930 resultados para GIS, GPS, buffer analysis, spatial analysis, correlation analysis, air pollution, vehicular pollution


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The Amazon holds over half of the planet's remaining tropical forests and comprises the largest biodiversity in the world, accounting for approximately 60 % of the Brazilian territory. However, deforestation fires in the region causes serious problems to exposed human. The aim of this study was to evaluate the chemical compounds as well as the cellular and molecular effects after exposure to organic material extracted from particulate matter less than 10 µm (PM10) in the Amazon region. As for the chemical composition, n-alkanes analysis showed a prevalence of anthropogenic influence during the fires in the region. In addition, there was a predominance of monosaccharides from biomass burning markers. Also, the Polycyclic Aromatic Hydrocarbons (PAH) and their derivatives have also been identified in samples collected in the Amazon. By using the PAH concentrations was possible to calculate the BaP-equivalent and it was found that the dibenz(a) anthracene contributes with 83% to potential carcinogenic risk. As for the potential mutagenic risk, the benzo (a) pyrene is the HPA that has a major contribution in this analysis. It may be noted that the retene was the most abundant PAH. This compound was genotoxic and cause death by necrosis in the human lung cells. In biological tests, the data showed that organic PM10 is capable of causing genetic damage in both plant cells and in human lung cells. This damage cause an arrest in the G1 phase of the cell cycle exposed, increasing the expression of p53 and p21. Additionally, the PM10 caused cell death by apoptosis, increasing the foci of histone - H2AX. Given these results, it is important to emphasize the reduction and better control of biomass burning in the Amazon region thus improving the quality of health of the population being exposed. As clearly stated recently by the World Health Organization, the reduction of air pollution could save millions of lives annually.

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As the population of urban poor living in slums increases, governments are trying to relocate people into government-provided free housing. Slum redevelopment affects every part of a household’s livelihood, but most importantly the health and wellbeing of younger generations. This paper investigates the effect of slum redevelopment schemes on child stunting levels. Data was collected in forty-one buildings under the slum-redevelopment program in Mumbai. The study demonstrates through a fixed effect regression analysis that an additional year of living in the building is associated with an increase in the height-for-age Z-score by 0.124 standard deviations. Possible explanations include an improvement in the overall hygienic environment, sanitation conditions, indoor air pollution, and access to health and water facilities. However, anecdotal evidence suggests that water contamination, loss of livelihood and increased expenses could worsen health outcomes for residents. This study prompts more research on the health effects of slum redevelopment projects, which are becoming increasingly common in the rapidly urbanizing developing world.

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The burden of chronic diseases such as cancer is increasing in low and middle income countries around the globe. Nepal, one of the world’s poorest countries, is no exception to this trend, with lung cancer as the leading causes of cancer deaths. Despite this, limited data is available on the environmental and behavioral risk factors that contribute to the lung cancer etiology in Nepal. The objectives of this dissertation are to: 1) investigate the ethnic differences in consumption of local tobacco products and their role in lung cancer risk in Nepal; 2) evaluate urinary metabolite of 1,3-butadiene as a biomarker of exposure to combustion related household air pollution (CRHAP); 3) investigate the association between CRHAP exposure and lung cancer risk using urinary metabolite of 1,3-butadiene as a biomarker of exposure; 4) investigate the association between CRHAP exposure and lung cancer risk using questionnaire based measure of exposure. Lung cancer cases (n=606) and frequency matched controls (N=606) were recruited from B.P. Koirala Memorial Cancer Hospital. We obtained biological samples and information on lifestyles including cooking habits and type of fuels used. We used liquid chromatograph tandem mass spectrometer (LC-MS/MS) to quantify urinary metabolites of 1,3-butadiene in urine samples. We employed a combination of logistic and linear regression models to detect any exposure-disease associations while controlling for known confounding variables. Overall, we found that ethnic groups in Nepal use different tobacco products that have different differing cancer potency -we observed the highest odds ratios for the traditional tobacco products. The biomarker analysis showed strong evidence that monohydroxybutyl mercapturic acid is associated with biomass fuel use among participants. However, we did not find significant association between urinary MHMBA and lung cancer risk. When we used questionnaire based measure of exposure to household air pollution, we observed significant, dose-response associations between CRHAP exposure and lung cancer risk, particularly among never-smokers. Our results show that important role of local tobacco products in lung cancer risk in Nepal. Furthermore, we demonstrate that CRHAP exposure is a risk factor for lung cancer risk, independent of tobacco smoking.

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When ambient air quality standards established in the EU Directive 2008/50/EC are exceeded, Member States are obliged to develop and implement Air Quality Plans (AQP) to improve air quality and health. Notwithstanding the achievements in emission reductions and air quality improvement, additional efforts need to be undertaken to improve air quality in a sustainable way - i.e. through a cost-efficiency approach. This work was developed in the scope of the recently concluded MAPLIA project "Moving from Air Pollution to Local Integrated Assessment", and focuses on the definition and assessment of emission abatement measures and their associated costs, air quality and health impacts and benefits by means of air quality modelling tools, health impact functions and cost-efficiency analysis. The MAPLIA system was applied to the Grande Porto urban area (Portugal), addressing PM10 and NOx as the most important pollutants in the region. Four different measures to reduce PM10 and NOx emissions were defined and characterized in terms of emissions and implementation costs, and combined into 15 emission scenarios, simulated by the TAPM air quality modelling tool. Air pollutant concentration fields were then used to estimate health benefits in terms of avoided costs (external costs), using dose-response health impact functions. Results revealed that, among the 15 scenarios analysed, the scenario including all 4 measures lead to a total net benefit of 0.3M€·y(-1). The largest net benefit is obtained for the scenario considering the conversion of 50% of open fire places into heat recovery wood stoves. Although the implementation costs of this measure are high, the benefits outweigh the costs. Research outcomes confirm that the MAPLIA system is useful for policy decision support on air quality improvement strategies, and could be applied to other urban areas where AQP need to be implemented and monitored.

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Air pollution can threat the environment and public health, and is assess by pollutant ́s concentration measurements in order to verify whether the limits set by environmental agencies are being respected. However, these measures do not indicate immediately the impacts to living beings. To faced this problem, plants are been investigated as potential bioindicators of air pollution and, among them, stand out bromeliads Tillandsia genus which colonize various substrates,. obtaining water and nutrients from the atmosphere directly. In this context, this research assessed the potential of epiphytic bromeliad Tillandsia recurvata (L.) L. found in urbanized areas of the city of Curitiba - PR as a bioindicator of urban air pollution. According to vehicle traffic, five sample points were selected and classified. Points P1 and P2 were classified as high-traffic vehicle due presenting trucks and urban transport; point P3 was classified as moderate traffic due the predominance of private vehicles and urban transport; and points P4 and P5 were classified as low-traffic, presenting circulation of private vehicles only. There were analyzed the abundance of T. recurvata, morphophysiological parameters (leaf area, leaf specific area, sclerophylly index, percentage dry weight / fresh weight, chlorophyll (a + b), analysis of structural mesophyll organization) and the heavy metals accumulation (Fe, Cd, Cr, Cu, Pb and Zn). The abundance analysis and the results obtained for metals analysis were correlated with the intensity of vehicular traffic, directing the sampling points P1 > P2 = P3 > P4 = P5. This result demonstrate that the abundance of T. recurvata is greater in urban air pollution impacted areas, thus indicating that T. recurvata absorbs and accumulates metals and can be used in biomonitoring of urban air pollution in areas impacted by vehicular traffic. Morphophysiological parameters analyzed shows that the internal plant ́s structure is not significantly impacted by urban air pollution due plant ́s adptations. The presence of absorbing scales, the CAM metabolism pathway and it ́s store water ability, among other features, demonstrate their potential as bio-indicator in urban areas, especially regarding heavy metals accumulation .

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Geographic Information System (GIS) is a technology that deals with location to support better representations and decision making. It has a long tradition in several planning areas, such as urbanism, environment, riskiness, transportation, archeology or tourism. In academics context higher education has followed that evolution. Despite of their potentialities in education, GIS technologies at the elementary and secondary have been underused. Empowering graduates to learn with GIS and to manipulate spatial data can effectively facilitate the teaching of critical thinking. Likewise it has been recognized that GIS tools can be incorporated as an interdisciplinary pedagogical tool. Nevertheless more practical examples on how GIS tools can enhance teaching and learning process, namely to promote interdisciplinary approaches. The proposed paper presents some results obtained from the project “Each thing in its place: the science in time and space”. This project results from the effort of three professors of Geography, History and Natural Sciences in the context of Didactics of World Knowledge curricular unit to enhance interdisciplinarity through Geographic Information Technologies (GIT). Implemented during the last three years this action-research project developed the research practice using GIS to create an interdisciplinary attitude in the future primary education teachers. More than teaching GIS the authors were focused on teaching with GIS to create an integrated vision where spatial data representation linked the space, the time and natural sciences. Accumulated experience reveals that those technologies can motivate students to learn and facilitating teacher’s interdisciplinary work.

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Strong evidence suggests that the climate is changing and that these changes are largely caused by human activities. A consensus exists among researchers that human activity is causing global warming and that actions to mitigate global warming need to be taken swiftly. The transportation sector, which relies heavily on fossil fuel burning and primarily oil, is one of the big contributors to air pollution problems at local, regional and global levels. It is the fastest growing source of greenhouse gas emissions and is estimated to be responsible for nearly a quarter of global energyrelated carbon dioxide emissions. Car sharing is a mobility solution encouraging its users to decrease private car usage in favour of communal transit and environmental goals. The idea of car sharing originates from the aspiration to decrease personal car ownership and to reduce vehicle distance travelled. This thesis seeks to complement the understanding of Finnish car sharing users and their usage through better categorization. Through better categorization and segmentation of Finnish car sharing users the thesis seeks to provide information for improved marketing insight. Research is done on the demographic and behavioural characteristics of Finnish car sharing users and they are compared with international findings about the characteristics of International car sharing users. The main research problem is Are Finnish car sharing users similar to international ones? A theoretical research framework on the determinants of individual car sharing usage is built based on international research about demographic and behaviouristic characteristics. After this a quantitative survey is performed to the customers of a Finnish car sharing organization. The data analysed in the thesis consist out of 532 answers received from the car sharing organizations customers. The data is analysed with descriptive and other exploratory methods, which create an understanding of Finnish car sharing users. At the end of the analysis the demographic and behavioural characteristics of Finnish car sharing users are compared with international ones. The research findings of the thesis indicate that the demographic and behavioural characteristics of Finnish car sharing usage largely follow those of their international counterparts. Thanks to the thesis results the car sharing organization is able to better target their customers through improved marketing insight.

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A poluição atmosférica é um dos principais factores de degradação da qualidade de vida da população. O conjunto BTEX (benzeno, tolueno, etilbenzeno e xilenos) constitui o grupo mais importante dos compostos orgânicos voláteis (VOCs) na atmosfera uma vez que participam na química da atmosfera e constituem um perigo para a saúde, nomeadamente o benzeno, por ser altamente cancerígeno. São maioritariamente libertados pelo tráfego automóvel. Neste trabalho foi determinada a concentração dos BTEX em nove pontos da cidade de Évora no período de 21 Março a 1 de Julho de 2009 tendo-se recorrido à técnica de amostragem passiva, com amostradores Radiello™, seguida de desadsorção líquida, usando CS2, e subsequente análise por GC-MS. A concentração de benzeno no ar da cidade de Évora não excedeu o valor legislado de 5 g/m3 neste período de amostragem, sendo as concentrações obtidas para os poluentes em geral muito baixas e na sua maioria inferiores ao LOQ do método analítico. ABSTRACT; Air pollution is the major factor in the degradation of the population quality of life. BTEX (benzene, toluene, ethylbenzene and xylenes) is the most important group of volatile organic compounds (VOCs) in the atmosphere because of their role in atmospheric chemistry and the risk they posed to human health, with benzene, being a highly carcinogenic compound. BTEX are released mainly by road traffic. Concentrations of BTEX were determined at nine sampling points in the city of Évora in the period from 21 March to 1 July 2009, using passive samplers Radiello™, followed by liquid desorption with CS2, and subsequent analysis by GC-MS. During the sampling period, the concentration of benzene in the outdoor air of Évora city did not exceed 5 g/m3, the maximum value admissible by legislation. The concentrations measured of the other pollutants were, in general, very low and mostly below the LOQ of the analytical method.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Química, Programa de Pós-Graduação em Tecnologia Química e Biológica, 2016.

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Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.