Factorization of air pollutant emissions: projections versus observed trends in Europe.

This paper revisits the emission scenarios of the European Commission's 2005 Thematic Strategy on Air Pollution (TSAP) in light of today's knowledge. We review assumptions made in the past on the main drivers of emission changes, i.e., demographic trends, economic growth, changes in the energy intensity of GDP, fuel-switching, and application of dedicated emission control measures. Our analysis shows that for most of these drivers, actual trends have not matched initial expectations. Observed ammonia and sulfur emissions in European Union in 2010 were 10% to 20% lower than projected, while emissions of nitrogen oxides and particulate matter exceeded estimates by 8% to 15%. In general, a higher efficiency of dedicated emission controls compensated for a lower-than-expected decline in total energy consumption as well as a delay in the phase-out of coal. For 2020, updated projections anticipate lower sulfur and nitrogen oxide emissions than those under the 2005 baseline, whereby the degree to which these emissions are lower depends on what assumptions are made for emission controls and new vehicle standards. Projected levels of particulates are about 10% higher, while smaller differences emerge for other pollutants. New emission projections suggest that environmental targets established by the TSAP for the protection of human health, eutrophication and forest acidification will not be met without additional measures.

Significance of travel to rural areas as a risk factor for malarial anemia in an urban setting.

The epidemiology of malaria in urban environments is poorly characterized, yet increasingly problematic. We conducted an unmatched case-control study of risk factors for malarial anemia with high parasitemia in urban Kisumu, Kenya, from June 2002 through February 2003. Cases (n = 80) were hospital patients with a hemoglobin level < or = 8 g/dL and a Plasmodium parasite density > or = 10,000/microL. Controls (n = 826) were healthy respondents to a concurrent citywide knowledge, attitude, and practice survey. Children who reported spending at least one night per month in a rural area were especially at risk (35% of cases; odds ratio = 9.3, 95% confidence interval [CI] = 4.4-19.7, P < 0.0001), and use of mosquito coils, bed net ownership, and house construction were non-significant, potentially indicating that malaria exposure during rural travel comprises an important element of risk. Control of severe malaria in an urban setting may be complicated by Plasmodium infections acquired elsewhere. Epidemiologic studies of urban malaria in low transmission settings should take travel history into account.

A census-weighted, spatially-stratified household sampling strategy for urban malaria epidemiology.

BACKGROUND: Urban malaria is likely to become increasingly important as a consequence of the growing proportion of Africans living in cities. A novel sampling strategy was developed for urban areas to generate a sample simultaneously representative of population and inhabited environments. Such a strategy should facilitate analysis of important epidemiological relationships in this ecological context. METHODS: Census maps and summary data for Kisumu, Kenya, were used to create a pseudo-sampling frame using the geographic coordinates of census-sampled structures. For every enumeration area (EA) designated as urban by the census (n = 535), a sample of structures equal to one-tenth the number of households was selected. In EAs designated as rural (n = 32), a geographically random sample totalling one-tenth the number of households was selected from a grid of points at 100 m intervals. The selected samples were cross-referenced to a geographic information system, and coordinates transferred to handheld global positioning units. Interviewers found the closest eligible household to the sampling point and interviewed the caregiver of a child aged < 10 years. The demographics of the selected sample were compared with results from the Kenya Demographic and Health Survey to assess sample validity. Results were also compared among urban and rural EAs. RESULTS: 4,336 interviews were completed in 473 of the 567 study area EAs from June 2002 through February 2003. EAs without completed interviews were randomly distributed, and non-response was approximately 2%. Mean distance from the assigned sampling point to the completed interview was 74.6 m, and was significantly less in urban than rural EAs, even when controlling for number of households. The selected sample had significantly more children and females of childbearing age than the general population, and fewer older individuals. CONCLUSION: This method selected a sample that was simultaneously population-representative and inclusive of important environmental variation. The use of a pseudo-sampling frame and pre-programmed handheld GPS units is more efficient and may yield a more complete sample than traditional methods, and is less expensive than complete population enumeration.

Quantitative urban classification for malaria epidemiology in sub-Saharan Africa.

BACKGROUND: Although sub-Saharan Africa (SSA) is rapidly urbanizing, the terms used to classify urban ecotypes are poorly defined in the context of malaria epidemiology. Lack of clear definitions may cause misclassification error, which likely decreases the accuracy of continent-wide estimates of malaria burden, limits the generalizability of urban malaria studies, and makes identification of high-risk areas for targeted interventions within cities more difficult. Accordingly, clustering techniques were applied to a set of urbanization- and malaria-related variables in Kisumu, Kenya, to produce a quantitative classification of the urban environment for malaria research. METHODS: Seven variables with a known or expected relationship with malaria in the context of urbanization were identified and measured at the census enumeration area (EA) level, using three sources: a) the results of a citywide knowledge, attitudes and practices (KAP) survey; b) a high-resolution multispectral satellite image; and c) national census data. Principal components analysis (PCA) was used to identify three factors explaining higher proportions of the combined variance than the original variables. A k-means clustering algorithm was applied to the EA-level factor scores to assign EAs to one of three categories: "urban," "peri-urban," or "semi-rural." The results were compared with classifications derived from two other approaches: a) administrative designation of urban/rural by the census or b) population density thresholds. RESULTS: Urban zones resulting from the clustering algorithm were more geographically coherent than those delineated by population density. Clustering distributed population more evenly among zones than either of the other methods and more accurately predicted variation in other variables related to urbanization, but not used for classification. CONCLUSION: Effective urban malaria epidemiology and control would benefit from quantitative methods to identify and characterize urban areas. Cluster analysis techniques were used to classify Kisumu, Kenya, into levels of urbanization in a repeatable and unbiased manner, an approach that should permit more relevant comparisons among and within urban areas. To the extent that these divisions predict meaningful intra-urban differences in malaria epidemiology, they should inform targeted urban malaria interventions in cities across SSA.