Calculation of effective perceived noise-level data from measured noise data: excerpts from ICAO Annex 16

1989 ◽  
pp. 301-318
Keyword(s):  
Noise Level ◽  
Level Data ◽  
Noise Data

An Experimental Study of the Noise Due to Traffic in a Congested Urban Area

2016 ◽  
Vol 15 (01) ◽  
pp. 1650010
Author(s):  
M. Sangeetha ◽  
P. Sankar
Keyword(s):  
Urban Area ◽  
Noise Level ◽  
Urban Areas ◽  
Noise Pollution ◽  
Simulation Software ◽  
Network Simulator ◽  
Level Data ◽  
Drastic Increase ◽  
Wireless Medium ◽  
Noise Data

Noise pollution in an urban environment is an issue of serious concern in the major cities of India. There are various factors that contribute to the increase of noise levels in urban areas. The intensity of traffic is one of the factors which contributes to a drastic increase in environmental noise. The management of noise pollution has to be considered in the decision making process. In this paper, an attempt is made to study the existing noise level due to the traffic in Velachery which is declared as a sensitive area by the Ministry of Environment and Forestry (MoEF). The noise level data is collected using the MS6710 digital sound meter. The Custic simulation software version 3.2 is used for finding the propagation of noise. The spatial patterns of measurement were also calculated, in the sub-urban area of Velachery, Chennai, Tamilnadu, India. A means of transmitting this data to vehicles moving in the area, through a wireless medium is simulated using NCTUns 6.0 (network simulator), to enable drivers to understand the environmental conditions. A hardware was also designed which can be used to transmit and receive the noise data using the Zigbee module. A noise transmitting station is placed at a junction, so that it can transmit this noise data to the receivers which are fitted inside the vehicles.

NoizCrowd: A Crowd-Based Data Gathering and Management System for Noise Level Data

2013 ◽  
pp. 172-186 ◽  
Author(s):  
Mariusz Wisniewski ◽  
Gianluca Demartini ◽  
Apostolos Malatras ◽  
Philippe Cudré-Mauroux
Keyword(s):  
Noise Level ◽  
Management System ◽  
Data Gathering ◽  
Level Data

scholarly journals Underwater Ambient Noise in Kongsfjorden, Spitsbergen, during the Summers of 2015 and 2016

ARCTIC ◽  
2020 ◽  
Vol 73 (3) ◽  
pp. 386-392
Author(s):  
Muthuraj Ashokan ◽  
Ganesan Latha ◽  
Ayyadurai Thirunavukkarasu
Keyword(s):  
Noise Level ◽  
Ambient Noise ◽  
Noise Levels ◽  
Acoustic Environment ◽  
Noise Data ◽  
Iceberg Calving ◽  
Instrument Noise ◽  
Average Noise Level

Underwater ambient noise was measured in Kongsfjorden, Svalbard, during the summers of 2015 and 2016 to understand the contribution of iceberg bubbling, iceberg calving, and shipping noise to the acoustic environment of the fjord. Comparison of the ambient noise data for the months of August, September, and October showed that average noise levels were similar, although the average noise level for 2015 was ~9 dB higher than in 2016 because of higher shipping noise. Maximum ambient noise was produced at frequencies less than 10 kHz during both summers. Spectrograms of iceberg calving noise showed that it occurred in the frequency below 500 Hz. Shipping noise was seen in the band below 600 Hz, and iceberg bubbling noise was detected in the band above 400 Hz. Instrument noise was observed in the frequency 400 Hz. It is clear that ice breaking and shipping contribute substantially to ambient noise in Kongsfjorden.

Noise level variation at norsar and its effect on detectability

1977 ◽  
Vol 67 (2) ◽  
pp. 479-492 ◽  
Author(s):  
F. Ringdal ◽  
H. Bungum
Keyword(s):  
Atmospheric Pressure ◽  
Noise Level ◽  
Amplitude Distribution ◽  
P Wave ◽  
Seasonal Fluctuations ◽  
Long Period ◽  
Short Period ◽  
Noise Data ◽  
Band Pass ◽  
Period Data

Abstract For a 3-year period, noise level measurements of short- and long-period data at NORSAR have been sampled at hourly intervals. Significant seasonal fluctuations in noise level have been found, in particular for long-period data. The noise amplitude distribution is approximately lognormal for band-pass filtered short-period data in the P-wave detection band, while the long-period noise data show a skewness that cannot be represented by a lognormal distribution. Diurnal fluctuations in noise level are quite small, but definitely present both for short and horizontal component long-period data. Cultural sources are found to account for the short-period variability, while the long-period fluctuations are attributed to atmospheric pressure variation. Event detection performance generally follows the noise level trends, with an increase in the number of reported events during summer of about 50 per cent relative to winter.

scholarly journals Effects of COVID-19 pandemic on festival celebrations and noise pollution levels

Noise Mapping ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 89-93
Author(s):  
Komal Kalawapudi ◽  
Taruna Singh ◽  
Ritesh Vijay ◽  
Nitin Goyal ◽  
Rakesh Kumar
Keyword(s):  
Noise Level ◽  
Noise Pollution ◽  
Pollution Level ◽  
Noise Levels ◽  
Indian Culture ◽  
Pollution Levels ◽  
Level Data ◽  
New Guidelines ◽  
The City ◽  
Mumbai City

Abstract India is a country where every religion and community celebrates their culture. Festivals have an important role in Indian culture and are celebrated whole-heartedly by the citizens. Most of these celebrations culminate to causing pollution especially noise pollution due to festivities and rituals. One such festival is Ganesh Chaturthi or Ganeshotsav which is magnificently celebrated in Maharashtra state of India. In the present study, noise pollution levels during Ganeshotsav at famous community pandals in Mumbai city were monitored in the year 2020. Noise level data was analyzed based on indices such as L 10, L 50, L 90, noise pollution level (LNP ) and noise climate (NC). Comparison of noise levels was carried out for the collected data during Ganesh Chaturthi in the previous years of 2018 and 2019. The city witnessed simple festival celebration in eco-friendly manner leading to significant decrease in noise levels due to CoVID-19 pandemic. The pandemic situation is an eye-opener for the city administration with demonstration in reduction of noise pollution. Many aspects of the pandemic can be carried forward in making new guidelines and policies to curtail pollution and eco-friendly celebration of festivals.

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

2015 ◽  
Vol 14 (04) ◽  
pp. 1550032
Author(s):  
Zhihua Gao ◽  
Yadan Li ◽  
Limin Zhao ◽  
Shuangwei Wang
Keyword(s):  
Noise Level ◽  
Measurement Data ◽  
Degree Theory ◽  
Actual Measurement ◽  
Optimistic Solution ◽  
Noise Measurements ◽  
Noise Data ◽  
The Right ◽  
Theoretical Simulations ◽  
Noise Map

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.

Acoustic Models for Dense- and Open-Graded Asphalt Pavement

2014 ◽  
Vol 587-589 ◽  
pp. 996-1001
Author(s):  
Yang Liu
Keyword(s):  
Noise Level ◽  
Unobserved Heterogeneity ◽  
Low Noise ◽  
Void Content ◽  
Low Frequencies ◽  
High Frequencies ◽  
Frequency Bands ◽  
Acoustic Models ◽  
Noise Data ◽  
Pavement Noise

Empirical acoustic models were developed for dense-graded and open-graded asphalt concrete. Tire/pavement noise data were collected from in-service flexible pavements at different frequency bands for four consecutive years. These data were panel structured, and with a portion of observations missed arbitrarily. A Monte Carlo Markov Chain (MCMC) sampling and a multiple imputation (MI) algorithm were used to capture the unobserved heterogeneity and deal with missing observations by Bayesian simulations that are associated with the data. Models for the two mixes at different frequency bands were constructed. Major findings of the study include: first, tire/pavement noise increases with age at all frequency bands; second, tire/pavement noise level increases with air-void content of the surface mixes at medium and high frequencies but decreases at low frequencies; third, tire/pavement noise level increases with mean profile depth (MPD) at low and medium frequencies but decreases at high frequencies; and fourth, open-graded mix has low noise level compared to its dense-graded counterpart.

Using Noise Data From Underwater Gas Vents To Estimate the Noise Level of Subsea Blowouts

1976 ◽  
Vol 28 (03) ◽  
pp. 259-262
Author(s):  
J.R. Sheffield ◽  
W.F. Wienzek
Keyword(s):  
Noise Level ◽  
Noise Data

scholarly journals Influence of Honking on the Road Traffic Noise Generated at Urban Rotaries for Heterogeneous Traffic

2020 ◽  
Vol 24 (1) ◽  
pp. 23-42
Author(s):  
Kamineni Aditya ◽  
Venkaiah Chowdary
Keyword(s):  
Noise Level ◽  
Road Traffic ◽  
Noise Analysis ◽  
Traffic Noise ◽  
Heterogeneous Traffic ◽  
Road Traffic Noise ◽  
Good Precision ◽  
Heavy Vehicles ◽  
Noise Levels ◽  
Noise Data

AbstractThis paper presents a study conducted at major rotaries for quantifying the traffic noise levels by considering the vehicle volume and their respective honking as governing parameters for heterogeneous traffic. Traffic volume and traffic noise data was collected using a digital video camera and a class 1 sound level meter, respectively. The traffic noise data was analysed using noise tools for identifying the noise level variation. The data collected was subjected to statistical analysis for light, medium and heavy vehicles, and their contribution towards noise levels is proven to be effective with the forthright fact that, heavy vehicles and their corresponding honking were majorly affecting the equivalent noise level compared to other vehicular proportion. An equivalent noise level [LAeq (dB)] rise of 2 to 6 dB (A) is solely caused by heavy vehicles, which is an important observation to be considered for traffic noise analysis at the rotaries. Based on the obtained results from one of the rotaries, noise prediction model is developed for estimating the LAeq (dB), which is able to predict the noise levels with good precision when validated with the data collected at second rotary intersection for different vehicle volumes.

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