Exposure of ecosystems to acidification, eutrophication and ozone (version 1) (CSI 005) - Assessment published Nov 2005

Indicator definition

The indicator shows the ecosystem or crops areas at risk of exposure to harmful effects of acidification, eutrophication and ozone as a consequence of air pollution, and shows the state of change in acidification, eutrophication and ozone levels of the European environment. The risk is estimated by reference to the 'critical load' for acidification and eutrophication and 'critical level' for ozone for each location, this being a quantitative estimate of the exposure to these pollutants below which significant and harmful such effects do not occur in the long term at present knowledge.

Two critical loads, for acidity and for nutrient nitrogen, are employed to describe exposure to acidification and to eutrophication respectively. The area over which the deposition of acidifying and eutrophying pollutants is in exceedance of critical loads provides an indication of the ecosystem area in which such damage could occur. The magnitude of the potential risk is displayed as the percentage of total ecosystem areas exposed to exceedence of these critical loads.  By showing the change in risk over time, the state of change in acidification and eutrophication is displayed. By including the risk to be met within a legislative target and year the distance from this target is displayed.

The fraction of agricultural crops  that is potentially exposed to ambient air concentrations of ozone in excess of the EU target value set for the protection of vegetation is also shown.

Units

• Regions at risk: % of total ecosystem area
• Critical loads/threshold, depositions, exceedance: acidifying equivalents (H⁺) per hectare per year (eq H⁺.ha^-1.a^-1).
• Change over time: % of change compared to base year.
• Percentage of the arable land in Europe potentially exposed to ambient air concentrations of ozone (O₃) in excess of the EU target value set for the protection of vegetation.
  

Policy context and targets

Context description

No context has been specified

Targets

No targets have been specified

Related policy documents

No related policy documents have been specified

Methodology

Methodology for indicator calculation

Acidification and eutrofication

Air emission data is reported annually by national authorities to UNECE/EMEP and to EU. Reported data includes both newest estimates (two years in arrears) and updates of emissions from previous years. Emission data is stored and verified at EMEP/MSC-W.

Using these emissions, EMEP/MSC-W calculates atmospheric transport of sulphur and nitrogen pollutants using the EMEP Unified Model at a spatial resolution of 50km and according to modelled meteorological conditions adjusted towards observations.

The Co-ordination Centre for Effects uses the resulting deposition estimates to calculate exceedances over reported critical loads for acidity and nutrient nitrogen. In 2004 the CCE updated this database with national updates of critical loads (see section on gap filling where countries did not provide data). These updated estimates have been used for the calculations for 1980, 2000, 2010, and 2020.

Nitrogen and sulphur deposition in each model grid-cell are used for calculation of the average accumulated exceedances of the critical loads, that is the area-weighted average of exceedances accumulated over all ecosystem points in an EMEP gridcell. The total area of ecosystems exposed to exceedances in a country is expressed as a percentage of the total country area. These areas are summed to provide two estimates, one for the EU25 States, and for one for a larger region comprising most countries Party to the Convention on Long-range transboundary air pollution.

Ozone

According to the definition in the ozone directive, AOT40 values are calculated from hourly data measured between 08.00 and 20.00 CET at Airbase rural background stations. For crops AOT40 is accumulated during the three month summer period (May - July). Only data series with more than 75% valid data were considered. The AOT40 value measured at a background station is assumed to be representative for an area within 100 km from the station. Interpolation is done with the use of kriging. Kriging is a method of spatial statistics (see e.g., N. Cressie, Statistics for spatial data, New York, 1993) which makes use of spatial autocorrelation ( the statistical relationship between the monitoring points expressed in the form of variograms). In the case of AOT40 ordinary co-kriging is performed where the altitude is included as additional variable because there is a statistical dependency between AOT40 and altitude.

Kriging results have been overlayed in a GIS with the HBEU_LC land cover database, which was delivered with CLC90. It includes the UK data and some troubles at the time of the GIS processing with the spatial reference used for CLC90 was avoided that way. The raster resolution of HBEU_LC is 1000 x 1000 meters. The class no. 2 "Strongly artificial vegetated areas" was used as the the land cover type to estimate the crops exposed to ozone.

The temporal trends have been estimated using a Mann-Kendal test.

Methodology for gap filling

Acidification and eutrophication

Older national submissions are used where available, and for European countries which have never submitted national totals the CCE uses its European background critical load database (Hettelingh et al, 2004).

Methodology references

• Critical Loads and Dynamic Modelling Results Hettelingh,J.-P., Slootweg,J., Posch,M.  Bilthoven, National Institute for Public Health and the Environment RIVM Report 259101014/2004.(CCE Progress Report 2004)
• Inventory Review 2004 - Emission data reported to CLRTAP and under the NEC Directive EMEP/EEA joint review report. Vestreng,V., Adams,M., Goodwin,J. (2004)  Norwegian Meteorological Institute. ( Technical Report MSC-W 1/2004 )
• Emission reporting instructions for 2005, including templates and QA/QC tool.
• Manual on Methodologies and Criteria for Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends Mapping Manual 2004, UNECE Convention on Long-range Transboundary Air Pollution, ICP Modelling and Mapping.
• Statistics for spatial data N. Cressie, Statistics for spatial data, Wiley, New York, 1993 Kriging weights the surrounding measured values to derive an interpolation for each location. The weights are based (i) on the distance between the measured points and the interpolated point, (ii) on the overall spatial arrangement among the measured points. The type of kriging at its parameters (in particular the parameters describing the semivariogram) are chosen in order to minimize the RMS error.
• European exchange of air quality monitoring meta information in 2002 ETC/ACC Technical Paper 2004/1

Uncertainties

Methodology uncertainty

Acidification and eutrophication

The estimate of the deposition of acidifying and eutrophying pollutants is a calculation directly dependent on reported emissions. Monitored depositions are not employed for any other reasons than comparison against the EMEP model. Thus, the exceedance of deposition over critical loads presented in this indicator is itself a calculation derived from reported air emissions. As negotiation of emission reduction agreements has been based on similar model calculations, reporting of emission reductions in accordance with those agreements would be expected to indicate the improvement in environmental quality required by policy objectives. Model estimates of pollutant depositions are used rather than observed depositions on account of their higher spatial resolution.

Ozone

The air quality data is officially submitted. It is assumed that the AQ data has been validated by the national data supplier. Methodology uncertainty is given by uncertainty in mapping AOT40 based on interpolation of point measurements at background stations. Station characteristics and representativeness is often insufficiently documented which may implies that stations have been included which are not representative for background conditions.

Data sets uncertainty

Acidification and eutrophication

There are uncertainties behind the modelled estimates of air pollutant supply and the estimated critical loads of ecosystems across Europe.

Computer modelling uses officially reported national pollutant emission totals and their geographical distributions using documented procedures. Temporal and spatial coverage is imperfect, however, as a number of annual national totals and geographical distributions are not reported according to time schedules. These are estimated as necessary using expert opinion by the Meteorological Synthesising Centre - West of EMEP (MSCW) and by the International Institute for Applied Systems Analysis (IIASA). The resolution of the computer estimates has improved recently to 50km grid averages. Local pollutant sources or geographical features below this scale will not be well resolved. The meteorological parameters used for modelling pollutant supply are largely computations, with some adjustment towards observed conditions.

The critical load estimates are reported by official national sources, but face difficulties of geographical coverage and comparability. The latest reporting round in 2004 supplied estimates for 16 of the 38 European Environment Agency countries. For a further nine countries earlier submissions were reported as still valid. Those reporting did so for a variety of ecosystem classes, although reported ecosystems typically covered less than 50% of their total country area. For other countries the most recently submitted critical loads data is used. Variations in approach between the 25 reporting countries can lead to double counting of land area. Not all countries apply the steady state mass balance method to compute critical loads, and not all countries map the same ecosystems.  Norway mapped both forest soils and catchment areas which lead to a 119% coverage of its country surface. In the assessment of different endpoints it is possible that ecosystem areas are counted more than once. The ecosystem types considered is a national decision, and hence there is some variation country to country in the detail of subdivision used from the European Nature Information System (EUNIS) categorisation. All reporting countries include forest, but not all include waters and other vegetation types. For the countries which have never submitted data, critical loads were derived using a European background database recognized by the LRTAP Convention (CCE-Progress report 2003; Posch M, Reinds GJ, Slootweg J, The European Background database, in: Posch et al. 2003, pp.37-44)

Ozone

Geographical coverage: EEA31 countries although not the whole geographical are is covered: No data available (either because no ozone data from rural background stations is available or the country is not included in the land cover map)  for Greece, Iceland, Norway, Sweden, Bulgaria, Cyprus, Estonia, Lithuania, Latvia, Malta, Romania, Slovenia; Switzerland is included.

Strength and weakness (at data level): Most data have been officially submitted to the Commission under the Exchange of Information Decision and/or to EMEP under the UN-ECE CLRTAP. Station characteristics and representativeness is often not well documented and coverage of territory and in time is incomplete. The different definition of AOT40-values (accumulation during 8.00 to 20.00 CET following the Ozone Directive versus accumulation during daylight hours following the definition in NECD) is expected to introduce minor inconsistencies in the data set. The indicator as chosen provides information on the area for which monitoring information is available. Yearly changes in monitoring density will influence the total monitored area. The indicator is subject to year-to-year fluctuations as it is mainly sensitive to episodic conditions, and these depend on particular meteorological situations, the occurrence of which varies from year to year.

Reliability, accuracy, robustness, uncertainty (at data level): In spite of a generally reasonable level of accuracy and precision of ozone measurements, the indicator is rather sensitive to the precision at the reference level (40 ppb or 80 microgram/m³), and to the accuracy of measured ozone levels. Moreover, the number of available data series varies considerably from year to year and for some years is very low. The indicator is subject to large year-to-year variations due to meteorological variability. For instance, the relatively favourable values for 1998 are largely due to unfavourable condition for ozone formation (in other words: "1998 was a bad summer"). When averaging over Europe this meteorologically induced variation may be less provided spatial data coverage is sufficient.

Rationale uncertainty

No uncertainty has been specified