Proposal of Combined Noise and Hand-Arm Vibration Index for Occupational Exposure: Application to a Study Case in the Olive Sector.

In many production and industrial sectors, workers are exposed to noise and hand-arm vibrations (HAV). European directives have established the maximum limit values or exposure action values for noise and vibration independently. However, in many cases, workers who endure hand-arm vibration also receive high noise levels. This research suggests a procedure to aid the establishment of precautionary measures for workers with simultaneous exposure to both physical agents. This procedure defines a combined index based on the energy doses for both noise and HAV. From this combined index, the suggested methodology allows a recommended exposure time for workers with simultaneous noise and HAV exposure to be calculated. This methodology can be adapted to tackle the relative importance assigned to both agents according to the safety manager and new knowledge on combined health effects. To test this method, a measurement campaign under real working conditions was conducted with workers from the olive fruit-harvesting sector, where a variety of hand-held machinery is used. The results of the study case show that the suggested procedure can obtain reliable exposure time recommendations for simultaneous noise and HAV exposures and is therefore a useful tool for establishing prevention measures.

Monitoring and Assessment of Indoor Environmental Conditions in Educational Building Using Building Information Modelling Methodology.

Managing indoor environmental quality (IEQ) is a challenge in educational buildings in the wake of the COVID-19 pandemic. Adequate indoor air quality is essential to ensure that indoor spaces are safe for students and teachers. In fact, poor IEQ can affect academic performance and student comfort. This study proposes a framework for integrating occupants' feedback into the building information modelling (BIM) methodology to assess indoor environmental conditions (thermal, acoustic and lighting) and the individual airborne virus transmission risk during teaching activities. The information contained in the parametric 3D BIM model and the algorithmic environment of Dynamo were used to develop the framework. The IEQ evaluation is based on sensor monitoring and a daily schedule, so the results show real problems of occupants' dissatisfaction. The output of the framework shows in which range the indoor environmental variables were (optimal, acceptable and unacceptable) and the probability of infection during each lecture class (whether or not 1% is exceeded). A case study was proposed to illustrate its application and validate it. The outcomes provide key information to support the decision-making process for managing IEQ and controlling individual airborne virus transmission risks. Long-term application could provide data that support the management of ventilation strategies and protocol redesign.

Modelling and visualization for the analysis and comprehension of the acoustic performance of buildings through the implementation of a building information modelling-based methodology.

Technical and technological advances have revolutionised the architecture, engineering, and construction industries in recent decades. Building information modelling (BIM) methodology has become essential in the process of information management and the development of building projects. This study aims to analyse the potential advantages of the implementation of BIM-based models for the acquisition of theoretical and procedural knowledge about building acoustics. This procedure was implemented as part of a problem-solving exercise in Science, Technology, Engineering, and Mathematics (STEM) university degrees. For this purpose, three-dimensional (3D) BIM models were generated to assess the contribution of their implementation in the process of visualization, comprehension, and analysis of the acoustic behaviour of buildings. The participants' experiences and satisfaction with the BIM models were measured through a questionnaire. The results showed a high level of satisfaction among the participants and good potential for the application of 3D models based on BIM methodology for the acquisition of knowledge and practical skills in building acoustics. These results highlight the potential of BIM models to provide information for understanding the procedure followed during data collection in the experimental analysis and to facilitate the understanding of system behavior.

Reopening higher education buildings in post-epidemic COVID-19 scenario: monitoring and assessment of indoor environmental quality after implementing ventilation protocols in Spain and Portugal.

Post-epidemic protocols have been implemented in public buildings to keep indoor environments safe. However, indoor environmental conditions are affected by this decision, which also affect the occupants of buildings. This fact has major implications in educational buildings, where the satisfaction and learning performance of students may also be affected. This study investigates the impact of post-epidemic protocols on indoor environmental conditions in higher education buildings of one Portuguese and one Spanish university. A sensor monitoring campaign combined with a simultaneous questionnaire was conducted during the reopening of the educational buildings. Results showed that although renewal air protocols were effective and the mean CO2 concentration levels remained low (742 ppm and 519 ppm in Portugal and Spain universities, respectively), students were dissatisfied with the current indoor environmental conditions. Significant differences were also found between the responses of Portuguese and Spanish students. Indeed, Spanish students showed warmer preferences (thermal neutrality = 23.3℃) than Portuguese students (thermal neutrality = 20.7℃). In terms of involved indoor factors, the obtained data showed significant correlations (p < 0.001) between acoustic factors and overall satisfaction in the Portuguese students (ρ = 0.540) and between thermal factors and overall satisfaction in the Spanish students (ρ = 0.522). Therefore, indoor environmental conditions should be improved by keeping spaces safe while minimizing the impact of post-epidemic protocols on student learning performance.

Whole Body Vibration Exposure Transmitted to Drivers of Heavy Equipment Vehicles: A Comparative Case According to the Short- and Long-Term Exposure Assessment Methodologies Defined in ISO 2631-1 and ISO 2631-5.

The construction and transport sectors are the industries with the highest proportions of workers exposed to vibrations in the European Union. Heavy equipment vehicle (HEV) drivers often perform operations on different uneven surfaces and are exposed to whole body vibration (WBV) on a daily basis. Recently, a new version of ISO 2631-5 was published. However, since this new method required as input the individual exposure profile and the acceleration signals recorded on more surfaces, limited studies have been carried out to evaluate HEV operations according to this standard. The objectives of this study were to assess the WBV exposure using the methods defined in ISO 2631-1:1997 and ISO 2631-5:2018 and to compare the obtained health risk assessments between drivers with different anthropometric characteristics. For this purpose, two drivers were selected and a field measurement campaign was conducted. Regarding short-term assessment, results showed that VDV was the most restrictive method with exposure levels above the exposure action limit value, while SdA indicated that the same exposures were safe for the worker. With respect to long-term assessment, Risk Factor RA showed that the driver with the highest body mass index was the only one who exceeded the low probability limit of adverse health effects.

Monitoring and Assessment of Indoor Environmental Conditions after the Implementation of COVID-19-Based Ventilation Strategies in an Educational Building in Southern Spain.

Since students and teachers spend much of their time in educational buildings, it is critical to provide good levels of indoor environmental quality (IEQ). The current COVID-19 pandemic has shown that maintaining a good indoor air quality level is an effective measure to control the transmission of the SARS-CoV-2 virus. This study used sensors to monitor key IEQ factors and assess several natural ventilation scenarios in a classroom of the University of Granada. Subsequently, the IEQ factors (temperature, relative humidity, CO2 concentration, acoustic environment, and air velocity) were evaluated for the selected ventilation scenarios in the occupied classroom, and the field monitoring was carried out in two different assessment periods, winter and summer. The obtained results show that the CO2 concentration levels were well below the recommended limits. However, the maintenance of the recommended thermal and acoustic IEQ factors was significantly affected by the natural ventilation strategies (temperature and relative humidity values were very close to the outside values, and the background sound pressure level was over 35 dBA during the entire assessment). The proper measurements and careful selection of the appropriate ventilation scenarios become of utmost importance to ensure that the ventilation rates required by the health authorities are achieved.

Analysis of Impact of Natural Ventilation Strategies in Ventilation Rates and Indoor Environmental Acoustics Using Sensor Measurement Data in Educational Buildings.

Indoor environmental conditions can significantly affect occupants' health and comfort. These conditions are especially important in educational buildings, where students, teachers and staff spend long periods of the day and are vulnerable to these factors. Recently, indoor air quality has been a focus of attention to ensure that disease transmission in these spaces is minimised. In order to increase the knowledge in this field, experimental tests have been carried out to characterise the impact of natural ventilation strategies on indoor air quality and the acoustic environment. This study has evaluated three ventilation scenarios in four different classrooms in buildings of the University of Granada, considering different window and door opening configurations. Ventilation rates were estimated using the CO2 Decay Method, and background noise recordings were made in each classroom for acoustic tests. Results show that specific natural ventilation strategies have a relevant impact that is worth considering on the background noise in indoor spaces. In this sense ventilation rates provided by the different configurations varied between 3.7 and 39.8 air changes per hour (ACH) and the acoustic tests show a background noise ranging from 43 to 54 dBA in these scenarios. Consequently, managers and teachers should take into account not only the ACH, but also other collateral impacts on the indoor environmental conditions such as the thermal comfort or the acoustic environment.

A methodology for assessment of long-term exposure to whole-body vibrations in vehicle drivers to propose preventive safety measures.

INTRODUCTION: The appearance of musculoskeletal disorders (MDs) in professional drivers due to exposition to whole-body vibration (WBV) makes it relevant to assess this exposure. The European Directive 2002/44/EC has two methods to evaluate exposure to WBV (defined in ISO2631-1:2008). These methods evaluate the exposure associated with an 8-hour working day; however, MDs due to WBV could also be caused by accumulated exposure to vibrations over long term, and hence, the methods defined in the European directive may be limited in their ability to ensure the safety of workers exposed to WBV throughout their years of employment. METHOD: A detailed comparison and discussion of methods defined in the European Directive and the ISO2631-5:2018 was used as a starting point of the main results of this paper. On this basis, a new methodology for the management and organization of preventive measures is proposed to consider the assessment of ISO2631-5:2018 standard and the full working life of workers. Experimental data to assess exposure to WBV in heavy equipment vehicle (HEV) drivers under different road surface conditions and range of velocities were considered to illustrate the process of the proposed methodology. RESULTS: The methods defined in the standards provide different assessments leading to a different possible consideration of safe operations when the risks associated with them may actually be high. The proposed methodology can be used with the aim of ensuring safety of workers throughout their working lives and providing an easy implementation of the calculations of ISO2631-5:2018 standard. CONCLUSIONS: A procedure to assess the health risk probability to which the HEV worker is exposed in terms of the exposure years and a different range of operational vehicle speeds is proposed and exemplified with a study case. Practical applications: This study provides a practical tool for the management of WBV exposure related to work-tasks in HEV drivers. Safety managers should consider the global exposition to WBV throughout their working life, and this research provides an easy tool to accomplish it.

Social choice functions: A tool for ranking variables involved in action plans against road noise.

Traffic noise is gaining importance in planning and operation of roads in developing countries, and particularly in Europe and Latin America. Many variables with different degrees of importance influence the perception of noise from roads. Thus, the problem of prioritizing road stretches for action against such noise is an important issue in environmental noise management. For example, it can be addressed using multicriteria methods. However, these methodologies require criteria or suitable variables to be ranked according to their relative importance. In the present study, for this ranking, a list of nine variables involved in the decision-making process (called "road stretch priority variables") was presented in the form of questionnaires to high-level experts from Andalusia, southern Spain. These experts ranked the variables by relevance. Using the same data, seven social choice functions (Plurality, Raynaud, Kemeny-Young, Copeland, Simpson, Schulze, and Borda) were used in order to rank the variables. The results indicate that the most important variables were those that take into account the parameters of greatest exposure for the citizens, followed by variables related to the intensity of the problem analyzed. The results show that a combination of the use of social choice functions on aggregated information from expert panels can provide a consensus for ranking priority variables related to road stretches.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)).

A Rao-Blackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predator-prey system.

Functional response estimation and population tracking in predator-prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle filtering method for: (a) estimating the behavioral parameter representing the rate of effective search per predator in the functional response and (b) forecasting the population biomass using field data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.

A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).

Application of a methodology for categorizing and differentiating urban soundscapes using acoustical descriptors and semantic-differential attributes.

A subjective and physical categorization of an ambient sound is the first step to evaluate the soundscape and provides a basis for designing or adapting this ambient sound to match people's expectations. For this reason, the main goal of this work is to develop a categorization and differentiation analysis of soundscapes on the basis of acoustical and perceptual variables. A hierarchical cluster analysis, using 15 semantic-differential attributes and acoustical descriptors to include an equivalent sound-pressure level, maximum-minimum sound-pressure level, impulsiveness of the sound-pressure level, sound-pressure level time course, and spectral composition, was conducted to classify soundscapes into different typologies. This analysis identified 15 different soundscape typologies. Furthermore, based on a discriminant analysis the acoustical descriptors, the crest factor (impulsiveness of the sound-pressure level), and the sound level at 125 Hz were found to be the acoustical variables with the highest impact in the differentiation of the recognized types of soundscapes. Finally, to determine how the different soundscape typologies differed from each other, both subjectively and acoustically, a study was performed.

Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400 Hz and 1-2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%.

Characterization of electron contamination in megavoltage photon beams.

The purpose of the present study is to characterize electron contamination in photon beams in different clinical situations. Variations with field size, beam modifier (tray, shaping block) and source-surface distance (SSD) were studied. Percentage depth dose measurements with and without a purging magnet and replacing the air by helium were performed to identify the two electron sources that are clearly differentiated: air and treatment head. Previous analytical methods were used to fit the measured data, exploring the validity of these models. Electrons generated in the treatment head are more energetic and more important for larger field sizes, shorter SSD, and greater depths. This difference is much more noticeable for the 18 MV beam than for the 6 MV beam. If a tray is used as beam modifier, electron contamination increases, but the energy of these electrons is similar to that of electrons coming from the treatment head. Electron contamination could be fitted to a modified exponential curve. For machine modeling in a treatment planning system, setting SSD at 90 cm for input data could reduce errors for most isocentric treatments, because they will be delivered for SSD ranging from 80 to 100 cm. For very small field sizes, air-generated electrons must be considered independently, because of their different energetic spectrum and dosimetric influence.