Modelling health-relevant source-receptor relationships for fine particles

5.3.1 Health-relevant metrics of air quality

The above approach quantifies changes in life expectancy as a function of changes in population exposure to fine particles. For their analysis the underlying epidemiological cohort studies employed annual mean concentrations of fine particles (PM2.5) measured at fixed monitoring sites representative

for urban background air sheds. To maintain formal consistency with the evidentiary studies from which relative risk rates are derived, an integrated assessment model also needs to connect mortality changes with the same air quality indicators. Thus, as long as the RAINS model relies on these evidentiary cohort studies, it needs to base its impacts estimates on annual mean concentrations of fine particles (PM2.5) at background stations that are representative for the urban and rural population in Europe.

This consistency concern dictates the required output from atmospheric dispersion calculations in RAINS that provide the response of health-relevant air quality metrics towards changes in precursor emissions. Since epidemiological studies only provide differences in health impacts through comparisons of more polluted sites with cleaner locations, the emphasis of dispersion modelling needs to be more on reflecting these differences (e.g., due to differences in anthropogenic emissions) than in reproducing absolute PM levels.

5.3.2 The EMEP Eulerian model

Traditionally, RAINS calculations of the atmospheric dispersion of pollutants have been based on the EMEP model as a full-fledged atmospheric model. However, only recently, the new EMEP Eulerian model has become available, which allows for the first time to consider changes in fine particulate matter concentrations resulting from changes in anthropogenic emissions.

The peer review of the EMEP model identified a range of strengths and weaknesses that need to be considered when constructing source-receptor relationships for the integrated assessment of fine particulate matter. In particular, the review (TFMM, 2003) acknowledged that

• “an excellent start had been made within EMEP to begin the challenging task of modelling the fate and behaviour of particulate matter across Europe by initially focusing on the mass fraction of particular components within PM10 and PM2.5.

• There was high confidence in the model’s ability to represent the broad spatial pattern of particulate sulphate across Europe, its trend and the role played by its long-range transport in providing the regional background levels required as an input to urban health impact studies.

• However, there are insufficient particulate nitrate and ammonium measurements available to provide an adequate test of the performance of the EMEP model.

• Confidence in the understanding of the mechanism of formation of secondary organic aerosols and of the quantification of some natural aerosol sources was so low that they had not been included in the EMEP model, leading to underestimations for PM10 and PM2.5;

• Understanding was growing steadily in the quantification of primary particle emissions and this would lead eventually to increased confidence in the estimation of regional levels of primary emitted particles, thereby improving the assessment of PM10;

• Whilst there remained a significant fraction by mass unaccounted for between the model and observed PM10 and PM2.5 levels, there was limited confidence in the model’s ability to assess levels of PM10 and PM2.5.”

These findings have several implications for the use of this model within an integrated assessment framework:

• At the moment, the scientific peers do not consider the modelling of total particulate mass of the EMEP model (and of all other state-of-the-art models) as accurate and robust enough for policy analysis. Thus, one should not base an integrated assessment on estimates of total PM mass concentrations.

• The largest deficiencies have been identified in the quantification of the contribution of natural sources (e.g., mineral dust, organic carbon, etc.).

• The quantification of secondary organic aerosols (SOA) is not considered mature enough to base policy analysis on. A certain fraction of SOA is definitely caused by anthropogenic emissions, but some estimates suggest that the contribution from natural sources dominates total SOA. Clarification of this question is urgent to judge whether the inability of contemporary atmospheric chemistry models to quantify SOA is a serious deficiency for modelling the anthropogenic fraction of total PM mass.

• In contrast, the modelling of secondary inorganic aerosols is considered reliable within the usual uncertainty ranges. This applies especially to sulphur aerosols. The lack of formal validation of the nitrate calculations is explained by insufficient monitoring data with known accuracy; the model performs reasonably well for other nitrogen-related compounds.

• The validation of calculations for primary particles is hampered by insufficient observational data. Primary particles comprise a variety of chemical species, some of which (e.g., organic aerosols) originate from secondary particle formation too. Work at EMEP is underway to use improved emission inventories of black carbon, which are themselves only in a research phase, to use black carbon monitoring data as a tracer for emissions of primary particles. In principle, however, modelling of the dispersion of non-reactive substances like primary particles is generally considered as a not too ambitious undertaking. Thus, with some further evidence from EMEP/MSC-W on the performance of the Eulerian model for black carbon, an integrated assessment could rely on EMEP’s dispersion calculations for primary particles over Europe.

• Thus, there are arguments that the present modelling capabilities allow quantification of the dispersion of (most of) the fine particles of anthropogenic origin. This would permit calculating changes in PM concentrations over Europe due to changes in anthropogenic emissions, and to estimate the health impacts that can be attributed to anthropogenic emission controls. On the other hand, it would not be possible to make any statements on the absolute level of PM mass concentrations, and subsequently not on the absolute health impacts of the total particle burden in the atmosphere. This limitation, however, does not seem to impose unbalanced restrictions on the overall analysis, since also the evidence from the available epidemiological studies does not allow drawing conclusions about the total health impacts.

The RAINS approach as outlined in Section 5.2 acknowledges this by focusing on the differences in health effects in comparison with a baseline (reference) situation.

5.3.3 Source-receptor relationships for fine particulate matter

To quantify the health benefits of emission reductions, the RAINS integrated assessment model requires source-receptor relationships that describe changes in ambient levels of fine particles due to changes in the various anthropogenic precursor emissions from the various sources. The available concentration-response curves from the epidemiological studies relate differences in annual mean concentration of total PM2.5 mass with observed changes in mortality. With the focus on changes in anthropogenic emissions, source-receptor relationships should describe the changed contributions of primary particles, secondary inorganic particles and secondary organic particles to total PM2.5 mass due to changes in the emissions of SO2, NOx, VOC, NH3 and primary PM.

With the caveats discussed above, the full-scale EMEP Eulerian atmospheric dispersion model is able to calculate these changes through respective “model experiments” (model runs with changed emissions). However, the computational complexity of the full EMEP model makes it impractical to operate the full EMEP model within the RAINS integrated assessment model, and makes it impossible to implement the optimisation approach, for which the “backwards” dispersion calculation is required. Thus, an attempt is made to identify appropriate “reduced form” source-receptor relationships that mimic the response of the full EMEP model in a computationally simple enough representation.

In their simplest form, reduced-form source-receptor relationships are linear, which allow simple matrix operations to compute air quality impacts from a given set of emission reductions or, conversely, to identify cost-effective control strategies that meet a set of air quality targets. In reality, physical and chemical processes are often rather complex and can in most cases only be fully represented by more complex, often non-linear, formulations. For the optimisation in an integrated assessment it is therefore of prime interest to understand to what extent such complex processes could be described by linearisations, which errors would be introduced by such linearisations and, if a linear description proves inadequate, which non-linear mathematical formulation could be developed that still allows efficient computation with the integrated assessment model.

For this purpose, a number of model experiments with the new EMEP Eulerian model have been designed that explore the response of computed PM2.5 concentrations towards changes in the various precursor emissions. An initial round of model experiments with 87 model runs has been completed in January 2004, which now provides a first basis for the analysis of potential source-receptor relationships (Table 5.3). While it is interesting in itself to explore potential non-linearities in air quality responses to emission changes and to relate them to specific chemical processes, the analysis for the integrated assessment is driven by the need to make the mathematical model formulation as simple as possible, but not too simple. Thus, the analysis was limited to a well-defined range of emissions, corresponding to the practical scope of the policy analysis on further emission controls after the year 2010. Thus, the expected emissions from the baseline development in 2010, in which current air quality legislation will be implemented (the Current Legislation CLE scenario), mark the upper limit for the emission range. It is not envisaged that with the current legislation (e.g., the Emission Ceilings Directive) emissions would increase beyond these ceilings. The lower end is in principle determined by the “Maximum Technically Feasible Reduction” (MFR) scenario, in which all available control measures are introduced into the market following the natural renewal of the emission sources. To widen the scope of the analysis, an “Ultimately Feasible Reduction” scenario was considered, which ignores this penetration constraint and assumes full application of emission

control measures at all sources. IIASA provided the quantified emission estimates for these scenarios, and MSC-W has produced the experiments with the EMEP Eulerian model.

The analysis explores the response towards emission changes of the various precursor emissions individually and collectively, for all of Europe and for individual countries, at different levels of pan-European emissions. Due to their emission densities, Germany, the Netherlands, UK and Italy have been selected as focal areas for detecting potential non-linearities.

Table 5.3: Overview of EMEP model experiments to explore non-linearities in source-receptor relationships. The following acronyms are used for the emission fields: CLE: Current legislation 2010, MFR: Maximum feasible reductions in 2010, UFR: Ultimately feasible reductions in 2020

Changes in emissions