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Article Excerpt
In this study we investigated the potential impact of climate change in Portugal on heat-related mortality, air pollution--related health effects, and selected vectorborne diseases. The assessment used climate scenarios from two regional climate models for a range of future time periods. The annual heat-related death rates in Lisbon may increase from between 5.4 and 6 per 100,000 in 1980-1998 to between 8.5 and 12.1 by the 2020s and to a maximum of 29.5 by the 2050s, if no adaptations occur. The projected warmer and more variable weather may result in better dispersion of nitrogen dioxide levels in winter, whereas the higher temperatures may reduce air quality during the warmer months by increasing tropospheric ozone levels. We estimated the future risk of zoonoses using ecologic scenarios to describe future changes in vectors and parasites. Malaria and schistosomiasis, which are currently not endemic in Portugal, are more sensitive to the introduction of infected vectors than to temperature changes. Higher temperatures may increase the transmission risk of zoonoses that are currently endemic to Portugal, such as leishmaniasis, Lyme disease, and Mediterranean spotted fever. Key words: climate change, disease, health impact assessment, Portugal. Environ Health Perspect 114:1950-1956 (2006). doi:10.1289/ehp.8431 available via http://dx.doi.org/ [Online 11 July 2006]
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In this article we describe the Climate Change in Portugal: Scenarios, Impacts and Adaptation Measures (SIAM) project. The first phase of the project was conducted to assess climate change impacts and adaptation measures in continental Portugal (Santos et al. 2002). The SIAM project was divided functionally into 10 groups and an integration team. Seven groups worked on climate change impacts and adaptation measures for specific sectors (impact groups): water resources, coastal zones, agriculture, human health, energy, forests and biodiversity, and fisheries. The remaining groups worked on climate and climate scenarios, socioeconomic scenarios, and a sociologic analysis of climate change issues in Portugal. To facilitate integration across sectors, groups used the same suite of climate data (observed and scenarios) and socioeconomic scenarios.
The results were communicated in Portuguese and in English to the public, decision makers, and other scientists. Throughout the assessment process there were many consultations/interviews with experts (international and national), other stakeholders, and with the other SIAM project groups to discuss cross-sector issues.
In this article we describe the SIAM health impact assessment, focusing on the methods used and the main quantitative results for heat-related mortality, air pollution--related health effects, and vectorborne diseases. Detailed information including suggested adaptation measures for all health impacts assessed is available in the SIAM health technical report (Casimiro and Calheiros 2002).
Health Impact Assessment Methods
Climate-sensitive health outcomes included in the assessment were identified for Portugal on the basis of previous national and international assessments (McMichael and Githeko 2001). Potential health outcomes identified were heat-related mortality, air pollution--related health effects, vector-and rodentborne diseases, water- and foodborne diseases, and health effects associated with floods and drought. Table 1 lists health outcomes further described in this article. During the assessment of each health outcome, the following questions were addressed: a) What is the current (or historical) burden of the health outcome in Portugal? b) What is the climate--health relationship for this health outcome? c) Assuming the climate--health relationship to be valid for all exposure scenarios, what climate change health impacts are anticipated for Portugal?
The current burden of climate-sensitive diseases was obtained from national monitoring and control programs as well as from the literature. Where there were sufficient health and climate data, such as for heat-related mortality, epidemiologic analyses were used to identify and quantify relationships between weather variables and health outcomes. In the case of indirect climate change impacts, such as air pollution--related health outcomes and vectorborne diseases, we focused on establishing the role of climate/weather on the pathways that lead to human exposure. For example, in the air pollution--related health impact assessment, we investigated the relationships between weather and air pollution levels. In the vectorborne assessment, we focused on the relationships between climate and vector survival/activity and/or parasite development. These relationships were then applied using risk assessment methods to estimate the burden of disease under different scenarios. The sections that follow describe these relationships in more detail as well as how they were applied in the risk assessment process.
Health Impacts in the Future: The Use of Scenarios
Estimating the potential impact of climate change on human health calls for the development of risk assessment methods based on scenarios that provide a description of how the future may develop on the basis of plausible and internally consistent sets of assumptions (Ebi and Gamble 2005).
Future climate scenarios for Portugal were created from two regional climate models (RCMs): PROMES, a regional climate model for the Iberian Peninsula developed at the Universidad Complutense de Madrid (Gallardo et al. 2001), and HadRM2, a regional climate model for Europe developed at the Hadley Centre (Exeter, UK), United Kingdom (Jones et al. 1997). Although both RCMs are nested inside the same global climate model (HadCM2) and have similar grid resolutions of approximately 50 km, they differ regarding time frames and future carbon dioxide (C[O.sub.2]) concentrations (Table 2). These differences result in two different sets of projected climate conditions. Because of the difference in the target decades and C[O.sub.2] concentrations, results from the two RCMs cannot be directly compared. Nevertheless, the PROMES climate change model projects a less warm scenario than the HadRM2 model. The results of these models were used in this study to allow for a simple sensitivity assessment of how each health outcome could be affected.
Because both RCM control runs had C[O.sub.2] concentrations similar to those observed during the end of the 20th century, control model runs from both RCMs were compared with (observed) climatology for the baseline period 1961-1990 (Miranda et al. 2002). These comparisons proved to be realistic, although they show that HadRM2 had better agreement with observations than did PROMES. As expected, differences between RCMs and observed climate were more noticeable when extreme weather conditions were compared than when examining average climate.
Compared with the control scenario, the climate change projections for both models show substantial increases in mean annual air temperature, with PROMES indicating an increase of approximately 3.3[degrees]C for the 2040s, and HadRM2 5.8,[degrees]C by the 2090s. This warming is not uniform throughout the year or in its geographic distribution. HadRM2 comparisons project average minimum temperature increases during the winter of the order of 4.5-5.5[degrees]C, with greater increases in the interior south. Changes in summer average maximum temperature are projected to increase by 4.5-9.5[degrees]C, with the northern interior experiencing the maximum increase. Similarly, PROMES anomalies project increases in the winter average minimum temperature ranging from 3.1 to 3.3[degrees]C, with highest increases in the south. However, PROMES summer average maximum temperature anomalies project increases of 4-4.5[degrees]C, with the maximum anomaly in the southwest coast. Both models project increases in the number of days per year with maximum temperatures above 35[degrees]C, as well as days with minimum temperatures above 20[degrees]C. Increased frequency in the days with heavy daily precipitation events in winter are projected by both models. However, HadRM2 projects reductions in mean annual precipitation and in the duration of the rainy season, whereas PROMES projects mean precipitation increases.
Observed climate conditions were used to establish the climate--health relationships (heat-relatedand air pollution--related impact assessments) as well as in the assessment of current health impacts. Results from both control and future runs of RCMs were used to assess the potential changes of each health outcome.
For the heat-related mortality assessment, additional daily weather scenarios for maximum temperature were projected (using both RCMs) for the 2020s and 2050s. Analysis of the mean maximum temperature changes showed that PROMES predicts a slightly warmer climate than the HadRM2 for the2020s and 2050s (Dessai 2003). These projections were not available for other climate variables, limiting their use to the heat-related mortality assessment.
The population/demographic projections developed by the SIAM socioeconomic group could not be used because they were not available at the city/district level. Thus, the population projections for Lisbon used in the heat-related mortality assessment were constructed (Dessai 2003) to be consistent with the Intergovernmental Panel on Climate Change Standardized Reference Emission Scenarios (Nakicenovic and Swart 2000). Lisbon's population was projected to grow in all scenarios. The median population from these calculations for the respective time periods was used in the heat-related mortality assessment (Dessai 2003).
Ecologic scenarios were developed and used in the vectorborne disease assessment. These scenarios incorporate a range of assumptions about the vectors (see below).
Heat-Related Mortality
Heat-related deaths occur during heat wave periods in Portugal (Garcia et al. 1999). An empirical-statistical model, developed and validated...
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