Suchergebnisse

Mathematical modeling is very important when experimental settings with human subjects are restricted to thermal limits necessary to protect the individual. The predicted heat strain (PHS) model has been published AS ISO 7933 for about six years. It describes a method for predicting the sweat rate and internal core temperature that the human body will develop in response to the working conditions. The PHS model was developed based on thousands of laboratory and field experiments collected from eight European laboratories. However, most of the laboratory and field tests were performed on human subjects with light clothing ensembles (0.38±0.34 clo < Icl < 0.77±0.18 clo). The prediction of physiological responses while human wearing highly insulating protective clothing might be weak. In order to check the prediction accuracy of current PHS model while using protective clothing, we conducted totally series of human subject tests at a simulated hot environment. The results of 18 tests involving the high visibility (HV), military (MIL) and firefighting (FIRE) clothing are reported here. Six human subjects were asked to walk on a treadmill at 4.5 km/h at 40 oC for 70 min. Two humidity levels were chosen: 2 kPa (RH = 27 %) and 3 kPa (RH = 41 %) depending on the garment. The rectal temperature, skin temperature, heart rate and metabolic rate were measured. The clothing and the subjects were weighed before and after the exposure in order to calculate the sweat and evaporation rate. The observed and predicted rectal temperatures and mean skin temperatures were compared. The PHS model failed to predict the final rectal temperature in FIRE and the predicted estimate was 1.83 oC higher than the observed value after 63-min exposure. The predicted curve showed a much deeper linear increase during the whole exercise. None of the predicted mean skin temperatures during the three testing scenarios were accurately predicted. The PHS model was consistently providing conservative mean skin temperature evaluations. The predicted curve in HV and MIL showed a much shallower increase during the early portion of the exposure and plateaued at temperatures lower than ever achieved by the subjects. The observed sweat rates were 556±110 g/h in HV, 717±200 g/h in MIL, and 834±274 g/h in FIRE. There was no significant difference between the predicted total sweat values and the experimental data (P=0.073). In summary, the PHS model produce prediction of core temperature which has an unacceptable error when human wore thick protective clothing. The weak prediction on the mean skin temperature in HV and MIL was in agreement with the empirical prediction equation in the source codes has the poorest and lowest correlation when a clothed human subject exercised at the humidity level above 2 kPa. It is therefore recommended that the PHS model should be amended to development and validated by manipulation of individual algorithms or physical (or physiological) parameters.

Cold protective clothing was studied in 2 European Union projects. The objectives were (a) to examine different insulation calculation methods as measured on a manikin (serial or parallel), for the prediction of cold stress (IREQ); (b) to consider the effects of cold protective clothing on metabolic rate; (c) to evaluate the movement and wind correction of clothing insulation values. Tests were carried out on 8 subjects. The results showed the possibility of incorporating the effect of increases in metabolic rate values due to thick cold protective clothing into the IREQ model. Using the higher thermal insulation value from the serial method in the IREQ prediction, would lead to unacceptable cooling of the users. Thus, only the parallel insulation calculation method in EN 342:2004 should be used. The wind and motion correction equation (No. 2) gave realistic values for total resultant insulation; dynamic testing according to EN 342:2004 may be omitted. ; Validerad; 2007; 20070904 (pafi)

Abstract
Climate change is adversely impacting the burden of diarrheal diseases. Despite significant reduction in global prevalence, diarrheal disease remains a leading cause of morbidity and mortality among young children in low- and middle-income countries. Previous studies have shown that diarrheal disease is associated with meteorological conditions but the role of large-scale climate phenomena such as El Niño-Southern Oscillation (ENSO) and monsoon anomaly is less understood. We obtained 13 years (2002–2014) of diarrheal disease data from Nepal and investigated how the disease rate is associated with phases of ENSO (El Niño, La Niña, vs. ENSO neutral) monsoon rainfall anomaly (below normal, above normal, vs. normal), and changes in timing of monsoon onset, and withdrawal (early, late, vs. normal). Monsoon season was associated with a 21% increase in diarrheal disease rates (Incident Rate Ratios [IRR]: 1.21; 95% CI: 1.16–1.27). El Niño was associated with an 8% reduction in risk while the La Niña was associated with a 32% increase in under-5 diarrheal disease rates. Likewise, higher-than-normal monsoon rainfall was associated with increased rates of diarrheal disease, with considerably higher rates observed in the mountain region (IRR 1.51, 95% CI: 1.19–1.92). Our findings suggest that under-5 diarrheal disease burden in Nepal is significantly influenced by ENSO and changes in seasonal monsoon dynamics. Since both ENSO phases and monsoon can be predicted with considerably longer lead time compared to weather, our findings will pave the way for the development of more effective early warning systems for climate sensitive infectious diseases.

This guide presents a joint effort of projects funded under the European Research Area for Climate Services (ERA4CS) (http://www.jpi-climate.eu/ERA4CS), a co- funded action initiated by JPI Climate with co-funding by the European Union (Grant 690462), 15 national public Research Funding Organisations (RFOs), and 30 Research Performing Organisations (RPOs) from 18 European countries. This guide sets out to increase the understanding of different pathways, methods, and approaches to improve knowledge co-production of climate services with users as a value-added activity of the ERA4CS Programme. Reflecting on the experiences of 16 of the 26 projects funded under ERA4CS, this guide aims to define and recommend good practices for transdisciplinary knowledge co-production of climate services to researchers, users, funding agencies, and private sector service providers. Drawing on responses from ERA4CS project teams to a questionnaire and interviews, this guide maps the diversity of methods for stakeholder identification, involvement, and engagement. It also conducts an analysis of methods, tools, and mechanisms for engagement as well as evaluation of co-production processes. This guide presents and discusses good practice examples based on the review of the ERA4CS projects, identifying enablers and barriers for key elements in climate service co-production processes. These were: namely (i) Forms of Engagement; (ii) Entry Points for Engagement; and, (iii) Intensity of Involvement. It further outlines key ingredients to enhance the quality of co-producing climate services with users and stakeholders. Based on the analysis of the lessons learned from ERA4CS projects, as well as a review of key concepts in the recent literature on climate service co-production, we provide a set of recommendations for researchers, users, funders and private sector providers of climate services. ; This report is not externally peer-reviewed Revisions: 2022-02-11 The report PDF was published. 2022-05-06 An incorrect spelled author name was ...