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«FICHA TÉCNICA Título Segurança e Higiene Ocupacionais - SHO 2012 - Livro de Resumos Autores/Editores Arezes, P., Baptista, J.S., Barroso, M.P., ...»

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The Heating Ventilation and Air Conditioning Systems (HVAC) are a crucial way to obtain the quality levels in the interior of buildings, in terms of occupational hygiene, or to ensure thermal comfort. These systems ensure air exchanges, pressurisation, temperature control, and air humidity, being of utmost importance in healthcare facilities. Comfort evaluation give us not only information about how people are feeling, but also whether the HVAC systems are working effectively or need to be adjusted, in order to provide a more effective comfort situation. There are several ways to evaluate thermal comfort. The Predicted Mean Vote (PMV) is the most used thermal comfort index for situations near the comfort zone. It is a function of six variables and gives a value of thermal neutrality in a scale of seven points (ISO, 2005). The Actual Mean Vote (AMV), which is the subject assessment of the thermal environment, was also calculated through questionnaires, in order to complement the present study. The correspondent sample was constituted by 36 health professionals. Another emergent area for thermal comfort evaluation arrives with technology evolution, namely the computational simulation techniques as the Computational Fluid Dynamics (CFD). This technique is a preventive evaluation method and a tool for the building project. This method is however an approximation and has several difficulties. The human body is a complex system and, therefore, hard to modulate, which can drive to fluctuations in the solver equations system. Still, with some simplifications it is possible to obtain, in an efficient way, a good approximation to reality (Kilic & Sevilgen, 2008; Teixeira et al., 2010).

In this work, six thermal variables (air temperature, air velocity, mean radiant temperature, relative humidity, clothing insulation and metabolism) are measured for the calculation of PMV. Later, the AMV value is also measured and, as a final point, it is created a CFD model of the environment.

2. MATERIALS AND METHOD

The four environmental variables were measured using a climatic station from Brüel & Kjær, as seen in Figure 1. This device has several electronic sensors which quantify the environmental variables, allowing us to use the correspondent data. The sensors used in the station are an air temperature transducer, a surface temperature transducer, a radiant temperature asymmetry transducer, a humidity transducer and an air velocity transducer.

Several measures were undertaken until sensor’s stabilisation, and average values were then calculated. For the measurements of these variables, it was considered an operating room sample of the operatory block. This climatic station, however, does not directly measure the black globe temperature, and it was necessary to convert the radiant temperature in the black globe temperature. Personal variables were calculated through a questionnaire for the users of the operatory block for a bigger sample. This questionnaire was also used to find the AMV value. These data together with measures of the geometry of the operating room were used to create a CFD model.

Figure 1 – Climatic station used for the environmental measurements.

3. RESULTS AND DISCUSSION

The values for thermal variables are shown in Table 1. It was decided to consider two globe temperatures instead of a mean globe temperature due to the asymmetry of the considered places (below a light focus and out of the focus). The personal variables’ data are the average of the values obtained from the valid questionnaires.

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Using the above data in Malchaire’s Excel program (Malchaire, 2008), it was obtained a PMV value of 0.60 and 0.37 for below and out of the light focus, respectively, with corresponding PPD (Predicted Percentage of Dissatisfied) values of 13% and 8%. These results are explained by the different values for the globe temperature. It can be seen that there is a contrast between the two regions. In the case of being working under the focus, people are exposed to a slightly warm environment, and it should also be noted that who normally works at this place has a higher metabolic rate task than the average, which would inflate PMV value and consequently increase discomfort. These values are however contradictory with the expected ones, because, during the measurements, operating room users were complaining about the cold they felt, which is confirmed by AMV values It was expected that the result of the mean AMV thermal sensation would be between the two PMV values, once its mean value considers the people exposed in the two cases (below and out of the light focus). Instead, it was obtained an AMV value of -0.17 which represents a significant difference from the lower limit of the calculated PMV. The first point of divergence could be related with PMV calculation. There are studies that suggest a significant margin of error associated with PMV calculation (Orosa & Oliveira, 2011). Another point that could have led to this difference is that the used method to calculate clothing insulation and metabolism values was not good enough. This data were obtained using insulation and metabolic tables of ISO 7730 (ISO, 2005) and they require inquiring people about their tasks and cloth they are using. Another point that influences the results is the used sample. As a matter of fact, the chosen operating room, being a recent one, might not be a good sample of the entire block. Differences were detected, due basically to generated draughts in certain situations.





Focusing now on the AMV, there is the problem associated with people’s individuality. It is known that comfort differs from individual to individual, and one can feel comfortable being or not at thermal neutrality which can lead to an incorrect answer to the questionnaire. Another effect is that people tend to remember more the bad feelings rather than the good ones, and if one feels colder in some situations, his answer can also be influenced.

Other studies also suggest that thermal sensation is affected by regional climate (Orosa & Oliveira, 2011). Climate changes affect people thermal expectancy and their perception of comfort, which is not considered in PMV calculation.

The CFD model of the operating room is under development for the moment. An image of the model can be seen in Figure 2. At this point, some convergence problems with the velocity camp and the species transport model are being solved.

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4. CONCLUSIONS It can be concluded that thermal comfort is a complex subject and the PMV value does not always represent people’s thermal vote. It can also be concluded that the light focus creates an asymmetric local, which can generate discomfort. In

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this case, the problem occurs when the person who is below the focus has a high metabolic rate, once that the other variables are almost constant.

One point to consider in future work is the questionnaire’s improvement in order to lower the error of people’s answers.

A change that can be made lies in metabolism calculation, which can be made using more direct methods as oxygen consumption levels determination. For the future, this calculation should also include a time weighted parcel to obtain more precise data.

About the CFD simulation, it can be said that the model should be more simplified to obtain a solution that may be used in due time.

5. REFERENCES Alfano, F. R. d. A., Palella, B. I., & Riccio, G. (2011). The role of measurement accuracy on the thermal environment assessment by means of PMV index. ELSEVIER, 46 (Building and Environment), 1361-1369.

ISO. (2005). Ergonomics of the thermal environment – Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria (Vol. 7730). Geneva: International Standards Organization.

Kilic, M., & Sevilgen, G. (2008). Modelling airflow, heat transfer and moisture transport around a standing human body by computational fluid dynamics. 35(International Communications in Heat and Mass Transfer).

Malchaire, J. (2008, February of 2011). SOBANE Retrieved February, 2011, from http://www.deparisnet.be/chaleur/Chaleur.htm Orosa, J. A., & Oliveira, A. C. (2011). A new thermal comfort approach comparing adaptive and PMV models. Renewable Energy, 36 (3), 951-956. doi: 10.1016/j.renene.2010.09.013 Parsons, K.C., Human Thermal Environments: the effects of hot, moderates and cold environments on human health, comfort and performance. The principles and practice. 2ª ed. 2003, London: Taylor & Francis Ltd.

Teixeira, S., Leão, C., Neves, M., Arezes, P., Cunha, A., & Teixeira, J. (2010, 14 – 17 Jun 2010). Thermal Comfort Evaluation using a CFD Study and a Transient Thermal Model of the Human Body. Paper presented at the Fifth European Conference on Computational Fluid Dynamics, Lisbon, Portugal.

Segurança e Saúde Ocupacional do “Assistente Operacional da Área da Saúde”:

um Estudo de Caso Occupational Health and Safety for “Nursing Home Workers”: a case study Saavedra, Saloméa; Pinheiro, Francisco Alvesb; Rua, Aurac;Tato Diogo, Migueld a Universidade do Porto / MESHO / FEUP / Rua Dr. Roberto Frias, s/n 4200-465/ Porto/ Portugal/ +351 225 081 997 / salome.eng@gmail.com; b Universidade do Porto / DemSSO / FEUP / Rua Dr. Roberto Frias, s/n 4200-465/ Porto/ Portugal/ +351 225 400 407 / pee10019@fe.up.pt; c Universidade do Porto / MESHO / FEUP / Rua Dr. Roberto Frias, s/n 4200-465/ Porto/ Portugal/ +351 225 081 997 / aura.rua@gmail.com; dUniversidade do Porto / CIGAR Centro de Investigação em Geo-Ambiente e Recursos / FEUP Faculdade de Engenharia da Universidade do Porto / Rua Dr.

Roberto Frias, s/n 4200-465/ Porto/ Portugal/+351 225 081 997 / tatodiogo@fe.up.pt

1. INTRODUCÃO O Assistente Operacional que trabalha na área da saúde, nomeadamente o Ajudante de Lar de Idosos e Centros de Dia, é um profissional exposto a múltiplos fatores de risco durante uma normal jornada de trabalho: agentes biológicos, radiações, substâncias químicas, fatores causais de lesões musculares, stress, depressão, etc. Na realidade, os profissionais da área da saúde desenvolvem atividades de risco, sendo expostos diariamente a agentes de riscos de ordem biológica, química, ergonómica e psicossocial, que consequentemente contribuem para um grande número de acidentes de trabalho e doenças profissionais. Neste contexto, o ser humano é simultaneamente objeto e agente do cuidar, e enquanto agente humano, é passível de ser influenciado pelas características e elementos do ambiente de trabalho, ao nível do seu bem-estar físico, mental e social.

A prestação de cuidados em Clínica Geral/Medicina Familiar, engloba atividades que são desempenhadas várias vezes ao longo de um turno de trabalho, como a movimentação e transferência de doentes, de peso e grau de dependência diferentes. Um aspecto agravante e peculiar associado a estas atividades reside nas características intrínsecas da carga movimentada, nomeadamente a imprevisibilidade quanto à movimentação da mesma.

O objetivo do presente artigo é, apresentar um estudo de caso, relativo às diversas atividades realizadas por um Assistente Operacional de um lar de idosos, por forma a identificar fatores de riscos e tempos de exposição aos mesmos, com o intuito de inventariar medidas e atitudes preventivas relativas às doenças profissionais e acidentes de trabalho que afectam este grupo de profissionais.

2. MATERIAL E MÉTODOS O estudo de caso do trabalhador Assistente Operacional foi realizado num Lar de Idosos, recorrendo à observação participante, com o acompanhamento direto no seu local de trabalho, conseguindo-se efetivar a identificação dos fatores de risco predominantes neste tipo de atividade (agentes biológicos, físico, químico, movimentação manual de cargas e psicossocial), assim como as principais tarefas desenvolvidas pelo profissional que fomentam o desenvolvimento dos referidos factores de risco.

O trabalhador observado foi indicado pela Instituição por ser considerado representativo do tipo de trabalhadores que ali trabalham. Na realidade, dadas as características individuais, sociais e laborais, a organização considerou que o trabalhador indicado estaria em condições de espelhar questões de várias naturezas (social, organizacional, laboral) que, de algum modo, afetam os trabalhadores de um Lar de Idosos.

Como material de suporte, foi projetado e delineado um modelo de registo, com o objetivo de, à medida da visualização, apreensão e acompanhamento do trabalhador no seu local de trabalho ao longo das várias horas de cada turno, registar em tempo real todas as atividades realizadas, modos de execução das tarefas, tempos despendidos, equipamentos/utensílios utilizados, as interações sociais, as situações e/ou acontecimentos a que o trabalhador esteve sujeito durante os turnos observados. Quanto aos turnos observados, foram selecionados os três turnos representativos de um dia de trabalho: manhã, tarde e noite.

3. RESULTADOS E DISCUSSÕES

Para avaliação dos três turnos selecionados, efetuou-se a correlação atividade/tarefa desenvolvida e o respectivo(s) fator(es) de risco(s) presente(s), aferindo-se o tempo de exposição a que o trabalhador observado esteve exposto a determinado fator de risco.

Na realidade, cada turno observado abrange tarefas diferentes. No turno da manhã executam-se tarefas de higienização do idoso, levantar, vestir, calçar, colocá-lo na cadeira de rodas, empurrar a cadeira de rodas até ao elevador, fazer camas, arrumar roupas e limpar casas-de-banho. No turno da tarde, são administradas refeições aos idosos acamados (lanches e jantares), higienização dos idosos acamados e são deitados todos os idosos. O turno da noite, inicia-se às 0.00h com a limpeza das casas-de-banho públicas, corredores e zonas sociais, seguindo-se a vigia dos idosos, para às 6.00h da manhã começar a administrar o pequeno-almoço aos idosos.

Com a correlação tempos de exposição/fatores de risco, foram descodificadas as atividades/tarefas que conduzem a percentagens de tempos de exposição aos fatores de risco com valores significativos.

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Gráfico 1 – Representação gráfica dos tempos de exposição em cada turno (manhã, tarde e noite) a cada factor de risco.



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