Actual Noise Measurements Research Articles

Actual versus perceived noise levels among critical care nurses and their related adverse effects: A cross-sectional study.

Critical care nurses should help in reducing noise to improve the well-being of patients and health care providers. To measure actual noise levels in intensive care units, identify nurses' perceived irritation levels of noise sources and examine the impact of noise on nurses' work performance and well-being. This cross-sectional study was conducted from January to April 2023 at a teaching hospital's six intensive care units. It involved three phases: mapping locations for noise measurement, actual noise measurements in decibels and a cross-sectional survey to identify nurses' perceptions and the adverse effects of noise. Actual noise was measured between patients' beds, nursing stations and beside 16 noise sources for 7 days 24 h a day. For nurse perceptions, the mean of perceived irritation levels by a Likert scale for each noise source was calculated to present perceived noise levels. Ranking of noise sources according to the actual measured and perceived irritation by nurses was done based on mean values. Curve estimation regression test was used to predict the relationship between actual and perceived noise and stepwise multiple linear regression identified factors associated with noise adverse effects. The mean noise level in the intensive care units was 65.55 dB. Nurses perceived mechanical ventilator alarms as the most irritating noise. However, the mean noise measurements showed that conversations among colleagues were the primary source of noise. There was no significant predictive relationship between the actual and perceived noise. Nurses reported feeling distressed, irritable, fatigued and less productive due to noise. There was a discrepancy between actual and perceived noise levels by nurses. Perceived noise levels had more impact on nurses than actual noise. By incorporating both objective measurements and subjective perceptions into efforts aimed at reducing noise, health care providers can create quieter intensive care units.

Requirements and best practices for traffic noise model validation

The exercise of validating a predictive model typically consists of modeling an existing real-world condition and predicting some resulting quantity of interest, and then comparing the predicted results to actual measured values for the same condition within a specified margin of error. In the case of road traffic noise models this would include modeling the characteristics of the existing roadway condition (geometry of the roadway network; number, vehicle type and speed of vehicles using the roadways; pavement types; intervening terrain; etc.) and comparing the resulting predicted noise level to actual noise measurements for the same conditions. In this paper the authors will provide a concise background of the validation purpose and process as well as a review of agency requirements and best practices for traffic noise model validation procedures.

Community noise exposure and annoyance, activity interference, and academic achievement among university students.

Background:Noise annoyance and effects on academic performance have been investigated for primary and secondary school students but comparatively little work has been conducted with university students who generally spend more time in dormitories or accommodation for their self-study.Objective:To determine, using a socio-acoustic approach involving face-to-face interviews and actual noise measurements, the effect of various community noise sources on student activities in accommodation both inside and outside a university precinct and also relationships with cumulative grade point average (GPA).Materials and Methods:The study sample comprised a student group resident off-campus (n = 450) and a control group resident in dormitories on-campus (n = 336). Noise levels [LA (dB)] were measured at both locations according to International Organization for Standardization standards. The extent of community noise interference with the student activities was examined with bivariate and stratified analyses and results presented as Mantel–Haenszel weighted odds ratios (ORMH) with 95% confidence intervals. Binary logistic regression was employed to assess the association between noise-disturbed student activities and dichotomized GPA values and derive odds ratios (ORs) for these associations.Results:Measured noise levels were all significantly (P < 0.05) higher for off-campus students. This was not reflected in the interviewed students’ subjective perceptions of how “noisy” their respective environments were. The off-campus student cohort was, however, more annoyed by all community noise categories (P < 0.001) except road traffic noise. For impact on specific student activities, the largest differences between on- and off-campus students were found for telephone and personal communication regardless of the type of community noise. There was no significant difference in the relationships between perceived annoyance due to community noise categories and cumulative GPA in the off-campus group compared to those for on-campus residents with ORMH values ranging from 1.049 to 1.164. The most important noise-impacted factors affecting off-campus students’ cumulative GPA were reading and mental tasks (OR = 2.801). Rest disturbance had a positive influence on cumulative GPA for on-campus students.Conclusion:These results provide support that various contemporary community noise sources affect university students’ activities and possibly influence their educational achievement as well.

Determining environmental noise measurement uncertainty in the context of the Italian legislative framework

Abstract This paper aims to demonstrate the importance of uncertainties in the measurement of environmental noise in the context of Italian legislation on noise pollution focusing the attention on the variability of the measurand as a source of uncertainty and offering a proposal for the evaluation of uncertainty for traffic noise measurement. In particular, drawing on a real traffic noise dataset, firstly outliers are eliminated from the actual noise measurements using an outlier detection algorithm based on K -neighbors distance and then uncertainty range is estimated with the bootstrap- t method. Since the original sequence was Gaussian, this range was compared with the confidence interval of the mean ± standard deviation interval and two intervals were almost coincident.

Reducing noise in the academic library: the effectiveness of installing noise meters

Purpose – The purpose of this paper is to explore the effect of an electronic noise-monitoring device (NoiseSign) at reducing noise levels in quiet study areas in an academic library. Design/methodology/approach – Surveys and decibel-level measurements were used to measure the perceived and objective noise levels, respectively, in both an intervention and a control area of two major branch libraries. Patrons’ perception of noise was measured with a passive paper and online survey, which asked patrons to rate the current noise level and their desired noise level. The actual noise measurements were collected twice a day with a hand-held decibel reader for 60 seconds and then corroborated after the intervention with automatically logged decibel readings from the noise monitor device in the two intervention areas. The authors conducted one-way ANOVA’s to determine if the results were significant. Findings – The NoiseSign had no statistically significant effect on either actual noise levels or user perceptions of noise in the library. The surveys comments and anecdotal observation of the spaces while doing measurements did reveal that noise in the quiet study areas was not the primary source of complaints. Originality/value – In spite of many proposed solutions to reducing noise in libraries, there has been very little research in this area. This is the first study to examine the effectiveness of using a noise-monitoring device in reducing noise levels at an academic library.

Deciding Optimal Noise Monitoring Sites with Matrix Gray Absolute Relation Degree Theory

Noise maps are applied to assess noise level in cities all around the world. There are mainly two ways of producing noise maps: one way is producing noise maps through theoretical simulations with the surrounding conditions, such as traffic flow, building distribution, etc.; the other one is calculating noise level with actual measurement data from noise monitors. Currently literature mainly focuses on considering more factors that affect sound traveling during theoretical simulations and interpolation methods in producing noise maps based on measurements of noise. Although many factors were considered during simulation, noise maps have to be calibrated by actual noise measurements. Therefore, the way of obtaining noise data is significant to both producing and calibrating a noise map. However, there is little literature mentioned about rules of deciding the right monitoring sites when placed the specified number of noise sensors and given the deviation of a noise map produced with data from them. In this work, by utilizing matrix Gray Absolute Relation Degree Theory, we calculated the relation degrees between the most precise noise surface and those interpolated with different combinations of noise data with specified number. We found that surfaces plotted with different combinations of noise data produced different relation degrees with the most precise one. Then we decided the least significant one among the total and calculated the corresponding deviation when it was excluded in making a noise surface. Processing the left noise data in the same way, we found out the least significant datum among the left data one by one. With this method, we optimized the noise sensor’s distribution in an area about 2[Formula: see text]km2. And we also calculated the bias of surfaces with the least significant data removed. Our practice provides an optimistic solution to the situation faced by most governments that there is limited financial budget available for noise monitoring, especially in the undeveloped regions.

X-ray fluoroscopy noise modeling for filter design

Fluoroscopy is an invaluable tool in various medical practices such as catheterization or image-guided surgery. Patient's screen for prolonged time requires substantial reduction in X-ray exposure: The limited number of photons generates relevant quantum noise. Denoising is essential to enhance fluoroscopic image quality and can be considerably improved by considering the peculiar noise characteristics. This study presents analytical models of fluoroscopic noise to express the variance of noise as a function of gray level, a practical method to estimate the parameters of the models and a possible application to improve the performance of noise filtering. Quantum noise is modeled as a Poisson distribution and results strongly signal-dependent. However, fluoroscopic devices generally apply gray-level transformations (i.e., logarithmic-mapping, gamma-correction) for image enhancement. The resulting statistical transformations of the noise were analytically derived. In addition, a characterization of the statistics of noise for fluoroscopic image differences was offered by resorting to Skellam distribution. Real fluoroscopic sequences of a simple step-phantom were acquired by a conventional fluoroscopic device and were utilized as actual noise measurements to compare with. An adaptive spatio-temporal filter based on the local conditional average of similar pixels has been proposed. The gray-level differences between the local pixel and the neighboring pixels have been assumed as measure of similarity. Filter performance was evaluated by using real fluoroscopic images of a step phantom and acquired during a pacemaker implantation. The comparison between experimental data and the analytical derivation of the relationship between noise variance and mean pixel intensity (noise-parameter models) were presented relatively to raw-images, after applying logarithmic-mapping or gamma-correction and for difference images. Results have confirmed a great agreement (adjusted R-squared values > 0.8). Clipping effects of real sensors were also addressed. A fine image restoration has been obtained by using a conditioned spatio-temporal average filter based on the noise statistics previously estimated. Fluoroscopic noise modeling is useful to design effective procedures for noise estimation and image filtering. In particular, filter performance analysis has showed that the knowledge of the noise model and the accurate estimate of noise characteristics can significantly improve the image restoration, especially for edge preserving. Fluoroscopic image enhancement can support further X-ray exposure reduction, medical image analysis and automated object identification (i.e., surgery tools, anatomical structures).

Occupational Noise Exposure and Risk of Acoustic Neuroma

A small number of prior epidemiologic studies of occupational noise exposure based on self-report have suggested an association with acoustic neuroma. The goal of the present study was to further examine the association between noise exposure and acoustic neuroma by using an objective measure of exposure in the form of a job exposure matrix. A total of 793 acoustic neuroma cases aged 21-84 years were identified between 1987 and 1999 from the Swedish Cancer Registry. The 101,756 controls randomly selected from the study base were frequency matched to cases on age, sex, and calendar year of diagnosis. Occupational information, available for 599 of the cases and 73,432 of the controls, was obtained from censuses and was linked to a job exposure matrix based on actual noise measurements. All risk estimates were close to unity, regardless of noise exposure level or parameter. The overall odds ratio for exposure to > or = 85 dB of noise was 0.89 (95% confidence interval: 0.64, 1.23). Contrary to previous study results, the present findings did not demonstrate an increased acoustic neuroma risk related to occupational noise exposure even after allowing for a long latency period. The effect of nondifferential misclassification of exposure must be considered a potential cause of the negative findings.

Residents' Perceptions and Field Measurements of Helicopter Operations

Despite the considerable research in the area of perceptions and annoyance in relation to noise levels research outside the context of commercial airports and military bases is lacking. Little is known about reactions to helicopter operations in areas where such disturbances are unexpected. Examples of such locations include several national parks and various communities in Hawaii affected by tour operations. A special federal aviation regulation has been enacted for the Grand Canyon National Park, and others may be precipitated from Public Law 100-91. At present, however, no legislative initiatives cover residential communities. The basic question that the research attempted to answer was whether, in towns of low-residential-density, exposure to loud, frequent, or long-lasting helicopter overflights corresponds to a negative attitude toward helicopter tour operations. Perceptual and actual noise measurements were collected, the former with a mail-back questionnaire survey and the latter with an extensive field survey. Investigations in four communities focused on potential relationships between people's annoyance and actual operational characteristics, such as noise intensity, frequency, and overflight duration. The expectation was substantiated that more exposure to helicopter overflights, particularly in terms of frequency and duration, relates to increasing annoyance.

Residents' Perceptions and Field Measurements of Helicopter Operations

Despite the considerable research in the area of perceptions and annoyance in relation to noise levels research outside the context of commercial airports and military bases is lacking. Little is known about reactions to helicopter operations in areas where such disturbances are unexpected. Examples of such locations include several national parks and various communities in Hawaii affected by tour operations. A special federal aviation regulation has been enacted for the Grand Canyon National Park, and others may be precipitated from Public Law 100-91. At present, however, no legislative initiatives cover residential communities. The basic question that the research attempted to answer was whether, in towns of low-residential-density, exposure to loud, frequent, or long-lasting helicopter overflights corresponds to a negative attitude toward helicopter tour operations. Perceptual and actual noise measurements were collected, the former with a mail-back questionnaire survey and the latter with an extensive...

A standard definition for wind‐generated, low‐frequency ambient noise source levels

Low‐frequency, wind‐generated ambient noise source levels are important input parameters for newly developed ambient noise prediction models such as DUNES. However, there has been a significant variation among recently reported source levels. An analysis is made of these values. Although the actual noise measurements, the assumed source model, and a small angle approximation are similar for all papers, differences arise due to additional geometrical factors or a further approximation to shift the source level to the surface. A standard source level definition and evaluation method are proposed. Standard values of low‐frequency, wind‐generated source levels are presented based on this strategy. [Work supported by NUSC.]LLL2. Abstract withdrawn.

Analysis, Measurement, and Control of Motor-Generated 120-Cycle Noise

This paper presents an attempt to solve the difficult problem of controlling 120-cycle noise by design. A case study of an automatic washer is presented. The subject is treated in the categories of the exciting source, the energy transmission paths, and the excited members. Prime emphasis is placed on the exciting source, which in this case is the main drive motor. The paper attempts to answer te following questions: how tismotorproduces 120-cycle energy; how it can be measured; and how it can be controlled. Several methods of determination of 120-cycle pulsating torque are discussed with examples of typical measurements and the correlation between methods. The difficulties of correlating predictions of noise and actual noise measurements are discussed, with techniques of statistical correlation offered. The paper concludes with a method of quality control which enables comparison of product performance to the engineering standard.