Decision support tools in air pollution policy

Information plays an important role in environmental policy making. Policy makers in the field of air pollution require access to available emission data and frequently make use of scenario type manipulations of such data to analyse air quality and to study possible changes therein in the future. In many cases they also want to assess contributions of different sources or source groups to ambient concentrations both in the present situation and as a consequence of possible future developments as modelled by the scenarios. The availability of a comprehensive modelling system, comprising emission data, scenario manipulations and dispersion modelling, will in such cases be of great help

Quality of emission inventory data: a European perspective

Emission inventories have been, and still are being, compiled within a broad range of national and international activities. The quality of the data in the inventories however is often not defined or not known. This paper concentrates on this aspect of European scale inventories and will present some examples from the field

The new CORINAIR system: an integrated set of tools for national emission inventories

National governments in Europe (and elsewhere) are, within the framework of a series of international protocols, required to annually report on the emissions of air pollutants. The European Commissions Environmental Directorate General (DG XI) initiated the compilation of the European Air Emission Inventory (CORINAIR) for the base year 1990 and later in order to improve the comparability and transparency of emission inventories produced by individual member states and to allow easy integration of such national data into the joint EU reporting mechanism. The European Environment Agency (EEA) took over responsibility for CORINAIR in 1994 as part of its work program

Why Factor Four bites back

"Factor Vier" is een begrip geworden in het milieubeleid nu het door minister De Boer als een belangrijke leidraad voor het milieubeleid op de wat langere termijn is genoemd. "Factor Vier" richt zich op het bereiken van duurzaamheid door het toepassen van betere, efficiëntere technologieën, waardoor de welvaart kan toenemen terwijl tegelijkertijd de milieudruk zal afnemen. Het impliciet technologische optimisme in deze benadering wordt in het onderstaande besproken in het licht van de vaak onverwachte reacties van de maatschappij op nieuwe technologie. Een en ander leidt tot de aanbeveling om naast de technologische en economische beschouwingen in het kader van "Factor Vier", ook gedragsaspecten expliciet mee te nemen. Als dat niet gebeurt, zal de techniek "terugbijten" en kan de beoogde winst worden teniet gedaa

Does environmental data collection need statistics?

The term 'statistics' with reference to environmental science and policymaking might mean different things: the development of statistical methodology, the methodology developed by statisticians to interpret and analyse such data, or the statistical data that are needed to understand environmental pollution and to identify possible policy options. This may and indeed has led to confusion on the part of the users of 'statistics' in environmental science and policymaking. This paper focuses on some of the needs of environmental scientists for statistical methodologies to help us do our jobs better. Statisticians and statistics can contribute to environmental science and the environmental policymaking process in three main ways. First, in describing phenomena: this may in some cases be a non-trivial application of statistical methods. Second, by assessing causal relations: (multivariate) analysis techniques can be applied in establishing the (causal) relations between pressures, state and impact. Third, by setting and checking of standards: uncertainties between the dose-effect relations and the formulation of the standard, and the standard and the state of the environment should be carefully dealt with. Statisticians may help in formulating the standards in such a way that standard checking is as straightforward as possible

Validation and verification of emission inventory data (chapter 5)

This chapter describes principles and practice of validation and verification of emission inventories as means to establish the quality of an emission inventory. It is argued that the perspective of the user of the data is important in defming the quality of emission data. Monitoring of the progress of (environmental) policy requires a different quality concept from the one that is used in scientific applications and the assessment of proposed abatement strategies. Validation is used for the assessment of the sound, agreed upon, approach of emission data collection, whereas verification refers to the establishment of the truth of the data. Validation therefore should be interpreted as the assessment of the procedural quality and verification as the assessment of the scientific quality

Application of the emission inventory model TEAM: Uncertainties in dioxin emission estimates for central Europe

This study uses an improved emission inventory model to assess the uncertainties in emissions of dioxins and furans associated with both knowledge on the exact technologies and processes used, and with the uncertainties of both activity data and emission factors. The annual total emissions for the year 2000 in 13 countries in central and eastern Europe can be estimated with 90% confidence within a range that is about a factor of 2-3 lower to a factor of 3-5 higher than a point value obtained from a more classical approach. It is also shown that the contribution of small residential sources and larger industrial installations and processes are of the same order of magnitude in these countries. It is argued that, despite these uncertainties, policy options can be evaluated and policy decisions on abatement of dioxin and furan emissions can be made. Dioxins and furans belong to the persistent organic pollutants (POPs), an important group of air pollutants that can have long-term effects on ecosystems and human health. Emission estimates for these pollutants all suffer from high uncertainties. This study shows that policy conclusions can still be derived despite these high uncertainties. © 2006 Elsevier Ltd. All rights reserved