Wu, S.-P., Zhang, Y.-J., Schwab, J.J., Li, Y.-F., Liu, Y.-L. & Yuan, C.-S., High-resolution ammonia emissions inventories in Fujian, China, 2009–2015. Atmospheric Environment, 162, pp. 100–114, 2017. https://doi.org/10.1016/j.atmosenv.2017.04.027.

[11] Skjøth, C.A., Geels, C., Berge, H., Gyldenkærne, S., Fagerli, H., Ellermann, T., Frohn, L.M., Christensen, J., Hansen, K.M., Hansen, K. & Hertel, O., Spatial and temporal variations in ammonia emissions – a freely accessible model code for Europe. Atmospheric Chemistry and Physics, 11, pp. 5221–5236, 2011. https://doi.org/10.5194/acp-11-5221-2011.

[12] Hellsten, S., Dragosits, U., Place, C.J., Vieno, M., Dore, A.J., Misselbrook, T.H., Tang, Y.S. & Sutton, M.A., Modelling the spatial distribution of ammonia emissions in the UK. Environmental Pollution, 154, pp. 370–379, 2008. https://doi.org/10.1016/j.envpol. 2008.02.017.

[13] Puliafito, S.E., Allende, D., Pinto, S. & Castesana, P., High resolution inventory of GHG emissions of the road transport sector in Argentina. Atmospheric Environment, 101, pp. 303–311, 2015. https://doi.org/10.1016/j.atmosenv.2014.11.040.

[14] Plejdrup, M.S., Nielsen, O.-K. & Brandt, J., Spatial emission modelling for residential wood combustion in Denmark. Atmospheric Environment, 144, pp. 389–396, 2016. https://doi.org/10.1016/j.atmosenv.2016.09.013.

[15] Johansson, L., Jalkanen, J.-P. & Kukkonen, J., Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution. Atmospheric Environment, 167, pp. 403–415, 2017. https://doi.org/10.1016/j.atmosenv.2017.08.042.

[16] Castesana, P., Dawidowski, L., Finster, L., Gomez, D. & Taboada, M., Ammonia emissions from the agriculture sector in Argentina; 2000–2012. Atmospheric Environment, 178, pp. 293–304, 2018. https://doi.org/10.1016/j.atmosenv.2018.02.003.

[17] Guttikunda, S.K. & Calori, G., A GIS based emissions inventory at 1 km x 1 km spatial resolution for air pollution analysis in Delhi, India. Atmospheric Environment, 67, pp. 101–111, 2013. https://doi.org/10.1016/j.atmosenv.2012.10.040.

[18] Tian, Y.Q., Radke, J.D., Gong, P. & Yu, Q., Model development for spatial variation of PM2.5 emissions from residential wood burning. Atmospheric Environment, 38, pp. 833–843, 2004. https://doi.org/10.1016/j.atmosenv.2003.10.040.

[19] Qi, J., Zheng, B., Li, M., Yu, F., Chen, C., Liu, F., Zhou, X., Yuan, J., Zhang, Q. & He, K., A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China. Atmospheric Environment, 170, pp. 156–168, 2017. https://doi.org/10.1016/j.atmosenv.2017.09.039.

[20] Kannari, A., Tonooka, Y., Baba, T. & Murano, K., Development of multiple-species 1 km x 1 km resolution hourly basis emissions inventory for Japan. Atmospheric Environment, 41, pp. 3428–3439, 2007. https://doi.org/10.1016/j.atmosenv.2006.12.015.

[21] Tsilingiridis, G., Sidiropoulos, C., Pentaliotis, A., Evripidou, C., Papastavros, C., Mesimeris, T. & Papastavrou, M., A spatially allocated emissions inventory for Cyprus. Global NEST Journal, 12(434), pp. 99–107, 2010. https://doi.org/10.30955/gnj.000682.

[22] Plejdrup, M.S. & Gyldenkærne, S., Spatial distribution of emissions to air – the SPREAD model. National Environmental Research Institute, Aarhus University, Denmark, NERI Technical Report no. 823, 72 pp., 2011.

[23] Nyíri, A., Emissions for CLRTAP modelling – experience and feedback. Presented on 25 April 2018 Workshop on Verification of Emission Estimates, Sofia, 2018.

[24] FAIRMODE, available at http://fairmode.jrc.ec.europa.eu/.

[25] de Kluizenaar, Y., Aherne, J. & Farrell, E.P., Modelling the spatial distribution of SO2 and NOx emissions in Ireland. Environmental Pollution, 112, pp. 171–182, 2001. https://doi.org/10.1016/S0269-7491(00)00120-2.

[26] AEA, Mapping Methods for Gridded Data, 2012. Unpublished.

[27] Plejdrup, M.S., Nielsen, O.-K. & Bruun, H.G., Spatial high-resolution mapping of national emissions. WIT Transactions on Ecology and the Environment, Vol. 230, WIT Press: Ashurst Lodge, Ashurst Southampton SO40 7AA, UK, 2018, ISBN 978-1-78466-269-1.

[28] The AmmoniaN2K Project, available at https://www.ucd.ie/ammonian2k/ (accessed 6 December 2018).

[29] European Union, 2010, Directive 2010/75/EU of the European Parliament and of the Council of 24 November 2010 on industrial emissions (integrated pollution prevention and control).

[30] CDR, available at https://cdr.eionet.europa.eu/.

[31] Duffy, P., Hyde, B., Hanley, E. & Barry, S., Irelands Informative Inventory Report 2012, Irish Environmental Protection Agency: An Ghníomhaireacht um Chaomhnú Comhshaoil, PO Box 3000, Johnstown Castle, Co. Wexford. Ireland, 2012.

[32] Copernicus Land Portal, CORINE Land Cover, available at https://land.copernicus.eu/pan-european/corine-land-cover.