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 [email protected] Outline. Introduction Reporting requirements 3. Wake up quiz 4. Post-processing of national inventory data 5. Gridding: concepts and datasets

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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

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

John van Aardenne [email protected]


Basic approach to mapping different sources and the sources of spatial datasets

Outline

  • Introduction

  • Reporting requirements

    3. Wake up quiz

    4. Post-processing of national inventory data

    5. Gridding: concepts and datasets

    6. Gridding: how does it work in practice

    7. Visualization


Basic approach to mapping different sources and the sources of spatial datasets

1. Introduction

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.....


Basic approach to mapping different sources and the sources of spatial datasets

2. Reporting requirements: EMEP grid

Number of grid cells: ~21000

  • Size of grid cell

  • at 40°N (Italy): 40x40 km2

  • at 60°N (Scandinavia): 50x50 km2

Extendedd 50 x 50 km2 grid


Basic approach to mapping different sources and the sources of spatial datasets

2. Reporting requirements: EMEP grid


Basic approach to mapping different sources and the sources of spatial datasets

2. Reporting requirements: sectors

A. Public power

B. Industrial comb. plants

C. Small combustion plants

D. Industrial process

E. Fugitive emissions

F. Solvents

G. Road – rail

H. Shipping

  • Off road mobile

  • J. Civil aviation (domestic lto)

  • K. Civil aviation (domest cruise)

L. Other waste displacement

M. Wastewater

N. Waste incineration

O. Agricultural livestock

P. Agriculture (other)

Q. Agricultural wastes

R. Other

S. Natural

T. International aviation (cruise)

z. Memo


Basic approach to mapping different sources and the sources of spatial datasets

wake up quiz

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


Basic approach to mapping different sources and the sources of spatial datasets

3a. The following grid cells represent……

  • Hungary

  • Austria

  • Latvia


Basic approach to mapping different sources and the sources of spatial datasets

3b. The following grid cells represent……

  • Malta

  • Liechtenstein

  • Luxembourg


Basic approach to mapping different sources and the sources of spatial datasets

3c. The following grid cells represent……

  • Belgium

  • The Netherlands

  • Turkey


Basic approach to mapping different sources and the sources of spatial datasets

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

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

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.


Basic approach to mapping different sources and the sources of spatial datasets

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

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.


Basic approach to mapping different sources and the sources of spatial datasets

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 or

from which emissions arise over a large area (agricultural fields, residential areas, forests)

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

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


Basic approach to mapping different sources and the sources of spatial datasets

5. Gridding: in formula


Basic approach to mapping different sources and the sources of spatial datasets

5. Gridding: conceptual


Basic approach to mapping different sources and the sources of spatial datasets

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


Basic approach to mapping different sources and the sources of spatial datasets

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

.


Basic approach to mapping different sources and the sources of spatial datasets

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


Basic approach to mapping different sources and the sources of spatial datasets

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


Basic approach to mapping different sources and the sources of spatial datasets

6. Gridding: how does it work in practice

  • Define “your grid” cells

  • Define the different spatial allocation proxies

  • 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


Basic approach to mapping different sources and the sources of spatial datasets

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


Basic approach to mapping different sources and the sources of spatial datasets

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.


Basic approach to mapping different sources and the sources of spatial datasets

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)


Basic approach to mapping different sources and the sources of spatial datasets

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

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: 21000

0.5x0.5: 25000

0.1x0.1: 624000


Basic approach to mapping different sources and the sources of spatial datasets

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.)?


Basic approach to mapping different sources and the sources of spatial datasets

6. Gridding: how does it work in practice

  • Define “your grid” cells

  • Define the different spatial allocation proxies

  • 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


Basic approach to mapping different sources and the sources of spatial datasets

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.


Basic approach to mapping different sources and the sources of spatial datasets

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

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?

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.


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