Niveles de exposición ambiental a polvo de carbón y su relación con las condiciones de higiene y seguridad industrial en trabajadores de minas subterráneas de la región de Boyacá

Introducción: La minería subterránea es considerada de alto riesgo afectando la salud de trabajadores expuestos a factores de riesgo y condiciones de trabajo, sin que exista información sobre concentración de material particulado y niveles de riesgo. Objetivo: Determinar la exposición ambiental a polvo de carbón y su relación con las condiciones de higiene y seguridad industrial en los trabajadores que laboran en minas subterráneas de la región de Boyacá. Materiales y métodos: Estudio de corte transversal donde se emplearon cuestionarios para recolectar datos sobre condiciones de trabajo y se realizaron muestreos ambientales de material particulado mediante método de análisis gravimétrico y metodología 0600 de NIOSH. Resultados: Estudio realizado en 19 empresas con 232 trabajadores, con edades entre 20 y 73 años. La concentración promedio de material particulado en los 209 monitoreos realizados fue de 3,4 +3,4mg/m3. El nivel de riesgo alto por exposición a polvo de carbón se encontró en el 70,8% (148) de los monitoreos y el 20,6% (43) en nivel severo, con promedio de 4,9 +4,9 mg/m3. Asociaciones significativas se reportaron entre trabajadores que no usaban protección respiratoria y nivel de riesgo medio y alto (p=0,033); uso mascarilla sin cartucho y nivel de riesgo bajo y medio (p=0,013); el no uso de protección auditiva y niveles medio y alto (p=0,010) y consumo de cigarrillo en el trabajo y niveles medio, alto y severo (p=0,008). Conclusiones: Se determinó vinculación y relación significativa entre los niveles de riesgo alto y severo por exposición a polvo de carbón con concentraciones por encima de niveles permisibles y las condiciones de seguridad industrial de trabajadores

Evaluation of a predictive model for Mycosphaerella graminicola for economic and environmental benefits

A method was developed to evaluate crop disease predictive models for their economic and environmental benefits. Benefits were quantified as the value of a prediction measured by costs saved and fungicide dose saved. The value of prediction was defined as the net gain made by using predictions, measured as the difference between a scenario where predictions are available and used and a scenario without prediction. Comparable 'with' and 'without' scenarios were created with the use of risk levels. These risk levels were derived from a probability distribution fitted to observed disease severities. These distributions were used to calculate the probability that a certain disease induced economic loss was incurred. The method was exemplified by using it to evaluate a model developed for Mycosphaerella graminicola risk prediction. Based on the value of prediction, the tested model may have economic and environmental benefits to growers if used to guide treatment decisions on resistant cultivars. It is shown that the value of prediction measured by fungicide dose saved and costs saved is constant with the risk level. The model could also be used to evaluate similar crop disease predictive models.

A novel method for assessing risks to pollinators from plant protection products using honeybees as a model species

Pollination is one of the most important ecosystem services in agroecosystems and supports food production. Pollinators are potentially at risk being exposed to pesticides and the main route of exposure is direct contact, in some cases ingestion, of contaminated materials such as pollen, nectar, flowers and foliage. To date there are no suitable methods for predicting pesticide exposure for pollinators, therefore official procedures to assess pesticide risk are based on a Hazard Quotient. Here we develop a procedure to assess exposure and risk for pollinators based on the foraging behaviour of honeybees (Apis mellifera) and using this species as indicator representative of pollinating insects. The method was applied in 13 European field sites with different climatic, landscape and land use characteristics. The level of risk during the crop growing season was evaluated as a function of the active ingredients used and application regime. Risk levels were primarily determined by the agronomic practices employed (i.e. crop type, pest control method, pesticide use), and there was a clear temporal partitioning of risks through time. Generally the risk was higher in sites cultivated with permanent crops, such as vineyard and olive, than in annual crops, such as cereals and oil seed rape. The greatest level of risk is generally found at the beginning of the growing season for annual crops and later in June–July for permanent crops.

Style analysis in real estate markets: beyond the sectors and regions dichotomy

While style analysis has been studied extensively in equity markets, applications of this valuable tool for measuring and benchmarking performance and risk in a real estate context are still relatively new. Most previous real estate studies on this topic have identified three investment categories (rather than styles): sectors, administrative regions and economic regions. However, the low explanatory power reveals the need to extend this analysis to other investment styles. We identify four main real estate investment styles and apply a multivariate model to randomly generated portfolios to test the significance of each style in explaining portfolio returns. Results show that significant alpha performance is significantly reduced when we account for the new investment styles, with small vs. big properties being the dominant one. Secondly, we find that the probability of obtaining alpha performance is dependent upon the actual exposure of funds to style factors. Finally we obtain that both alpha and systematic risk levels are linked to the actual characteristics of portfolios. Our overall results suggest that it would be beneficial for real estate fund managers to use these style factors to set benchmarks and to analyze portfolio returns.

Análise de decisão multicritério na localização de usinas termoelétricas utilizando SIG

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aplicação da metodologia de superfície de resposta na remediação de um solo arenoso contaminado artificialmente com cobre

Soil flushing is an alternative remediation technology for soils contaminated with heavy metals, which the main contaminant removing process consists in percolating an extraction solution. This work aimed to use the response surface methodology to point out combinations among the parameters of the extraction solution (Na2EDTA concentration, volume e pH) in order to reduce the concentration of copper in a sandy soil to risk levels lower than the intervention levels for exposure scenarios adopted by the Environmental Company of Sao Paulo State. Thus, a series of tests in leaching columns were carried out using a Fluvisol artificially contaminated (1257,3 mg kg-1). The tests were conducted in triplicate and setup a central composite rotatable design with 15 different parameters combinations of the extraction solution and one replicate in the center point. Using 5% significance level, the adjusted model (R2 = 0,98) indicated combinations of Na2EDTA concentration, pH and volume of the extraction solution which allow reduction of copper concentration below levels reported by environmental agency of Sao Paulo State for industrial, residential, agricultural or maximum exposure scenarios.

Sistema com aeração, decantação e filtragem para melhoria da qualidade da água para irrigação localizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Citricultura na região sul paulista: modelo de previsão econômica para tomada de decisão em condições de risco

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação da metodologia de superfície de resposta na otimização da remediação de um solo arenoso contaminado com cobre

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise da incidência de casos de dengue na área urbana de Belém – PA: uma aplicação de modelos espaciais e temporais

Neste trabalho, foi realizado um estudo de mapeamento de áreas de incidência e previsões para os casos de dengue na área urbana de Belém. Para as previsões foi utilizada à incidência de dengue com a precipitação pluviométrica a partir de modelos estatísticos, baseados na metodologia de Box e Jenkins de series temporais. O período do estudo foi de 05 anos (2007-2011). Na pesquisa temos métodos multivariados de series temporais, com uso de função de transferência e modelos espaciais, em que se analisou a existência de autocorrelações espaciais na variável em estudo. Os resultados das análises dos dados de incidência de casos de dengue e precipitação mostraram que, o aumento no número de casos de dengue acompanha o aumento na precipitação, demonstrando a relação direta entre o número de casos de dengue e a precipitação nos anos em estudo. O modelo de previsão construído para a incidência de casos de dengue apresentou um bom ajuste com resultados satisfatórios podendo, neste caso, ser utilizado na previsão da dengue. Em relação à análise espacial, foi possível uma visualização da incidência de casos na área urbana de Belém, com as respectivas áreas de incidência, mostrando os níveis de significância em porcentagem. Para o período estudado observou-se o comportamento e as variações dos casos de dengue, com destaque para quatro bairros: Marco, Guamá, Pedreira e Tapanã, com possíveis influências destes bairros nas áreas (bairros) vizinhas. Portanto, o presente estudo evidencia a contribuição para o planejamento das ações de controle da dengue, ao servir de instrumento no apoio às decisões na área de saúde pública.

Sirex noctilio (Hymenoptera: Siricidae) e seus inimigos naturais em Pinus taeda e caracterização morfológica de Deladenus siricidicola

Pós-graduação em Agronomia (Entomologia Agrícola) - FCAV

Classificação e mapeamento de áreas de risco de enchentes e inundações no bairro Jardim Inocoop - Rio Claro - SP

In the last few decades, Brazil has experienced an accelerated urbanization process in which many cities have grown in a disorderly way occupying environmentally fragile areas unsuitable for habitation. Anthropogenic actions such as high levels of impermeable soil, structural changes in watercourses, lack of riparian vegetation, illegal presence of trash and rubbish along the river banks added to irregular settlements in floodplains result in the rise of high risk areas. When accompanied by intense and prolonged rainfall phenomena, those areas have been the scenery of serious accidents such as floods. This study aims to classify the level of the risk of floods in the neighborhood of Jardim Inocoop, in the town of Rio Claro, São Paulo countryside, Brazil. One of the main technical support to tackle this issue is the identification and classification of the risks. In order to classify the risk level of flood in this case study, the methodology adopted was developed by the Ministry of Cities and Technology Research Institute, and take into account the arrangement of the hydrological scenario, vulnerability of households and dangerous process according to the distance of the houses from the axis of drainage. Therefore, the risk levels adopted to classify are listed below: very high (MA), high risk (A), moderate (M) and low risk (B). In conclusion, it is imperative to develop prevention plans in order to avoid or to minimize the damages caused by natural disasters. Therefore, the zoning of the risk sceneries remains as an important issue once it helps to identify the areas with high level risk of flood. Consequently, the occupation must be regulated where there is low or absent risk and it must be often forbidden where the high risk of flood is detected. Thus, the present study remains as an attempt to notify the risk of floods through its spatialization on a map, remainig...(Complete abstract click electronic access below)

The implementation of the Medical Regulation Office and Mobile Emergency Attendance System and its impact on the gravity profile of non-traumatic afflictions treated in a University Hospital: a research study

Abstract Background The public health system of Brazil is structured by a network of increasing complexity, but the low resolution of emergency care at pre-hospital units and the lack of organization of patient flow overloaded the hospitals, mainly the ones of higher complexity. The knowledge of this phenomenon induced Ribeirão Preto to implement the Medical Regulation Office and the Mobile Emergency Attendance System. The objective of this study was to analyze the impact of these services on the gravity profile of non-traumatic afflictions in a University Hospital. Methods The study conducted a retrospective analysis of the medical records of 906 patients older than 13 years of age who entered the Emergency Care Unit of the Hospital of the University of São Paulo School of Medicine at Ribeirão Preto. All presented acute non-traumatic afflictions and were admitted to the Internal Medicine, Surgery or Neurology Departments during two study periods: May 1996 (prior to) and May 2001 (after the implementation of the Medical Regulation Office and Mobile Emergency Attendance System). Demographics and mortality risk levels calculated by Acute Physiology and Chronic Health Evaluation II (APACHE II) were determined. Results From 1996 to 2001, the mean age increased from 49 ± 0.9 to 52 ± 0.9 (P = 0.021), as did the percentage of co-morbidities, from 66.6 to 77.0 (P = 0.0001), the number of in-hospital complications from 260 to 284 (P = 0.0001), the mean calculated APACHE II mortality risk increased from 12.0 ± 0.5 to 14.8 ± 0.6 (P = 0.0008) and mortality rate from 6.1 to 12.2 (P = 0.002). The differences were more significant for patients admitted to the Internal Medicine Department. Conclusion The implementation of the Medical Regulation and Mobile Emergency Attendance System contributed to directing patients with higher gravity scores to the Emergency Care Unit, demonstrating the potential of these services for hierarchical structuring of pre-hospital networks and referrals.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Basic Erosive Wear Examination (BEWE): a new scoring system for scientific and clinical needs

A new scoring system, the Basic Erosive Wear Examination (BEWE), has been designed to provide a simple tool for use in general practice and to allow comparison to other more discriminative indices. The most severely affected surface in each sextant is recorded with a four level score and the cumulative score classified and matched to risk levels which guide the management of the condition. The BEWE allows re-analysis and integration of results from existing studies and, in time, should initiate a consensus within the scientific community and so avoid continued proliferation of indices. Finally, this process should lead to the development of an internationally accepted, standardised and validated index. The BEWE further aims to increase the awareness of tooth erosion amongst clinicians and general dental practitioners and to provide a guide as to its management.