basic approach to mapping different sources, and the sources of spatial datasets

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Basic Approach to Mapping Different Sources, and the Sources of Spatial Datasets

John van Aardenne jva@eea-europa.eu

European Environment Agency

1.Introduction2.Reporting requirements3. Wake up quiz4. Post-processing of national inventory data5. Gridding: concepts and datasets6. Gridding: how does it work in practice7. Visualization

Outline

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This presentation is aimed at providing a basic overview for those new

or relatively new to emission gridding.

Disclaimer:Your presenter is not a GIS expert, nor a programmer, but with common sense, database knowledge and nice GIS colleagues

managed to work on spatially resolved emission inventories starting from simple scaling emissions with population (Moguntia model Nox emissions), to EDGAR-HYDE AP and GHG emissions (1x1

degree), Historical AP and GHG emissions for IPCC AR5 (0.5 degree) and EDGARv4 (0.1 degree).

So there is hope.....

1. Introduction

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Extendedd 50 x 50 km2 grid

2. Reporting requirements: EMEP grid

Number of grid cells: ~21000Size of grid cell at 40°N (Italy): 40x40 km2

at 60°N (Scandinavia): 50x50 km2

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2. Reporting requirements: EMEP grid

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2. Reporting requirements: sectors

A. Public powerB. Industrial comb. plantsC. Small combustion plantsD. Industrial processE. Fugitive emissionsF. SolventsG. Road – railH. ShippingI. Off road mobileJ. Civil aviation (domestic lto)K. Civil aviation (domest cruise)

L. Other waste displacementM. WastewaterN. Waste incinerationO. Agricultural livestockP. Agriculture (other)Q. Agricultural wastesR. OtherS. NaturalT. International aviation (cruise)z. Memo

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wake up quiz

Imagine the emep grid........

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3a. The following grid cells represent……

1. Hungary2. Austria3. Latvia

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3b. The following grid cells represent……

1. Malta2. Liechtenstein3. Luxembourg

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3c. The following grid cells represent……

1. Belgium2. The Netherlands3. Turkey

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4. Post-processing of emission inventory data: Emission inventories as annual total by sector are not sufficient to allow for atmospheric chemistry modeling

Seinfeld, J.H. and Pandis, S.N., Atmospheric chemistry and physics: from air pollution to climate change, Wiley and Sons, New York, 969-971, 1998.

The EMEP unified model has 20 height layers (www.emep.int)

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Horizontal allocation: assigning emissions to their proper grid

cell using gridded data on spatial surrogates with known geographic

distributions

Vertical allocation: assigning emissions to their proper layer in the atmosphere. Often static vertical distribution factors are applied to the emissions of each sector or all emissions are put

into the lowest layer.

Temporal allocation: representing emissions variation over time

(closure of facilities for maintenance, rush hour, weekends, public holidays)

Source: US EPA emissions modeling clearinghouse, Bieser et al., 2011.

4. Post-processing of emission inventory data: 3 activities are needed.

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5. Gridding: conceptual

Point source: an emission source at a known location such as an industrial plant or a power station. (could be an LPS, or not, depending on threshold)

Area source: sources that are too numerous or small to be individually identified as point sources orfrom which emissions arise over a large area (agricultural fields, residential areas, forests)

Line source: source that exhibits a line type of geography, e.g. a road, railway, pipeline or shipping lane

The sum of all different types of emissions in your domain

The grid cells representing the geographic domain for which you have emissions data.

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5. Gridding: in formula

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5. Gridding: conceptual

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5. Gridding: the “trick” is to find spatial proxies to allocate emissions to a specific grid. You will see several examples today, here results from a recent publication. Bieser et al. 2011 SMOKE for EUROPE

Sector Proxy 1 Proxy 2 Proxy 3 Proxy 4Combustion in energy and transformation industries

E-PRTR CORINE land cover (CLC; commercial and industrial units)

Global land cover database(urban area)

Population (GWPv3)

Non-industrial combustion

Population (GWPv3)

- - -

Road transport

TREMOVE Open street maps and Digital chart of the world (motorways, roads)

CORINE land cover (urban area)

Global land cover (urban area)

Agriculture CLC (agricultural areas, pasture)

GLC (agricultural areas)

EUROSTAT (animal stocks, employees agriculture)

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5. Gridding: recently released high resolution dataset (100m) Gallego F.J., 2010, A population density grid of the European Union,Population and Environment. 31: 460-473.

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5. Gridding: population density with coverage also for non-EU countries and split in urban and rural can be found at: http://sedac.ciesin.columbia.edu/gpw/

NationalBoundaries and GPWv3 2005 Pop Density

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5. Gridding: example CORINE land cover by NUTS unit (http://dataservice.eea.europa.eu/PivotApp/pivot.aspx?pivotid=501)

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1.Define “your grid” cells

2. Define the different spatial allocation proxies

3.Calculate the fraction of spatial proxy in each grid cell

4. Separate point source emission information from national total and allocate remaining emissions by sector with spatial proxy

5. Saving time: combine sources with same spatial proxy

6. Gridding: how does it work in practice

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6.1 Define “your” grid cells. An example from the world on 0.5 grid showing country boundaries based on GWP data

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6.1 Define “your” grid cells. What you are seeing is this file table with grid locations (lon-lat) and definition of countries.

This plot is nothing more than a x and y coordinate to identify the grid cell and the corresponding value telling which country is found in the grid cell.

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6.2 Define the different spatial allocation proxies

See chapter 7 of the Guidebook, Appendix A Sectoral guidance for spatial emissions distribution.

- apply those proxies that are associated with the emissions - but also ensure to identify proxy data that would give strange

results (e.g. large fraction of wood combustion in London or Paris, when using population as proxy)

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6. 3 Calculate the fraction of spatial proxy in each grid cell

Grid=id

Emep(i)

Emep(j)

A B C D E

1 66 39 0.8 12 67 39 0.1 03 66 40 0.1 0

With for e.g. A: Population: B. Urban population, C. Road network (%km, or using traffic count data)

Number of grid cells (EMEP domain):Current grid: 210000.5x0.5: 250000.1x0.1: 624000

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6. 3 Calculate the fraction of spatial proxy in each grid cell

Gridding: if spatial proxy data are available in the required grid resolution you can start using excel on course resolution grids but same principle can be build in a database environment

If the spatial dataset (e.g. population, traffic density) has to be build from the original datasource, this part is of course less straightforward (other presentation will confirm this).

For non-standard datasets and high resolution grids, you need GIS support (software and staff)

For TFEIP, can standard datasets on proxies be made available if other projects have already done the work (e.g. E-PRTR diffuse emissions, FP research project, etc.)?

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1.Define “your grid” cells

2. Define the different spatial allocation proxies

3.Calculate the fraction of spatial proxy in each grid cell

4. Separate point source emission information from national total and allocate remaining emissions by sector with spatial proxy

5. Saving time: combine sources with same spatial proxy

6. Gridding: how does it work in practice

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7. Gridding visualization: you will see many examples in the following presentations, ask what software they are using

Nox emissions from surface fuel combustion (Dignon 1992), Image courtesy.Schumann, U., A. Chlond, A. Ebel, B. Kärcher, H. Pak, H. Schlager, A. Schmitt, P. Wendling (Eds.): Pollutants from air traffic - Results of atmospheric research 1992-1997. DLR-Mitteilung 97-04, 291 pp. DLR, Köln, Germany, 1997.

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8. Do we have more certainty if we go for higher resolution emission inventories? Some thoughts.....

Seinfeld, J.H. and Pandis, S.N., Atmospheric chemistry and physics: from air pollution to climate change, Wiley and Sons, New York, 969-971, 1998.

We are getting access to datasets with ever increasing spatial resolution (e.g. 100 mtr population, exact location of point sources),

Inventories: more work, nicer pictures, more (un)certainty?

Model results: better match with observation, better understanding of chemistry, more aggregation of emissions due to mismatch model resolution?