Suchergebnisse

Air Quality Plans for the Northern Region of Portugal: Improving Particulate Matter and Coping with Legislation

In December 1997, the Kyoto Protocol was adopted, setting limits on the greenhouse gas (GHG) emissions of industrialized countries. The European Union agreed to reduce its emissions of GHG by 8% during the period 2008–2012 in comparison to their 1990 levels. Subsequently, in a scheme known as "burden-sharing", Portugal was allowed to increase its emissions by 27% in the same period. Large industrial facilities are responsible for a significant share of carbon dioxide (CO2) emissions and are object of a European Directive (2003/87/EC) establishing the scheme for GHG emission allowance trading within the European Union, launched with the purpose of allowing the reduction of GHG emissions cost-effectively. According to the Directive, Member States shall develop a National Allocation Plan (NAP) stating the total quantity of allowances that each one intends to allocate and how it proposes to allocate them among the activities included in the trading scheme. In this work, an analysis of the Portuguese industry is performed, focused on the energy consumption and CO2 emissions levels in the period 1990–2001 and on the estimation of the two parameters for the period 2002–2012, considering different economic growth scenarios and investments on energy reduction technologies. Results show that all the analysed sectors present a significant growth in CO2 emissions, exceeding the limit established in the frame of the Kyoto Protocol, and that measures other than cost-effective energy technologies will have to be implemented.

According to the Air Quality Framework Directive, air pollutant concentration levels have to be assessed and reported annually by each European Union member state, taking into consideration European air quality standards. Plans and programmes should be implemented in zones and agglomerations where pollutant concentrations exceed the limit and target values. The main objective of this study is to perform a long-term air quality simulation for Portugal, using the CHIMERE chemistry-transport model, applied over Portugal, for the year 2001. The model performance was evaluated by comparing its results to air quality data from the regional monitoring networks and to data from a diffusive sampling experimental campaign. The results obtained show a modelling system able to reproduce the pollutant concentrations' temporal evolution and spatial distribution observed at the regional networks of air quality monitoring. As far as the fulfilment of the air quality targets is concerned, there are excessive values for nitrogen and sulfur dioxides, ozone also being a critical gaseous pollutant in what concerns hourly concentrations and AOT40 (Accumulated Over Threshold 40 ppb) values.

In this work the potential impacts of climate-induced changes in air pollution levels and its impacts on population health was investigated. The IPCC scenario (SRES A2) was used to analyse the effects of climate on future PM10 concentrations over Portugal and their impact on short-term population exposure and mortality. The air quality modelling system has been applied with high spatial resolution looking on climate changes at regional scale. To quantify health impacts related with air pollution changes the WHO methodology for health impact assessment was implemented. The results point to 8% increase of premature mortality attributed to future PM10 levels in Portugal. The pollution episodes with daily average PM10 concentration above the current legislated value (50 µg.m-3) would be responsible for 81% of attributable cases. The absolute number of deaths attributable to PM10 under future climate emphasizes the importance of indirect effects of climate change on human health.

In its 27th edition the conference on Air Pollution continues to produce valuable research on issues related to the modelling, monitoring and management of air pollution. The papers included in this book continue a wide ranging collection of high quality research works that develop the fundamental science of air pollution

Wood is commonly used in residential combustion for heating purposes; however, it can be a major source of air pollutants, namely fine particles, volatile organic compounds and carbon monoxide. Since 2004, the PM10 daily limit value has been surpassed in Portugal, and the European Commission has stated that plans and programs must be designed in order to reduce these levels. In Portugal, 18% of PM10 emissions are due to residential wood combustion, which may deeply impact the PM10 levels in the atmosphere. The main aim of this study is to investigate the impact of residential wood combustion on the air quality in Portugal. The air quality modelling system MM5/CHIMERE was applied over Portugal for a winter month, for the following three scenarios: the reference scenario, considering the actual emissions of PM10; scenario 1, where residential wood combustion emissions are not considered; and scenario 2, which takes into account a complete conversion from traditional fireplaces to certified appliances (with a 90% reduction in PM emissions). The residential wood combustion contribution to PM10 air quality concentration values during January 2007 ranges from 0 to 14 μg m−3, with a mean contribution of 10 μg m−3 in the Lisboa area and 6 μg m−3 in the Porto region. Concerning the legislated values, the area where the daily average limit value (50 μg m−3) is exceeded decreases by 46% in the simulation when residential combustion is not considered. The modelling results for scenario 2 are not significantly different from those for scenario 1. In summary, the regulation of the residential wood combustion sector is as an effective way to reduce the PM10 levels in the atmosphere as regards air quality plans and programs.

We summarise results from a workshop on 'Model Benchmarking and Quality Assurance' of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define 'benchmarking' as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.

We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.

We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.