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20 pages, 6816 KiB  
Article
Mapping Noise from Motorised Transport in the Context of Infrastructure Management
by Piotr Jaskowski, Marcin Koniak, Jonas Matijošius and Artūras Kilikevičius
Appl. Sci. 2025, 15(3), 1277; https://doi.org/10.3390/app15031277 - 26 Jan 2025
Viewed by 625
Abstract
Noise pollution presents significant challenges for urban infrastructure management, highlighting the need for practical assessment tools such as noise maps. These maps enable the visualization and geo-referencing of noise levels, identifying areas requiring immediate intervention and long-term strategic responses. Road sections with traffic [...] Read more.
Noise pollution presents significant challenges for urban infrastructure management, highlighting the need for practical assessment tools such as noise maps. These maps enable the visualization and geo-referencing of noise levels, identifying areas requiring immediate intervention and long-term strategic responses. Road sections with traffic exceeding 3 million vehicles per year were selected for measurement. The findings are presented in detail, revealing that the Long-term Day-Night Average Noise Level (Lden) exceeds acceptable limits, affecting approximately 1.899 km2 and impacting around 1200 residents within the exceedance zone. Similarly, the equivalent noise level (LAeq) surpasses acceptable thresholds over an area of 1.220 km2, affecting an additional 700 residents. Notably, there were no exceedances of the key noise impact indicators, including high annoyance (HA), high sleep disturbance (HSD), and ischemic heart disease (IHD). Changes in traffic organisation were implemented to address areas that exceed the applicable noise standards, including a ban on trucks and the introduction of local speed limits. The measures have successfully mitigated the noise problem in Grodzisk County (Poland). Further anti-noise initiatives are planned, including planting vegetation along the roadways. Full article
(This article belongs to the Section Acoustics and Vibrations)
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<p>Noise emission map showing noise expressed by L<sub>den.</sub></p>
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<p>Noise emission map showing noise expressed by L<sub>den.</sub></p>
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<p>Noise emission map showing noise expressed by L<sub>den.</sub></p>
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<p>Emission map showing noise expressed as L<sub>Aeq.</sub></p>
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<p>Emission map showing noise expressed as L<sub>Aeq.</sub></p>
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<p>Emission map showing noise expressed as L<sub>Aeq.</sub></p>
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<p>Map of acoustically protected areas with permissible noise levels expressed in L<sub>den</sub> and L<sub>Aeq</sub> indicators.</p>
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<p>Map of acoustically protected areas with permissible noise levels expressed in L<sub>den</sub> and L<sub>Aeq</sub> indicators.</p>
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<p>Map of acoustically protected areas with permissible noise levels expressed in L<sub>den</sub> and L<sub>Aeq</sub> indicators.</p>
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<p>Map of noise-prone areas in which the permissible noise levels expressed in L<sub>den</sub> are exceeded.</p>
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<p>Map of noise-prone areas in which the permissible noise levels expressed in L<sub>den</sub> are exceeded.</p>
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<p>Map of noise-prone areas in which the permissible noise levels expressed in L<sub>den</sub> are exceeded.</p>
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11 pages, 1174 KiB  
Article
Unilateral Versus Bilateral Cochlear Implants in Adults: A Cross-Sectional Questionnaire Study Across Multiple Hearing Domains
by Alessandra Pantaleo, Luigi Curatoli, Giada Cavallaro, Debora Auricchio, Alessandra Murri and Nicola Quaranta
Audiol. Res. 2025, 15(1), 6; https://doi.org/10.3390/audiolres15010006 - 20 Jan 2025
Viewed by 604
Abstract
Aim: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations—unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)—focusing on their perception of speech in quiet and noisy environments, music, environmental [...] Read more.
Aim: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations—unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)—focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people’s voices and tinnitus. Methods: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale. Statistical analyses were performed to compare the subjective experiences of the three groups. Results: Patients reported that understanding speech in noise and tinnitus perception were their main concerns. BCI users experienced fewer difficulties with understanding speech in both quiet (p < 0.001) and noisy (p = 0.008) environments and with perceiving non-vocal sounds (p = 0.038) compared to UCI and BM users; no significant differences were found for music perception (p = 0.099), tinnitus perception (p = 0.397), or voice naturalness (p = 0.157). BCI users also reported less annoyance in quiet (p = 0.004) and noisy (p = 0.047) environments, and in the perception of voices (p = 0.009) and non-vocal sounds (p = 0.019). Tinnitus-related psychological impact showed no significant differences between groups (p = 0.090). Conclusions: Although speech perception in noise and tinnitus remain major problems for CI users, the results of our study suggest that bilateral cochlear implantation offers significant subjective advantages over unilateral implantation and bimodal stimulation in adults, particularly in difficult listening environments. Full article
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<p>Mean questionnaire scores by hearing domain across user group. Bar chart showing mean scores of the questionnaire for UCI, BCI, and BM users. Part A assesses auditory difficulty, while Part B evaluates psychological impact. Error bars represent the standard deviation.</p>
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<p>Violin plots comparing scores for each item across UCI (unilateral cochlear implant), BM (bimodal stimulation), and BCI (bilateral cochlear implant) users, divided into Part A (<b>left</b>) and Part B (<b>right</b>). Medians are highlighted in black.</p>
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8 pages, 234 KiB  
Article
The Tinnitus Handicap Inventory Total Score: What Really Counts? Experience on a Sample of 1156 Patients
by Roberto Teggi, Iacopo Cangiano, Marco Familiari, Vittorio Gioffrè, Alessandro Nobile and Omar Gatti
Audiol. Res. 2025, 15(1), 4; https://doi.org/10.3390/audiolres15010004 - 16 Jan 2025
Viewed by 518
Abstract
Background: Tinnitus is a frequent symptom, and is present in 10–15% of people who suffer from chronic tinnitus, defined as heard every day for at least 6 months. Among these, 1–2% develop a strong emotive reaction, anxiety, and depression, leading to poor quality [...] Read more.
Background: Tinnitus is a frequent symptom, and is present in 10–15% of people who suffer from chronic tinnitus, defined as heard every day for at least 6 months. Among these, 1–2% develop a strong emotive reaction, anxiety, and depression, leading to poor quality of life. Objectives: to evaluate the comorbidities in tinnitus sufferers. Methods: In our retrospective study, we collected data on 1156 subjects with tinnitus present for at least 3 months, including age, audiometric exam, THI questionnaire, vascular disorders, fluctuations, causal factors, lifetime psychiatric disorders, and the presence of migraine. A linear regression model was used to assess the independent role of these variables on the THI total score representing tinnitus annoyance. A lifetime history of psychiatric disorders and migraine were predictive for the development of a disabling tinnitus. Results: Among comorbidities a history of previous psychiatric disorders was predictive for developing tinnitus. Moreover, no correlation has been found between hearing level and THI total score. Conclusions: Our data are not inconsistent with the hypothesis that psychological disorders and a particular personality trait may be the main causal factors for tinnitus annoyance. Full article
16 pages, 5641 KiB  
Article
Research on Battery Electric Vehicles’ DC Fast Charging Noise Emissions: Proposals to Reduce Environmental Noise Caused by Fast Charging Stations
by David Clar-Garcia, Hector Campello-Vicente, Miguel Fabra-Rodriguez and Emilio Velasco-Sanchez
World Electr. Veh. J. 2025, 16(1), 42; https://doi.org/10.3390/wevj16010042 - 14 Jan 2025
Cited by 2 | Viewed by 817
Abstract
The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range [...] Read more.
The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range and charging times, which significantly impact the time needed for a trip compared to their combustion engine counterparts. However, recent improvements in fast charging technology have enhanced these aspects, making EVs more suitable for both daily and long-distance trips. EVs can now deal with long trips, with travel times only slightly longer than those of internal combustion engine (ICE) vehicles. Fast charging capabilities and infrastructure, such as 350 kW chargers, are essential for making EV travel times comparable to ICE vehicles, with brief stops every 2–3 h. Additionally, EVs help reduce noise pollution in urban areas, especially in noise-saturated environments, contributing to an overall decrease in urban sound levels. However, this research highlights a downside of DC (Direct Current) fast charging stations: high-frequency noise emissions during fast charging, which can disturb nearby residents, especially in urban and residential areas. This noise, a result of the growing fast charging infrastructure, has led to complaints and even operational restrictions for some charging stations. Noise-related disturbances are a significant urban issue. The World Health Organization identifies noise as a key contributor to health burdens in Europe, even when noise annoyance is subjective, influenced by individual factors like sensitivity, genetics, and lifestyle, as well as by the specific environment. This paper analyzes the sound emission of a broad sample of DC fast charging stations from leading EU market brands. The goal is to provide tools that assist manufacturers, installers, and operators of rapid charging stations in mitigating the aforementioned sound emissions in order to align these infrastructures with Sustainable Development Goals 3 and 11 adopted by all United Nations Member States in 2015. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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<p>Ingeteam Rapid 50 Trio (<b>left</b>) and Ingeteam Rapid 180 (<b>right</b>) fast charging stations.</p>
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<p>Microphone set-up. Front view.</p>
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<p>Microphone set-up. Side view.</p>
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<p>Ingeteam 50 kW fast charging station (<b>left</b>) and Tesla V2 Supercharger 150 kW (<b>right</b>).</p>
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<p>Iberdrola’s Smart Mobility LAB, Bilbao (Spain).</p>
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<p>Measured and corrected sound pressure levels for all DC fast chargers tested.</p>
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<p>Noise emission spectra for all DC fast chargers tested.</p>
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<p>Average noise emission spectra for fast chargers tested according to their rated power.</p>
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<p>Comparison between different first-generation Ingeteam Rapid (50 kW) fast chargers noise emission spectra.</p>
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<p>Ingeteam Rapid 50 kW fast charging station with a sound-dampening device.</p>
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<p>Noise emission spectra of different Ingeteam Rapid 50 kW fast charging stations.</p>
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<p>Sound propagation of the Ingeteam Rapid 50 kW fast charging station LA<sub>eq</sub> (dBA).</p>
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<p>Sound propagation of the Ingeteam Rapid 50 kW fast charging station, front view (<b>left</b>) and side view (<b>right</b>).</p>
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18 pages, 2059 KiB  
Article
Estimation of the Occurrence and Significance of Noise Effects on Pedestrians Using Acoustic Variables Related to Sound Energy in Urban Environments
by Juan Miguel Barrigón Morillas, David Montes González, Rosendo Vílchez-Gómez and Guillermo Rey-Gozalo
Appl. Sci. 2024, 14(23), 11212; https://doi.org/10.3390/app142311212 - 2 Dec 2024
Viewed by 686
Abstract
The impact of environmental noise on the health and well-being of people living in cities is an issue that has been addressed in the scientific literature to try to develop effective environmental policies. In this context, road traffic is the main source of [...] Read more.
The impact of environmental noise on the health and well-being of people living in cities is an issue that has been addressed in the scientific literature to try to develop effective environmental policies. In this context, road traffic is the main source of noise in urban environments, but it is not the only source of noise that pedestrians hear. This paper presents an experimental study using in situ surveys and acoustic measurements to analyse the capacity of acoustic variables related to sound energy to estimate the occurrence and importance of noise effects in urban environments. The results revealed that average sound energy indicators can be considered most significant in terms of the perception of the noise effects studied on pedestrians. When estimating noise effects from them, frequency weightings related to flat or nearly flat spectra (Z and C weightings) were found to provide better results than an A weighting; however, it was also concluded that if the average energy is considered, the use of a temporal I weighting did not lead to improvements. The perception of how noisy a street is, it is strongly associated with a low frequency, and annoyance was the effect that generally showed the strongest significant correlations with acoustic indicators. The indicators of minimum sound levels explained a larger proportion of the variability of noise effects than the indicators of maximum energy; they were even better in this regard than any of the average energy indicators in terms of explaining the variability of startle and annoyance in the ears, and they were found to be equivalent when interruption of a telephone conversation was assessed. Both acoustic variables associated with sound energy in different parts of the audible spectrum and Leq in each one-third octave band showed significant correlations with the effects of noise on pedestrians. Similarities in the structure of the spectra were found between some of these effects. Full article
(This article belongs to the Special Issue Recent Advances in Soundscape and Environmental Noise)
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<p>Survey points in Cáceres (from Google Earth).</p>
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<p>A box plot of the answers for the subjective variables (a)–(i).</p>
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<p>One-third octave band noise profile of the measurements at the different points considered.</p>
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<p>Correlation coefficients between subjective variables and the equivalent unweighted noise level for each of the 1/3 octave frequency bands: (<b>A</b>) variables (a), (b), (c), (h) and (i); (<b>B</b>) variables (d), (e), (f), and (g).</p>
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29 pages, 3406 KiB  
Article
Comparison of Prediction Models for Sonic Boom Ground Signatures Under Realistic Flight Conditions
by Jacob Jäschke, Samuele Graziani, Francesco Petrosino, Antimo Glorioso and Volker Gollnick
Aerospace 2024, 11(12), 962; https://doi.org/10.3390/aerospace11120962 - 22 Nov 2024
Viewed by 1152
Abstract
This paper presents a comparative analysis of simplified and high-fidelity sonic boom prediction methods to assess their applicability in the conceptual design of supersonic aircraft. The high-fidelity approach combines Computational Fluid Dynamics (CFD) for near-field shock analysis with ray-tracing and the Augmented Burgers [...] Read more.
This paper presents a comparative analysis of simplified and high-fidelity sonic boom prediction methods to assess their applicability in the conceptual design of supersonic aircraft. The high-fidelity approach combines Computational Fluid Dynamics (CFD) for near-field shock analysis with ray-tracing and the Augmented Burgers Equation for far-field propagation through a non-uniform atmosphere, whereas the simplified Carlson method uses analytical approximations for rapid predictions. The comparison across selected climb, cruise, and descent conditions for a supersonic reference aircraft shows that the Carlson method captures general trends in sonic boom behavior, such as changes in peak overpressure and signal duration with varying Mach number and altitude. However, significant deviations are noted under realistic atmospheric conditions, highlighting limitations in the simplified model’s accuracy. Common psycho-acoustic metrics were evaluated to assess the potential annoyance on the ground. The results demonstrate that while the simplified method is effective for early-stage design assessments, the high-fidelity model is essential for precise sonic boom characterization under realistic conditions, particularly for regulatory and community impact evaluations. Full article
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<p>Simulation domains for state-of-the-art sonic boom prediction. The near-field domain is computed via CFD methods while the far-field domain accounts for the atmospheric variations and is modeled with a combination of ray-tracing and a non-linear wave equation. Adapted from [<a href="#B16-aerospace-11-00962" class="html-bibr">16</a>].</p>
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<p>Details of the CFD computational domain. (<b>a</b>) Unstructured and structured region of the CFD near-field mesh. (<b>b</b>) Computational domain of the CFD near-field simulation.</p>
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<p>Definition of the ray-tracing azimuth angles <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> around an aircraft flying towards the reader. Positive azimuth angles are defined towards port side, negative azimuth angles towards starboard side.</p>
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<p>Geometry model of CS1.</p>
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<p>CS1 mission profile and associated Angle of Attack.</p>
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<p>Properties of the three reference atmospheres for the current study. The pressure profiles of both cases of SBPW3 are almost identical, such that the orange line is hidden by the green one in the pressure plot. The wind velocities in both directions are zero for the windless ISA case.</p>
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<p>Extracted near-field pressure signatures from the CFD solution at a radial distance of <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>1</mn> <mi>L</mi> <mo>=</mo> <mn>62</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> for the three operating conditions of CS1. The azimuth angle of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math> shows the extracted pressure signal below the aircraft (on-track). The right plot shows an exemplary pressure signature extracted at off-track conditions for an azimuth angle of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>40</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Computed primary sonic boom footprint for CS1 flying towards east. The marks depict the ground intersection points of the computed rays. The labels show the values of the limiting azimuth angles <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>. <b>ISA</b> shows the footprint in the windless International Standard Atmosphere, <b>SBPW3 Case 1</b> for the realistic atmosphere from the Third Sonic Boom Prediction Workshop (SBPW3) with a wide range of azimuth angles, and <b>SBPW3 Case 2</b> the SBPW3 case for a realistic atmosphere that is intended to result in a particular wide carpet.</p>
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<p>Computed shock wave pressure signatures at ground level for the three operating conditions of CS1 in the International Standard Atmosphere. The azimuth angle of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math> shows the on-track sonic boom and the right plot shows an exemplary off-track sonic boom, computed for an azimuth angle of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>40</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Computed shock wave pressure signatures at ground level for the three operating conditions of CS1 in the realistic atmospheres of the Third Sonic Boom Prediction Workshop (SBPW3). The left plot depicts case 1 and the right plot depicts case 2. The azimuth angle of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math> is plotted with solid lines. Dash-dotted lines show an exemplary off-track sonic boom at an azimuth angle of <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>40</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Computed acoustic sonic boom carpet as observed at ground level for the three operating conditions of CS1 flying towards east (i.e., towards the reader) in the International Standard Atmosphere. The left plot shows the distribution of peak amplitudes over the sonic boom carpet, and the right plot shows the distribution of Stevens’ Perceived Level of Noise Mk VII.</p>
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<p>Computed sonic boom carpet at ground level for the three operating conditions of CS1 flying towards east in the first realistic atmosphere of SBPW3. The left plot shows the distribution of peak amplitudes over the sonic boom carpet, and the right plot shows the distribution of Stevens’ Perceived Level of Noise Mk VII.</p>
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<p>Computed sonic boom carpet at ground level for the three operating conditions of CS1 flying towards the east in the second realistic atmosphere of SBPW3. The left plot shows the distribution of peak amplitudes over the sonic boom carpet, and the right plot shows the distribution of Stevens’ Perceived Level of Noise Mk VII.</p>
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25 pages, 5612 KiB  
Article
Innovative Approaches to Industrial Odour Monitoring: From Chemical Analysis to Predictive Models
by Claudia Franchina, Amedeo Manuel Cefalì, Martina Gianotti, Alessandro Frugis, Corrado Corradi, Giulio De Prosperis, Dario Ronzio, Luca Ferrero, Ezio Bolzacchini and Domenico Cipriano
Atmosphere 2024, 15(12), 1401; https://doi.org/10.3390/atmos15121401 - 21 Nov 2024
Viewed by 611
Abstract
This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical [...] Read more.
This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical species: two values for the concentration of VOCs recorded via the PID sensor (VPID) and the EC sensor (VEC), and concentrations of sulfuric acid (VH2S) and benzene (VC6H6). Using Random Forest and least squares regression analysis, the study identifies VH2S and VC6H6 as key contributors to odour concentration (CcOD). Three Random Forest models (RF0, RF1, RF2), with different characteristics for splitting between the test set and the training set, were tested, with RF1 showing superior predictive performance due to its training approach. All models highlighted VH2S and VC6H6 as significant predictors, while VPID and VEC had less influence. A significant correlation between odour concentration and specific chemical sensor readings was found, particularly for VH2S and VC6H6. However, predicting odour concentrations below 1000 ouE/m3 proved challenging. Linear regression further confirmed the importance of VH2S and VC6H6, with a moderate R-squared value of 0.70, explaining 70% of the variability in odour concentration. The study demonstrated the effectiveness of combining Random Forest and least squares regression for robust and interpretable results. Future research should focus on expanding the dataset and incorporating additional variables to enhance model accuracy. The findings underscore the necessity of specific sensor training and standardised procedures for accurate odour monitoring and characterisation. Full article
(This article belongs to the Special Issue Environmental Odour (2nd Edition))
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<p>Aerial view of the sawage treatment plant. The red spot indicates the location of the e-Nose and the green spots indicate the position of sedimentation tanks.</p>
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<p>Wind rose of the study site during the 27-day measurement campaign.</p>
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<p>Illustration of the distribution of odour concentration during the 27-day campaign in relation to wind direction and frequency.</p>
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<p>Box plot for percentiles of odour concentration of W, SW, and NW wind direction.</p>
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<p>Frequency of odour concentration of plant odour emissions.</p>
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<p>Random Forest 0 (RF0): series of graphs that utilise the first 30% of the data for training the algorithm and calculate the predictive model on the subsequent remaining 70% of the dataset. (<b>Panel A</b>) illustrates a heat scatter plot, where varying color intensities represent areas of high concentration (shown in red) and low concentration (represented in blue) of values; (<b>Panel B</b>) displays the error plot, offering a graphical representation of the differences between observed and predicted values; (<b>Panel C</b>) presents a bar chart that highlights the distribution of these errors.</p>
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<p>Random Forest 1 (RF1): series of graphs that utilise a random 30% of the data for training the algorithm and calculate the predictive model on the subsequent remaining 70% of the dataset. (<b>Panel A</b>) illustrates a heat scatter plot, where varying color intensities represent areas of high concentration (shown in red) and low concentration (represented in blue) of values; (<b>Panel B</b>) displays the error plot, offering a graphical representation of the differences between observed and predicted values; (<b>Panel C</b>) presents a bar chart that highlights the distribution of these errors.</p>
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<p>Random Forest 2 (RF2): series of graphs that utilise a random 30% of the data for training the algorithm and calculating the predictive model. The predictive model was applied to the complete dataset. (<b>Panel A</b>) illustrates a heat scatter plot, where varying color intensities represent areas of high concentration (shown in red) and low concentration (represented in blue) of values; (<b>Panel B</b>) displays the error plot, offering a graphical representation of the differences between observed and predicted values; (<b>Panel C</b>) presents a bar chart that highlights the distribution of these errors.</p>
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<p>Random Forest 0 (RF0): series of graphs that utilise the first 30% of the data for training the algorithm and calculate the predictive model on the subsequent remaining 70% of the dataset. The dataset includes odour data below 1000 ou<sub>E</sub>/m<sup>3</sup>. (<b>Panel A</b>) illustrates a heat scatter plot, where varying color intensities represent areas of high concentration (shown in red) and low concentration (represented in blue) of values; (<b>Panel B</b>) displays the error plot, offering a graphical representation of the differences between observed and predicted values; (<b>Panel C</b>) presents a bar chart that highlights the distribution of these errors.</p>
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<p>Random Forest 1 (RF1): series of graphs that utilise a random 30% of the data for training the algorithm and calculate the predictive model on the subsequent remaining 70% of the dataset. The dataset includes odour data below 1000 ou<sub>E</sub>/m<sup>3</sup>. (<b>Panel A</b>) illustrates a heat scatter plot, where varying color intensities represent areas of high concentration (shown in red) and low concentration (represented in blue) of values; (<b>Panel B</b>) displays the error plot, offering a graphical representation of the differences between observed and predicted values; (<b>Panel C</b>) presents a bar chart that highlights the distribution of these errors.</p>
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<p>Random Forest 2 (RF2): series of graphs that utilise a random 30% of the data for training the algorithm and calculating the predictive model. The predictive model was applied to the complete dataset. The dataset includes odour data below 1000 ou<sub>E</sub>/m<sup>3</sup>. (<b>Panel A</b>) illustrates a heat scatter plot, where varying color intensities represent areas of high concentration (shown in red) and low concentration (represented in blue) of values; (<b>Panel B</b>) displays the error plot, offering a graphical representation of the differences between observed and predicted values; (<b>Panel C</b>) presents a bar chart that highlights the distribution of these errors.</p>
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<p>Odour concentration obtained from the measurements with the e-Nose and odour concentration calculated using the literature model (Equation (6)).</p>
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<p>Odour concentration obtained from the measurements with the e-Nose and odour concentration calculated using the Least Squares Model (Equation (7)).</p>
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9 pages, 253 KiB  
Brief Report
Innovative Methodologies in University Teaching: Pilot Experience of an Escape Room in Nursing Students
by Javier Fagundo-Rivera, Rocío Romero-Castillo, Miguel Garrido-Bueno and Pablo Fernández-León
Educ. Sci. 2024, 14(10), 1097; https://doi.org/10.3390/educsci14101097 - 9 Oct 2024
Viewed by 1270
Abstract
The presence of playful elements in learning environments is increasingly frequent in university settings. The objective of this study was to evaluate the gaming experience of the participants of an escape room activity developed in the second year of the Bachelor of Science [...] Read more.
The presence of playful elements in learning environments is increasingly frequent in university settings. The objective of this study was to evaluate the gaming experience of the participants of an escape room activity developed in the second year of the Bachelor of Science in Nursing program. An escape room activity was carried out, based on 10 tasks, on the thematic content of the subject ‘History, Theory and Methods of Nursing II’, with students in the second year. After the game experience, the Gameful Experience Scale (GAMEX) questionnaire of 27 items in the validated version in Spanish and for nursing students was applied. An open-ended question was also included to allow the students to give their opinion on aspects of improvement, or their feelings during their performance, and a thematic analysis was utilized for this qualitative approach. A total of 107 students participated in the escape room activity, and 75 individuals acceded to the request to be surveyed. The results in the Enjoyment dimension showed that five of the six questions were assessed with the maximum score by the majority of respondents. In the Absorption dimension, between 15% and 25% of the sample did not manage to abstract themselves from the real world. In the dimensions of Creative Thinking and Activation, up to 70% managed to feel imaginative, creative, or exploratory, feeling active and excited. Nearly 85% of the sample reported the Absence of Negative Affect (hostility, annoyance, or frustration) during the game. In the Dominance dimension, up to 70% of the sample considered feeling influential during the game. Two categories were identified after analyzing the participants’ responses: room for improvement in the activity and feelings during the activity. In conclusion, the escape room is positioned as a useful tool for university teaching in nursing. This didactic game allows students to have fun while learning, and to value the knowledge and techniques provided by the subject while being able to work as a team. Full article
20 pages, 4368 KiB  
Article
Measurement of the Direct Impact of Hematophagous Flies on Feeder Cattle: An Unexpectedly High Potential Economic Impact
by Phoompong Boonsaen, Adèle Nevot, Sathaporn Onju, Clément Fossaert, Piangjai Chalermwong, Kornkanok Thaisungnoen, Antoine Lucas, Sophie Thévenon, Roungthip Masmeatathip, Sathaporn Jittapalapong and Marc Desquesnes
Insects 2024, 15(10), 735; https://doi.org/10.3390/insects15100735 - 24 Sep 2024
Viewed by 1318
Abstract
In addition to blood pathogen transmission, insects of the order Diptera affect livestock through visual and contact harassment; blood-feeders are responsible for painful bites and blood despoliation, generating behavioral modifications, anemia, and production losses. Knowledge of their economic impact is a basis for [...] Read more.
In addition to blood pathogen transmission, insects of the order Diptera affect livestock through visual and contact harassment; blood-feeders are responsible for painful bites and blood despoliation, generating behavioral modifications, anemia, and production losses. Knowledge of their economic impact is a basis for cost-effective control. Here, we measured the global impact of diptera insects by comparing two batches of six feeder cattle, one in the open air and the other protected by a mosquito net. The analytical data were insect density in the open air and, for feeder cattle, tail flick counts, hematocrit values (Ht), feed intake, feed conversion ratio (FCR), and live body weight gain (LBWG). Over a period of five months, the results showed significant losses in the LBWG of cattle exposed to insects, estimated at 8.0 ± 1.5 kg/month [2.7; 13.3], with a total loss reaching 40.0 ± 5.5 kg/head. Main diurnal insects were Stomoxys spp. and Musca crassirostris. There was a strong correlation between fly density and diurnal tail flicks. Night trapping and tail flicks showed a potentially important role of mosquitoes to be further explored. The Ht levels of exposed animals were 3–4% lower than those of controls. FCRs indicated that exposed animals needed 33% more dry matter intake/kg of LBWG. An economic assessment showed that dipterans were responsible for a 10–11% loss in LBWG during the main growing period of feeder cattle (10–15 months). A feedlot of 100 calves would register a total loss of USD 16,000 within 5 months, which appears to be an unexpectedly huge loss caused by dipterans. Investing part of this money into fly control would probably be beneficial. Full article
(This article belongs to the Section Medical and Livestock Entomology)
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<p>Pictures of the experimental devices; Group A and Pen A: fly-proof system; Group B and Pen B: open-air system.</p>
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<p>Pictures of feeder cattle under a mosquito net (<b>A</b>) with tail pedometer disposal (<b>B</b>) and in open air (<b>C</b>), Nzi trap collection (<b>D</b>), Vavoua trap (<b>E</b>), and some hematophagous flies: <span class="html-italic">Musca crassirostris</span> (<b>F</b>) and a tabanid: <span class="html-italic">Haematopota</span> sp. (<b>G</b>).</p>
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<p>Fly densities observed during Experiment 1.</p>
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<p>Fly densities observed during Experiment 2.</p>
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<p>Mean PCV (95% CI) in Groups A and B and hematophagous fly densities in Experiment 1, from weeks 1 to 9. The broad lines indicate smooth mean values, the thin solid line indicates insect density, the bold solid line represents Group A, and the bold long dashed line represents Group B. Shaded parts correspond to the confidence interval of the means (at 95%).</p>
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<p>Mean PCV (95% CI) in Groups A and B and hematophagous fly densities in Experiment 2, from weeks 1 to 24. The broad lines indicate smooth mean values, the thin solid line indicates insect density, the bold solid line represents Group A, and the bold long dashed line represents Group B. Shaded parts correspond to the confidence interval of the means (at 95%).</p>
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<p>Mean cattle live body weight (95% CI) in Groups A and B and hematophagous fly densities in Experiment 1. The broad lines indicate smooth mean values, the thin solid line indicates insect density, the bold solid line represents Group A, and the bold long dashed line represents Group B. Shaded parts correspond to the confidence interval of the means (at 95%).</p>
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<p>Mean cattle live body weight (95% CI) in Groups A and B and hematophagous fly densities in Experiment 2. The broad lines indicate smooth mean values, the thin solid line indicates insect density, the bold solid line represents Group A, and the bold long dashed line represents Group B. Shaded parts correspond to the confidence interval of the means (at 95%).</p>
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17 pages, 319 KiB  
Article
Emotional and Work-Related Factors in the Self-Assessment of Work Ability among Italian Healthcare Workers
by Nicola Magnavita, Igor Meraglia and Carlo Chiorri
Healthcare 2024, 12(17), 1731; https://doi.org/10.3390/healthcare12171731 - 30 Aug 2024
Cited by 2 | Viewed by 1026
Abstract
The Work Ability Index (WAI) is the most commonly used tool for evaluating work capacity. Self-assessments made by workers can be influenced by various occupational and emotional factors. We wanted to study the association of work-related factors, such as work annoyance, stress, overcommitment, [...] Read more.
The Work Ability Index (WAI) is the most commonly used tool for evaluating work capacity. Self-assessments made by workers can be influenced by various occupational and emotional factors. We wanted to study the association of work-related factors, such as work annoyance, stress, overcommitment, job satisfaction, social support, and emotional factors, such as anxiety, depression, and happiness, with work ability, in a sample of 490 healthcare workers from an Italian public health company. A principal component analysis indicated the presence of two components of the WAI questionnaire; the first expresses “subjectively estimated work ability” (SEWA), and the second refers to “ill-health-related work ability” (IHRWA). Using stepwise multiple hierarchical linear regression, we identified the factors that best predicted the total score on the WAI and on the two components. The total score was negatively predicted by anxiety, depression, a lack of happiness, low job satisfaction, overcommitment, and work annoyance. Age, being female, anxiety, and occupational stress were associated with a reduction in the IHRWA component score, while overcommitment, work annoyance, a lack of social support, depression, and a lack of happiness were negatively associated with the SEWA component. These results can help interpret those of epidemiological studies and provide guidance on ways to improve work ability. Full article
18 pages, 5160 KiB  
Article
Identification, Evaluation, and Influencing Factors of Soundscapes in Public Open Spaces in High-Density Residential Areas
by Zeyu Xu, Ming Yang and Lei Yu
Appl. Sci. 2024, 14(16), 6946; https://doi.org/10.3390/app14166946 - 8 Aug 2024
Cited by 1 | Viewed by 1291
Abstract
Public open spaces make crucial contributions to the livability of communities and promote physical and mental health. Soundscapes play an important role in the overall physical comfort of public open spaces. However, owing to insufficient studies of high-density situations, soundscapes are ignored in [...] Read more.
Public open spaces make crucial contributions to the livability of communities and promote physical and mental health. Soundscapes play an important role in the overall physical comfort of public open spaces. However, owing to insufficient studies of high-density situations, soundscapes are ignored in public open spaces in high-density residential areas. This paper presents a case study of a soundscape in the overseas Chinese town (OCT) of Shenzhen, China. Through in situ observation, four distinct soundscapes were easily identified by performing soundscape conceptualization according to the ISO. In terms of the four identified soundscape areas, subjective evaluations of acoustic comfort and annoyance and their influencing factors were thoroughly explored. The results reveal that the natural soundscape had the best evaluations, whereas the artificial one did not have the worst result. It is interesting to note that acoustic factors do not always significantly influence a soundscape’s evaluation. A non-acoustic factor such as the spatial function may play a role as it is related to the context of an individual perceiving an acoustic environment. This study provides first-hand empirical evidence for understanding soundscapes and the influencing factors present in high-density residential public open spaces. The results provide useful knowledge for enhancing soundscape quality in such spaces. Full article
(This article belongs to the Section Environmental Sciences)
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<p>The research framework of the present study.</p>
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<p>Location of the Shenzhen OCT ecological square.</p>
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<p>The four blocks of the Shenzhen OCT ecological square.</p>
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<p>A structural framework map of sound collection and surveys.</p>
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<p>Typical sound spectra in the four blocks.</p>
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54 pages, 26713 KiB  
Article
Thermal–Acoustic Interaction Effects on Physiological and Psychological Measures in Urban Forests: A Laboratory Study
by Ye Chen, Taoyu Li, Shaoyou Chen, Hangqing Chen and Yuxiang Lan
Forests 2024, 15(8), 1373; https://doi.org/10.3390/f15081373 - 6 Aug 2024
Viewed by 1041
Abstract
The environment in which people live is a complex system influenced by multiple factors interacting with each other, and therefore, it is crucial to deeply explore the influences of various factors on environmental perception. Among the numerous factors affecting the experience of urban [...] Read more.
The environment in which people live is a complex system influenced by multiple factors interacting with each other, and therefore, it is crucial to deeply explore the influences of various factors on environmental perception. Among the numerous factors affecting the experience of urban forests visits, the thermal–acoustic environment stands out prominently. This study focuses on urban forests located in subtropical regions, with specific research conducted in the Xihu Park in Fuzhou, China. The study explores the thermal–acoustic interaction in urban forest environments. A total of 150 participants evaluated the perception of sound, thermal sensation, and overall perception through laboratory experiments, with 36 of them having their objective physiological indicators monitored. Different levels of sound and temperature were selected for the experiments, with three levels for each type of sound. Our results show that increasing temperature enhanced the perceived loudness of sound, especially when the environment was quiet. Sound type and loudness had a significant impact on thermal sensation, but no interaction was observed with temperature. Moreover, we found that certain sounds could improve overall comfort, and the effect was most evident at moderate loudness. Temperature had a significant influence on both comfort and annoyance, with increasing temperature leading to higher annoyance. These findings provide important insights into how the interplay between sound and heat affects human perception and emotional state, providing scientific guidance for the design of more human-centered environments. Full article
(This article belongs to the Special Issue Soundscape in Urban Forests - 2nd Edition)
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<p>(<b>a</b>) Location of Fuzhou City in the map of China. (<b>b</b>) Xihu Park in Fuzhou City. (<b>c</b>) Five sound collection sites within Xihu Park.</p>
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<p>Subjective loudness of birdcall under the influence of different temperatures.</p>
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<p>Subjective loudness of water under the influence of different temperatures.</p>
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<p>Subjective loudness of conversation under the influence of different temperatures.</p>
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<p>Subjective loudness of light music under the influence of different temperatures.</p>
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<p>Subjective loudness of traffic under the influence of different temperatures.</p>
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<p>Subjective loudness of cutting grass under the influence of different temperatures.</p>
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<p>Acoustic comfort of birdcall under the influence of different temperatures.</p>
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<p>Acoustic comfort of water under the influence of different temperatures.</p>
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<p>Acoustic comfort of conversation under the influence of different temperatures.</p>
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<p>Acoustic comfort of light music under the influence of different temperatures.</p>
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<p>Acoustic comfort of traffic under the influence of different temperatures.</p>
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<p>Acoustic comfort of cutting grass under the influence of different temperatures.</p>
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<p>Acoustic preference of birdcall under the influence of different temperatures.</p>
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<p>Acoustic preference of water under the influence of different temperatures.</p>
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<p>Acoustic preference of conversation under the influence of different temperatures.</p>
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<p>Acoustic preference of light music under the influence of different temperatures.</p>
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<p>Acoustic preference of traffic under the influence of different temperatures.</p>
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<p>Acoustic preference of cutting grass under the influence of different temperatures.</p>
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<p>Mean and variability of HR of different volumes under the low-heat condition. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR of different volumes under the medium-heat condition. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR of different volumes under the high-heat condition. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA of different volumes under the low-heat condition. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA of different volumes under the medium-heat condition. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA of different volumes under the high-heat condition. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Thermal sensation votes under the influence of birdcall.</p>
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<p>Thermal sensation votes under the influence of water.</p>
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<p>Thermal sensation votes under the influence of conversation.</p>
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<p>Thermal sensation votes under the influence of light music.</p>
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<p>Thermal sensation votes under the influence of traffic.</p>
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<p>Thermal sensation votes under the influence of cutting grass.</p>
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<p>Thermal comfort votes under the influence of birdcall.</p>
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<p>Thermal comfort votes under the influence of water.</p>
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<p>Thermal comfort votes under the influence of conversation.</p>
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<p>Thermal comfort votes under the influence of light music.</p>
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<p>Thermal comfort votes under the influence of traffic.</p>
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<p>Thermal comfort votes under the influence of cutting grass.</p>
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<p>Thermal acceptability votes under the influence of birdcall.</p>
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<p>Thermal acceptability votes under the influence of water.</p>
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<p>Thermal acceptability votes under the influence of conversation.</p>
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<p>Thermal acceptability votes under the influence of light music.</p>
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<p>Thermal acceptability votes under the influence of traffic.</p>
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<p>Thermal acceptability votes under the influence of cutting grass.</p>
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<p>Mean HR at different temperatures in the absence of sound.</p>
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<p>Mean and variability of HR at different temperatures under birdcall. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR at different temperatures under water. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR at different temperatures under conversation. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR at different temperatures under light music. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR at different temperatures under traffic. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of HR at different temperatures under cutting grass. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean EDA at different temperatures in the absence of sound.</p>
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<p>Mean and variability of EDA at different temperatures under birdcall. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA at different temperatures under water. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA at different temperatures under conversation. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA at different temperatures under light music. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA at different temperatures under traffic. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Mean and variability of EDA at different temperatures under cutting grass. (<b>a</b>) Low volume. (<b>b</b>) Medium volume. (<b>c</b>) High volume.</p>
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<p>Overall comfort under the interaction of temperature and birdcall.</p>
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<p>Overall comfort under the interaction of temperature and water.</p>
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<p>Overall comfort under the interaction of temperature and conversation.</p>
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<p>Overall comfort under the interaction of temperature and light music.</p>
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<p>Overall comfort under the interaction of temperature and traffic.</p>
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<p>Overall comfort under the interaction of temperature and cutting grass.</p>
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<p>Overall annoyance votes under the interaction of temperature and birdcall.</p>
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<p>Overall annoyance votes under the interaction of temperature and water.</p>
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<p>Overall annoyance votes under the interaction of temperature and conversation.</p>
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<p>Overall annoyance votes under the interaction of temperature and light music.</p>
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<p>Overall annoyance votes under the interaction of temperature and traffic.</p>
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<p>Overall annoyance votes under the interaction of temperature and cutting grass.</p>
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31 pages, 2390 KiB  
Review
A Systematic Review and Meta-Analysis of Noise Annoyance as a Determinant of Physiological Changes Linked to Disease Promotion
by Emily Senerth, Tejanth Pasumarthi, Neha Tangri, Bhavya Abbi, Skye Bickett, James P. McNamee, David S. Michaud and Rebecca L. Morgan
Int. J. Environ. Res. Public Health 2024, 21(7), 956; https://doi.org/10.3390/ijerph21070956 - 22 Jul 2024
Viewed by 1492
Abstract
This systematic review investigates the certainty of evidence (CoE) regarding noise annoyance as a determinant of biological changes known to contribute to disease development. We searched PubMed MEDLINE, EMBASE, Cochrane Central, and CINAHL for English-language comparative studies conducted on humans of any age [...] Read more.
This systematic review investigates the certainty of evidence (CoE) regarding noise annoyance as a determinant of biological changes known to contribute to disease development. We searched PubMed MEDLINE, EMBASE, Cochrane Central, and CINAHL for English-language comparative studies conducted on humans of any age from 1 January 1940, to 28 August 2023. Further, studies that provided quantitative data on the relationship between noise annoyance and biomarkers of interest were included. Where possible, random-effects meta-analyses were used to calculate the odds ratios of noise annoyance on biomarkers and biological conditions considered to be risk factors for developing health effects. The risk of bias of individual studies was assessed using the Risk of Bias of Non-randomized Studies of Exposures (ROBINS-E) instrument. The CoE for each outcome was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. The search identified 23 primary studies reporting on relevant biomarkers. Although some studies and pooled estimates suggest a possible association between noise annoyance and biological measures, the CoE overall is very low due to concerns with the risk of bias, inconsistency, and imprecision in the estimates of effects. In the context of environmental impact assessment, where guidelines aim to mitigate the prevalence of populations experiencing a high level of noise annoyance, our results suggest that such practices should be grounded in the understanding that annoyance is health-relevant because it reflects an undesirable reaction to noise, rather than a precursor to chronic physical health conditions. Full article
(This article belongs to the Special Issue Community Response to Environmental Noise)
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<p>PRISMA flow diagram.</p>
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<p>Meta-analysis of hypertension prevalence (all estimates).</p>
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<p>Meta-analysis of hypertension prevalence (adjusted estimates).</p>
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18 pages, 1918 KiB  
Article
Acoustic Comfort Prediction: Integrating Sound Event Detection and Noise Levels from a Wireless Acoustic Sensor Network
by Daniel Bonet-Solà, Ester Vidaña-Vila and Rosa Ma Alsina-Pagès
Sensors 2024, 24(13), 4400; https://doi.org/10.3390/s24134400 - 7 Jul 2024
Cited by 1 | Viewed by 1795
Abstract
There is an increasing interest in accurately evaluating urban soundscapes to reflect citizens’ subjective perceptions of acoustic comfort. Various indices have been proposed in the literature to achieve this purpose. However, many of these methods necessitate specialized equipment or extensive data collection. This [...] Read more.
There is an increasing interest in accurately evaluating urban soundscapes to reflect citizens’ subjective perceptions of acoustic comfort. Various indices have been proposed in the literature to achieve this purpose. However, many of these methods necessitate specialized equipment or extensive data collection. This study introduces an enhanced predictor for dwelling acoustic comfort, utilizing cost-effective data consisting of a 30-s audio clip and location information. The proposed predictor incorporates two rating systems: a binary evaluation and an acoustic comfort index called ACI. The training and evaluation data are obtained from the “Sons al Balcó” citizen science project. To characterize the sound events, gammatone cepstral coefficients are used for automatic sound event detection with a convolutional neural network. To enhance the predictor’s performance, this study proposes incorporating objective noise levels from public IoT-based wireless acoustic sensor networks, particularly in densely populated areas like Barcelona. The results indicate that adding noise levels from a public network successfully enhances the accuracy of the acoustic comfort prediction for both rating systems, reaching up to 85% accuracy. Full article
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<p>Locations of the Barcelona sound sensor network and the videos collected during the 2021 <span class="html-italic">Sons al Balcó</span> campaign.</p>
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<p>Distribution of the Barcelona sound sensors deployed in 2021 by the predominant noise sources in their locations.</p>
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<p>Subjective Assessment of the Soundscapes in Barcelona (Likert scale).</p>
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<p>Enhanced Dwelling’s Soundscape Quality Estimator (Binary).</p>
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<p>Enhanced Dwelling’s Acoustic Comfort Index Estimator.</p>
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<p>Performance of the nearest sensor-based prediction depending on the maximum distance between sensor and studied location accepted.</p>
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<p>Error distance for the ACI assessment for the ASED, nearest-based and combined approaches.</p>
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<p>Quarterly evolution of the <math display="inline"><semantics> <msub> <mi>L</mi> <mrow> <mi>A</mi> <mi>e</mi> <mi>q</mi> </mrow> </msub> </semantics></math> measured in the subset of BCN sound meters network used during this present study that were continuously collecting data from 2017 to 2022.</p>
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14 pages, 1909 KiB  
Article
Effectiveness of Computer-Mediated Educational Counseling for Tinnitus Relief: A Randomized Controlled Trial
by Sumin Lee, Tae-Jun Jin, Donghyeok Lee and In-Ki Jin
Brain Sci. 2024, 14(7), 629; https://doi.org/10.3390/brainsci14070629 - 24 Jun 2024
Viewed by 1033
Abstract
Counseling can help alleviate tinnitus-caused emotional distress and correct misconceptions, making it an effective rehabilitation option for people with tinnitus. Advances in communication technology have increased the demand for computer-mediated tinnitus counseling; however, the effectiveness of such counseling in reducing tinnitus is unclear. [...] Read more.
Counseling can help alleviate tinnitus-caused emotional distress and correct misconceptions, making it an effective rehabilitation option for people with tinnitus. Advances in communication technology have increased the demand for computer-mediated tinnitus counseling; however, the effectiveness of such counseling in reducing tinnitus is unclear. Thus, this study aimed to determine the tinnitus-relieving effects of computer-mediated counseling. Thirty-six participants with tinnitus were randomly assigned to online counseling (15 participants) or video-based counseling (21 participants) groups, defining how remote counseling was conducted. Tinnitus counseling, comprising 100 items, lasted 2 weeks and was separated into six sessions for the online counseling group and 8–9 items daily for 12 days for the video-based counseling group. The effectiveness of counseling was determined based on score changes between baseline and 2-week follow-up using the Korean version of the Tinnitus Primary Function Questionnaire and Visual Analog Scales for annoyance and loudness. While no significant improvements were observed in other domains, average emotional aspect-related scores showed significant improvements in both groups. Regarding individual results, four and seven participants in the online and video-based counseling groups reported significant improvements in the emotional domain, respectively. Overall, computer-mediated educational counseling might be a rehabilitation option for individuals with tinnitus. Full article
(This article belongs to the Special Issue Advances in Tinnitus and Hearing Disorders)
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<p>Consolidated Standards of Reporting Trials (CONSORT) flow diagram for this clinical trial.</p>
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<p>Protocol of the present study. K-TPFQ = Korean version of the Tinnitus Primary Function Questionnaire; PTA = pure-tone audiometry; VAS = Visual Analog Scale.</p>
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<p>Change in average K-TPFQ scores over time for the two study groups. K-TPFQ = Korean version of the Tinnitus Primary Function Questionnaire. * Asterisks indicate statistical significance.</p>
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<p>Change in average VAS scores over time for the two study groups. VAS = Visual Analog Scale. * Asterisks indicate statistical significance.</p>
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