W. Schoepp

Modelling PM2.5 impact indicators in Europe

Long-term exposure to fine particulate matter (PM2.5) has been shown to have significant negative impacts on human health. It is estimated that current levels of air pollution shorten the statistical life expectancy of European citizens by several months. The GAINS integrated assessment model calculates shortening of life expectancy from population exposure to PM2.5 using epidemiologically-derived health impact functions. In addition, GAINS estimates PM2.5 concentrations at 1875 air quality monitoring stations located in diverse environments ranging from remote background locations to busy street canyons. In this article, different approaches to dealing with the PM2.5 pollution problem are compared. We assess for the present and future the attainment of EU and WHO air quality standards for PM2.5 and estimate the loss of life expectancy under different policy scenarios developed for the ongoing revision of the EU Air Quality Legislation. We present PM2.5 forecasts for 1875 individual air quality monitoring stations in Europe.Calculations bridge the gap between regional background and street canyons.Compliance with the EU limit value is achieved easily, but not with the WHO guideline.We quantify EU wide health benefits of future emission control legislation scenarios.We analyse the relationship between compliance and population exposure.

GAINS: an interactive tool for assessing international GHG mitigation regimes

Mitigating greenhouse gases (GHGs) is key to reducing the longterm impacts of climate change. In this paper we present the GAINS system, i.e. a data warehouse with an online integrated assessment model that is already used in various international policy fora as a tool to quantify the costs and environmental benefits of reducing emissions of greenhouses gases and air pollutants. We explain the basic concepts and requirements of the system and illustrate how short-term co-benefits for local pollution can motivate GHG mitigation as a response of the otherwise intangible long-term and global risk of climate change. Hereafter, GAINS can be used as a common framework to make available and to compare the implications of the outputs of different energy system models working at different spatial and temporal scales. Furthermore, outputs of GAINS can be used as input for other models. Finally, we thus illustrate how integrated data management as implemented in the GAINS system supports the development of science-driven policies in term of case studies.

GAINS-BI: business intelligent approach for greenhouse gas and air pollution interactions and synergies information system

The Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-Model is studied and developed to provide a consistent framework for the analysis of co-benefits reduction strategies from air pollution and greenhouse gas sources. In this paper we introduced a BI approach, namely GAINS-BI, applied as a further development of the GAINS model. In this context, the GAINS-BI conceptual model, including GAINS-BI architecture and concepts, is specified based on a sound mathematical models used for calculate emission and costs. Hereafter, a multidimensional data model, e.g. activity, emission and cost data cubes, has been studied and introduced to represent specific multidimensional analysis requirements of greenhouse gas and air pollution application domains. To proof of concepts, some implementation results have been presented.

Cloud intelligent services for calculating emissions and costs of air pollutants and greenhouse gases

The GAINS (Greenhouse gas - Air pollution Interactions and Synergies) model quantifies the full DPSIR (demand-pressure-state-impact-response) chain for the emissions of air pollutants and greenhouse gases. To fulfill regional specific requirements of the GAINS model, we have studied and developed a cloud intelligent service system for calculating emissions and costs for reducing emissions at regional as well as global levels. In this paper, first we present a cloud intelligent conceptual model that is used to specify an application framework, namely GAINS cloud intelligent application framework. Using this application framework, first we build a global data warehouse called GAINS DWH World, then a class of regional data warehouses, e.g. GAINS DWH Europe, GAINS DWH Asia, etc, are specified and used for regional data analysis and cost optimization.

Forecast of Sulfur Deposition in Japan for Various Energy Supply and Emission Control Scenarios

In order to contribute to the analysis and solution of regional scale environmental problems in East Asia, we developed a tool for the comprehensive assessment of alternative policy options to improve air quality. This tool projects the future regional energy supply, calculates the emission levels of sulfur dioxide and estimates the geographical pattern of sulfur deposition resulting from emissions. Sulfur deposition in Japan through 2030 was forecasted for various energy supply and emission control scenarios using the analysis tool. Future sulfur depositions were calculated from the source-receptor matrix for 1995 and the growth rate of emission for the source subregion. In the case of the current legislation scenario, anthropogenic SO2 emissions in East Asia would grow by 34 percent and sulfur deposition in Japan would increase by approximately 20 percent between 1995 and 2030. This increase in sulfur deposition over these 35 years is sligthly less than the contribution from volcanic emission to sulfur deposition in Japan. In the case of the hypothetical dirty scenario for China, sulfur deposition in several grids which face the Sea of Japan would double by 2030.

EC4MACS: an integrated assessment toolbox of well-established modeling tools to explore the synergies and interactions between climate change, air quality and other policy objectives

EC4MACS (European Consortium for Modelling of Air Pollution and Climate Strategies) establishes a suite of modelling tools for a comprehensive integrated assessment of the effectiveness of emission control strategies for air pollutants and greenhouse gases. This assessment brought together expert knowledge in the fields of energy, transport, agriculture, forestry, land use, atmospheric dispersion, health and vegetation impacts, and it developed a coherent outlook into the future options to reduce atmospheric pollution in Europe. In this paper, first we introduce background to the EC4MACS framework, which links well-established sectoral models of the most relevant policy areas. In this context, an ETL package is used to gather extracted information from multiple model data sources. The integrated data are loaded into the GAINS (Greenhouse gas-Air pollution Interactions and Synergies) Data Warehouse. Afterwards, a web service based toolbox is developed to publish EC4MACS key data, which are represented in this paper in term of case studies.

Mitigation Efforts Calculator (MEC)

The Mitigation Efforts Calculator (MEC) has been developed by the International Institute for Applied Systems Analysis (IIASA) as an online tool to compare greenhouse gas (GHG) mitigation proposals by various countries for the year 2020. In this paper, first we introduce the MEC conceptual model, i.e. the methodology and system architecture. We then discuss the abstract formulation of four different international greenhouse gas trading regimes that are conceivable. Hereafter, the optimization process and its output results, namely cost curves are presented. Finally, we illustrate the MEC as a tool for interactively evaluating complex cost curve information in the context of GHG mitigation targets as currently discussed in international climate policy circles.

Federated data warehousing application framework and platform-as-a-services to model virtual data marts in the clouds

Each federated data warehousing system contains a data warehouse and a class of data marts. Building and maintaining such systems are painful processes. In this paper, we propose our approach to improve these processes. First, a cloud-based federated data warehousing system architecture, description of which is specified in a formal manner, is introduced. Afterwards, Unified Modelling Language (UML) is used to design the conceptual model. In this context, a cloud-based federated data warehousing application framework and its components, i.e., global data warehouse and linked virtual data marts, as well as its services, i.e., platform-as-a-services, software-as-a-services are modelled. Hereafter, the GAINS model is presented as an implementation example to proof of our concepts.

Sustainable Agriculture in China: Estimation and Reduction of Nitrogen Impacts

In this chapter we present an integrated model for long term and geographically explicit planning of agricultural activities to meet demands under resource constraints and ambient targets. Environmental, resource and production feasibility indicators permit estimating impacts of agricultural practices on environment to guide agricultural policies regarding production allocation, intensification, and fertilizer application while accounting for local constraints. Physical production potentials of land are incorporated in the model, together with demographic and socio-economic variables and behavioral drivers to reflect spatial distribution of demands and production intensification levels. The application of the model is demonstrated with a case study of nitrogen accounting at the level of China counties. We discuss current intensification trends and estimate the ranges of agricultural impacts on China’s environment under plausible pollution mitigation scenarios with a particular focus on nitrogen sources and losses.