37 resultados para TRACING SEWAGE POLLUTION

em Université de Lausanne, Switzerland


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An assessment of sewage workers' exposure to airborne cultivable bacteria, fungi and inhaled endotoxins was performed at 11 sewage treatment plants. We sampled the enclosed and unenclosed treatment areas in each plant and evaluated the influence of seasons (summer and winter) on bioaerosol levels. We also measured personal exposure to endotoxins of workers during special operation where a higher risk of bioaerosol inhalation was assumed. Results show that only fungi are present in significantly higher concentrations in summer than in winter (2331 +/- 858 versus 329 +/- 95 CFU m(-3)). We also found that there are significantly more bacteria in the enclosed area, near the particle grids for incoming water, than in the unenclosed area near the aeration basins (9455 +/- 2661 versus 2435 +/- 985 CFU m(-3) in summer and 11 081 +/- 2299 versus 2002 +/- 839 CFU m(-3) in winter). All bioaerosols were frequently above the recommended values of occupational exposure. Workers carrying out special tasks such as cleaning tanks were exposed to very high levels of endotoxins (up to 500 EU m(-3)) compared to routine work. The species composition and concentration of airborne Gram-negative bacteria were also studied. A broad spectrum of different species within the Pseudomonadaceae and the Enterobacteriaceae families were predominant in nearly all plants investigated. [Authors]

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Environmental research in earth sciences is focused on the geosphere, i.e. (1) waters and sediments of rivers, lakes and oceans, and (2) soils and underlying shallow rock formations,both water-unsaturated and -saturated. The subsurface is studied down to greater depths at sites where waste repositories or tunnels are planned and mining activities exist. In recent years, earth scientists have become more and more involved in pollution problems related to their classical field of interest, e.g. groundwater, ore deposits, or petroleum and non-metal natural deposits (gravel, clay, cement precursors). Major pollutants include chemical substances, radioactive isotopes and microorganisms. Mechanisms which govern the transport of pollutants are of physical, chemical (dissolution, precipitation, adsorption), or microbiological (transformation) nature. Land-use planning must reflect a sustainable development and sound scientific criteria. Today's environmental pollution requires working teams with an interdisciplinary background in earth sciences, hydrology, chemistry, biology, physics as well as engineering. This symposium brought together for the first time in Switzerland earth and soil scientists, physicists and chemists, to present and discuss environmental issues concerning the geosphere.

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This study shows the efficiency of passive sampling to reveal industrial and agricultural pollution trends. Two practical applications for nonpolar and polar contaminants are presented. Low-density polyethylene (LDPE) samplers were deployed for one year in the Venoge River (VD) to monitor indicator PCBs (iPCBs, IUPAC nos. 28, 52, 101, 138, 153 and 180). The results showed that the impact of PCB emissions into the river is higher in summer than in other seasons due to the low flow rate of the river during this period. P,olar organic chemical integrative samplers (POCIS) were deployed for 4 months in the Sion-Riddes canal (VS) to investigate herbicides (terbuthylazine, diuron and linuron). Desisopropylatrazine-d5 (DIA-d5) was tested as a performance reference compound (PRC) to estimate aqueous concentration. The results showed an increase of water contamination due to the studied agricultural area. The maximal contamination was observed in April and corresponds to the period of herbicide application on the crops.

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Evidence has emerged that the initiation and growth of gliomas is sustained by a subpopulation of cancer-initiating cells (CICs). Because of the difficulty of using markers to tag CICs in gliomas, we have previously exploited more robust phenotypic characteristics, including a specific morphology and intrincic autofluorescence, to identify and isolate a subpopulation of glioma CICs, called FL1(+). The objective of this study was to further validate our method in a large cohort of human glioma and a mouse model of glioma. Seventy-four human gliomas of all grades and the GFAP-V(12)HA-ras B8 mouse model were analyzed for in vitro self-renewal capacity and their content of FL1(+). Nonneoplastic brain tissue and embryonic mouse brain were used as control. Genetic traceability along passages was assessed with microsatellite analysis. We found that FL1(+) cells from low-grade gliomas and from control nonneoplasic brain tissue show a lower level of autofluorescence and undergo a restricted number of cell divisions before dying in culture. In contrast, we found that FL1(+) cells derived from many but not all high-grade gliomas acquire high levels of autofluorescence and can be propagated in long-term cultures. Moreover, FL1(+) cells show a remarkable traceability over time in vitro and in vivo. Our results show that FL1(+) cells can be found in all specimens of a large cohort of human gliomas of different grades and in a model of genetically induced mouse glioma as well as nonneoplastic brain. However, their self-renewal capacity is variable and seems to be dependent on the tumor grade.

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This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

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SETTING: A 950 bed teaching hospital in Switzerland. AIM: To describe the result of a contact investigation among health care workers (HCW) and patients after exposure to a physician with smear-positive pulmonary tuberculosis in a hospital setting using standard tuberculin skin tests (TST) and Interferon-gamma release assay (IGRA). METHOD: HCW with a negative or unknown TST at hiring had a TST two weeks after the last contact with the index case (T0), repeated six weeks later if negative (T6). All exposed HCW had a T-SPOT.TB at T0 and T6. Exposed patients had a TST six weeks after the last contact, and a T-SPOT.TB if the TST was positive. RESULTS: Among 101 HCW, 17/73 (22%) had a positive TST at T0. TST was repeated in 50 at T6 and converted from negative to positive in eight (16%). Twelve HCW had a positive T-SPOT.TB at T0 and ten converted from negative to positive at T6. Seven HCW with a positive T-SPOT.TB reverted to negative at T6 or at later controls, most of them with test values close to the cut-off. Among 27 exposed patients tested at six weeks, ten had a positive TST, five of them confirmed by a positive T-SPOT.TB. CONCLUSIONS: HCW tested twice after exposure to a case of smear-positive pulmonary TB demonstrated a possible conversion in 10% with T-SPOT and 16% with TST. Some T-SPOT.TB reverted from positive to negative during the follow-up, mostly tests with a value close to the cut-off. Due to the variability of the test results, it seems advisable to repeat the test with values close to the cut-off before diagnosing the presence of a tuberculous infection.

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Some years ago, a parish in Geneva decided to reduce heating costs by insulating its church to make it more energy efficient. Three years after the last renovations, it was observed that the internal surfaces of the naves had already become dusty compared with the customary frequency of 10-12 years. Dust even deposited on various surfaces during religious services. Our investigation showed that nearly all the dust found inside the church may in fact be soot from incense and candle combustion. Incense appears to be a significant source of polycyclic aromatic hydrocarbons. With a mechanical ventilation system and petrol lamps resembling candles the problem can be resolved.

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A majority of smokers and non-smokers mind tobacco smoke. Passive smoking causes death by sudden infant death, lung cancer and coronary heart disease. 3000 to 6000 persons are killed every year in France. The lack of implementation of the Evin's law published in 1991 explains why non-smokers are not given the protection they can expect. The trend of scientific knowledge and of French and international public opinions support a growing demand for a complete protection of non-smokers with a total ban of smoking in all public or working places.

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Since 1998 the highly polluted Havana Bay ecosystem has been the subject of a mitigation program. In order to determine whether pollution-reduction strategies were effective, we have evaluated the historical trends of pollution recorded in sediments of the Bay. A sediment core was dated radiometrically using natural and artificial fallout radionuclides. An irregularity in the (210)Pb record was caused by an episode of accelerated sedimentation. This episode was dated to occur in 1982, a year coincident with the heaviest rains reported in Havana over the XX century. Peaks of mass accumulation rates (MAR) were associated with hurricanes and intensive rains. In the past 60 years, these maxima are related to strong El Niño periods, which are known to increase rainfall in the north Caribbean region. We observed a steady increase of pollution (mainly Pb, Zn, Sn, and Hg) since the beginning of the century to the mid 90s, with enrichment factors as high as 6. MAR and pollution decreased rapidly after the mid 90s, although some trace metal levels remain high. This reduction was due to the integrated coastal zone management program introduced in the late 90s, which dismissed catchment erosion and pollution.