Local Noise Action Plans
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Best LIFE Environment Projects 2015
RONM VI EN N T E P E R O F I J L E C T T S S E B Best LIFE Environment projects 2015 LIFE Environment Environment LIFE ENVIRONMENT | BEST LIFE ENVIRONMENT PROJECTS 2015 EUROPEAN COMMISSION ENVIRONMENT DIRECTORATE-GENERAL LIFE (“The Financial Instrument for the Environment”) is a programme launched by the European Commission and coordinated by the Environment and Climate Action Directorates-General. The Commission has delegated the im- plementation of many components of the LIFE programme to the Executive Agency for Small and Medium-sized Enterprises (EASME). The contents of the publication “Best LIFE Environment Projects 2015” do not necessarily reflect the opinions of the institutions of the European Union. Authors: Gabriella Camarsa (Environment expert), Justin Toland, Jon Eldridge, Wendy Jones, Joanne Potter, Stephen Jones, Stephen Nottingham, Marianne Geater, Isabelle Rogerson, Derek McGlynn, Carlos de la Paz (NEEMO EEIG), Eva Martínez (NEEMO EEIG, Communications Team Coordinator). Managing Editor: Hervé Martin (European Commission, Environment DG, LIFE D.4). LIFE Focus series coordination: Simon Goss (LIFE Communications Coordinator), Valerie O’Brien (Environment DG, Publications Coordinator). Technical assistance: Chris People, Luule Sinnisov, Cristóbal Gines (NEEMO EEIG). The following people also worked on this issue: Davide Messina, François Delceuillerie (Environment DG, LIFE Unit), Giulia Carboni (EASME, LIFE Unit). Production: Monique Braem (NEEMO EEIG). Graphic design: Daniel Renders, Anita Cortés (NEEMO EEIG). Photos database: Sophie Brynart (NEEMO EEIG). Acknowledgements: Thanks to all LIFE project beneficiaries who contributed comments, photos and other useful material for this report. Photos: Unless otherwise specified; photos are from the respective pro- jects. For reproduction or use of these photos, permission must be sought directly from the copyright holders. -
State Law Reference— Noise Regulation, G.S. 160A-184
State Law reference— Noise regulation, G.S. 160A-184. Sec. 17-8. - Certain noises and sounds prohibited. It shall be unlawful, except as expressly permitted in this chapter, to make, cause, or allow the making of any noise or sound which exceeds the limits set forth in sections 17-9 through 17-13. (Code 1961, § 21-30.1; Ord. No. S2013-025, § 1, 11-18-2013) Sec. 17-9. - Terminology and standards regarding noises and sounds. (a) Terminology and standards. All terminology used in the provisions of sections 17-7 through 17-16 not defined in subsection (b) of this section, shall be in conformance with applicable publications of the American National Standards Institute (ANSI) or its successor body. (b) Definitions: Ambient sound means the total noise in a given environment. A-weighted sound level means the sound pressure level in decibels as measured on a sound level meter using the A-weighting network. The level so read is designated dB(A). A-weighted sound level meter means an instrument which includes an omnidirectional microphone, an amplifier, an output meter, and frequency weighting network for the measurement of sound. A sound meter that meets these requirements shall be utilized for conducting sound measurements. Background noise means ambient sound. Classification of use occupancies. For the purpose of defining the "use occupancy" all premises containing habitually occupied sleeping quarters shall be considered in residential use. All premises containing transient commercial sleeping quarters shall be considered tourist use. All premises containing businesses where sales, professional, or other commercial use is legally permitted shall be considered commercial use. -
Noise Barriers
INDOT’s Goal for Noise Reduction What is Noise? INDOT’s goal for substantial noise Noise is defined as unwanted sound and can come from man-made and natural 2,000 vehicles per hour sounds twice as loud (+10 dB(A)) reduction is to provide at least a 7 sources. Sound levels are measured in decibels (dB) and typically range from 40 as 200 vehicles per hour. dB(A) reduction for first-row receivers to 100 dB. in the year the barrier is constructed. However, conflicts with adjacent Because human hearing is limited in detecting very high and low frequencies, properties may make it impossible to “A-weighting” is commonly applied to sound levels to better characterize their achieve substantial noise reduction at effects on humans. A-weighted sound levels are expressed as dB(A). all impacted receptors. Therefore, the noise reduction design goal for Indiana is 7 dB(A) for a majority (greater than Common Outdoor Common Indoor Traffic at 65 MPH sounds twice as loud (+10 dB(A)) Noise Levels as traffic at 30 MPH. 50 percent) of the first-row receivers. Noise Levels Noise Levels Decibels, dB(A) 110 Rock Band Highway Traffic Noise Barriers: Jet Flyover at 1150ft (350m) 105 What Causes Traffic Noise? 100 Inside Subway Train (NY) The level of highway traffic noise depends on three factors: • Can reduce the loudness of traffic noise by as much Gas Lawn Mower at 3ft (1m) 95 • Volume of traffic as one-half Diesel Truck at 50ft (15m) 90 Food Blender at 3ft (1m) • Do not completely block all traffic noise 85 • Speed of traffic Noise Barriers 80 Garbage Disposal at 3ft (1m) • Can be effective regardless of the material used • Number of multi-axle vehicles 75 Shouting at 10ft (3m) • Must be tall and long with no openings Gas Lawn Mower at 100ft (30m) As any of these factors increase, noise levels increase. -
Problems in Residential Design for Ventilation and Noise
Proceedings of the Institute of Acoustics PROBLEMS IN RESIDENTIAL DESIGN FOR VENTILATION AND NOISE J Harvie-Clark Apex Acoustics Ltd, Gateshead, UK (email: [email protected]) M J Siddall LEAP : Low Energy Architectural Practice, Durham, UK (email: [email protected]) & Northumbria University, Newcastle upon Tyne, UK ([email protected]) 1. ABSTRACT This paper addresses three broad problems in residential design for achieving sufficient ventilation provision with reasonable internal ambient noise levels. The first problem is insufficient qualification of the ventilation conditions that should be achieved while meeting the internal ambient noise level limits. Requirements from different Planning Authorities vary widely; qualification of the ventilation condition is proposed. The second problem concerns the feasibility of natural ventilation with background ventilators; the practical result falls between Building Control and Planning Authority such that appropriate ventilation and internal noise limits may not both be achieved. Greater coordination between planning guidance and Building Regulations is suggested. The third problem concerns noise from mechanical systems that is currently entirely unregulated, yet again can preclude residents from enjoying reasonable indoor air quality and noise levels simultaneously. Surveys from over 1000 dwellings are reviewed, and suitable noise limits for mechanical services are identified. It is suggested that commissioning measurements by third party accredited bodies are required in all cases as the only reliable means to ensure that that the intended conditions are achieved in practice. 2. INTRODUCTION The adverse effects of noise on residents are well known; the general limits for internal ambient noise levels are described in the World Health Organisations Guidelines for Community Noise (GCN)1, Night Noise Guidelines (NNG)3 and BS 82332. -
Active Noise Systems for Reducing Outdoor Noise
ACTIVE NOISE SYSTEMS FOR REDUCING OUTDOOR NOISE Biagini Massimiliano, Borchi Francesco, Carfagni Monica, Fibucchi Leonardo, and Lapini Alessandro Dipartimento di Ingegneria Industriale, Università di Firenze, via Santa Marta, 3 - I-50139 , Firenze, Italy email: [email protected] Argenti Fabrizio Dipartimento di Ingegneria dell’Informazione, Università di Firenze, via Santa Marta, 3 - I-50139 , Firenze, Italy An ANC system prototype designed for stationary noise control linked to traditional noise barriers was initially developed by authors in the past years. The encouraging results initially obtained have shown that ANC systems are feasible and should be further investigated to improve their performances. Nevertheless, FXLMS algorithm, considered in the first prototype architecture, is known to be affected by convergence problems that generally arise when a system fails to properly identify the noise to be cancelled; this process may (and often does) yield an instable system, where the control algorithm tries to catch-up its own sound, increasing the overall sound pressure level on the targets as in “avalanche” effect, potentially leading to damage fragile instrumentation. Hence, instability is unacceptable in practical applications and must be avoided or prevented. With this respect, in this manuscript some more robust alternative ANC algorithms have been investigated and experimentally verified. Keywords: active noise control, stability. 1. Introduction Active noise control (ANC) techniques are nowadays becoming more and more refined and reached a high level of maturity that allowed integration in modern acoustic devices, mainly targeted to hearing aids, headphones and propagation of noise in ducts [1, 2]. Only in recent years applications considering open field scenarios have been developed in practice (open spaces, ambient noise prop- agation [3, 4]), being such situations affected by weather phenomena, randomly moving sources and, circumstantially, time-varying emission spectrum. -
SCDOT Traffic Noise Abatement Policy
CONTENTS SECTION 1: INTRODUCTION Page 4 1.1 What is noise? Page 4 1.2 How is noise measured? Page 4 1.3 How have noise regulations evolved over time? Page 4 1.4 What is the purpose and applicability of this policy? Page 5 1.5 When is a noise analysis needed? Page 5 SECTION 2: DEFINITIONS Page 6 SECTION 3: TYPES OF SCDOT NOISE ANALYSIS Page 10 3.1 Scoping the Level of Noise Analysis Page 10 3.2 What are the required elements of a SCDOT detailed noise analysis? Page 12 3.3 What are the required elements and/or considerations of a SCDOT final design Page 13 noise analysis? SECTION 4: ELEMENTS OF A SCDOT NOISE ANALYSIS Page 13 4.1 Average Pavement Page 13 4.2 Noise Contours Page 14 4.3 Traffic Characteristics Page 14 4.4 Posted vs. Design Speeds Page 14 4.5 TNM Input Parameters for a SCDOT Noise Analysis Page 14 Receivers Page 14 Roadways Page 16 4.6 Required Additional TNM Input Parameters Page 16 Receivers Page 17 Roadways Page 17 Building Rows/Terrain Lines/Ground Zones/Tree Zones Page 17 4.7 Quality Assurance/Quality Control Page 17 SECTION 5: ANALYSIS OF TRAFFIC NOISE IMPACTS Page 17 5.1 Field Noise Measurements Page 17 5.2 Model Validation Page 19 5.3 Model Calibration Page 19 5.4 Prediction of Future Highway Traffic Noise Levels for Study Alternatives Page 20 5.5 Identification of Highway Traffic Noise Impacts for Study Alternations Page 20 Activity Category A Page 21 Activity Category B Page 22 Activity Category C Page 22 Activity Category D Page 22 Activity Category E Page 23 Activity Category F Page 23 Activity Category G Page 23 -
Survey of Noise in Image and Efficient Technique for Noise Reduction
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2015): 78.96 | Impact Factor (2015): 6.391 Survey of Noise in Image and Efficient Technique for Noise Reduction Arti Singh1, Madhu2 1, 2Research Scholar, Computer Science, BBAU University, Lucknow , Uttar Pradesh, India Abstract: Removing noise from the image is big challenge for researcher because removal of noise in image causes the artifacts and image blurring. Noise occurred in image during the time of capturing and transmission of the image. There are many methods for noise removal from the images. Many algorithms and techniques are available for removing noise from image, but each method exist their own assumptions, merits and demerits. Noise reduction algorithms for remove noise is totally depends on what type of noise occur in the image. In this paper, focus on some important type of noise and noise removal techniques is done. Keywords: Impulse noise, Gaussian noise , Speckle noise ,Poisson noise, mean filter, median filter ,etc 1. Introduction or over heated faulty component can cause noise arise in image because of sharp and sudden changes of Digital image processing is using of algorithms for image signal. 퐼 푖, 푗 푥 < 푙 improve quality order of digital image .This error on 퐼푠푝 푖, 푗 = image is called noise in image which do not reflect real 퐼푚푖푛 + 푌 퐼푚푎푥 −퐼푚푖푛 푥 > 푙 intensities of actual scene. There are two problems found x,y∈[0,1] are two uniformly distributed random variables in image processing: Blurring and image noise. Noise image occurred by many reasons: When we capture image from camera (scratches are available in camera). -
Sound, Noise and Vibration an Explanation
Sound, Noise and Vibration An explanation Rupert Thornely-Taylor P5 (1) HOL/10002/0002 Outline of Presentation • What sound is - sources, and ways in which is it transmitted from source to receiver. • What vibration is - sources, and ways in which is it transmitted from source to receiver. • Human perception of sound and vibration. • Measurement scales and indices. • Assessment approaches - relationship between noise and vibration and human response to them. • Ways in which noise and vibration and their effects can be reduced. • Government policy regarding assessment and decision making. • HS2's application of government policy. P5 (2) HOL/10002/0003 Scope of sound and vibration issues SURFACE OPERATION - RAILWAY SURFACE OPERATION - FIXED PLANT UNDERGROUND OPERATION SURFACE CONSTRUCTION TUNNEL CONSTRUCTION P5 (3) HOL/10002/0004 Basics – what sound is • Sound is air oscillation that is propagated by wave motion at frequencies between 20 cycles/per second (Hertz, abbreviated Hz) and 20,000 cycles/second (20kHz). • Sound decays with distance – it spreads out, is reduced (attenuated) by soft ground surfaces and by intervening obstacles. • Sound is measured in frequency – weighted decibels (dBA) approximating the response of the human ear. • Noise is unwanted sound, which is difficult to measure due to the complexity of the human ear. P5 (4) HOL/10002/0005 Basics – what vibration is • Vibration is oscillation of solids that can be propagated through wave motion. • Vibration in soil decays with distance and is also attenuated by energy absorption in the soil and by obstacles and discontinuities. • Vibration is mainly of interest in the frequency range 0.5Hz to 250Hz and can give rise to audible sound which is then measured in decibels. -
Noise Tutorial Part IV ~ Noise Factor
Noise Tutorial Part IV ~ Noise Factor Whitham D. Reeve Anchorage, Alaska USA See last page for document information Noise Tutorial IV ~ Noise Factor Abstract: With the exception of some solar radio bursts, the extraterrestrial emissions received on Earth’s surface are very weak. Noise places a limit on the minimum detection capabilities of a radio telescope and may mask or corrupt these weak emissions. An understanding of noise and its measurement will help observers minimize its effects. This paper is a tutorial and includes six parts. Table of Contents Page Part I ~ Noise Concepts 1-1 Introduction 1-2 Basic noise sources 1-3 Noise amplitude 1-4 References Part II ~ Additional Noise Concepts 2-1 Noise spectrum 2-2 Noise bandwidth 2-3 Noise temperature 2-4 Noise power 2-5 Combinations of noisy resistors 2-6 References Part III ~ Attenuator and Amplifier Noise 3-1 Attenuation effects on noise temperature 3-2 Amplifier noise 3-3 Cascaded amplifiers 3-4 References Part IV ~ Noise Factor 4-1 Noise factor and noise figure 4-1 4-2 Noise factor of cascaded devices 4-7 4-3 References 4-11 Part V ~ Noise Measurements Concepts 5-1 General considerations for noise factor measurements 5-2 Noise factor measurements with the Y-factor method 5-3 References Part VI ~ Noise Measurements with a Spectrum Analyzer 6-1 Noise factor measurements with a spectrum analyzer 6-2 References See last page for document information Noise Tutorial IV ~ Noise Factor Part IV ~ Noise Factor 4-1. Noise factor and noise figure Noise factor and noise figure indicates the noisiness of a radio frequency device by comparing it to a reference noise source. -
Measurement of In-Band Optical Noise Spectral Density 1
Measurement of In-Band Optical Noise Spectral Density 1 Measurement of In-Band Optical Noise Spectral Density Sylvain Almonacil, Matteo Lonardi, Philippe Jennevé and Nicolas Dubreuil We present a method to measure the spectral density of in-band optical transmission impairments without coherent electrical reception and digital signal processing at the receiver. We determine the method’s accuracy by numerical simulations and show experimentally its feasibility, including the measure of in-band nonlinear distortions power densities. I. INTRODUCTION UBIQUITUS and accurate measurement of the noise power, and its spectral characteristics, as well as the determination and quantification of the different noise sources are required to design future dynamic, low-margin, and intelligent optical networks, especially in open cable design, where the optical line must be intrinsically characterized. In optical communications, performance is degraded by a plurality of impairments, such as the amplified spontaneous emission (ASE) due to Erbium doped-fiber amplifiers (EDFAs), the transmitter-receiver (TX-RX) imperfection noise, and the power-dependent Kerr-induced nonlinear impairments (NLI) [1]. Optical spectrum-based measurement techniques are routinely used to measure the out-of-band optical signal-to-noise ratio (OSNR) [2]. However, they fail in providing a correct assessment of the signal-to-noise ratio (SNR) and in-band noise statistical properties. Whereas the ASE noise is uniformly distributed in the whole EDFA spectral band, TX-RX noise and NLI mainly occur within the signal band [3]. Once the latter impairments dominate, optical spectrum-based OSNR monitoring fails to predict the system performance [4]. Lately, the scientific community has significantly worked on assessing the noise spectral characteristics and their impact on the SNR, trying to exploit the information in the digital domain by digital signal processing (DSP) or machine learning. -
Monitoring the Acoustic Performance of Low- Noise Pavements
Monitoring the acoustic performance of low- noise pavements Carlos Ribeiro Bruitparif, France. Fanny Mietlicki Bruitparif, France. Matthieu Sineau Bruitparif, France. Jérôme Lefebvre City of Paris, France. Kevin Ibtaten City of Paris, France. Summary In 2012, the City of Paris began an experiment on a 200 m section of the Paris ring road to test the use of low-noise pavement surfaces and their acoustic and mechanical durability over time, in a context of heavy road traffic. At the end of the HARMONICA project supported by the European LIFE project, Bruitparif maintained a permanent noise measurement station in order to monitor the acoustic efficiency of the pavement over several years. Similar follow-ups have recently been implemented by Bruitparif in the vicinity of dwellings near major road infrastructures crossing Ile- de-France territory, such as the A4 and A6 motorways. The operation of the permanent measurement stations will allow the acoustic performance of the new pavements to be monitored over time. Bruitparif is a partner in the European LIFE "COOL AND LOW NOISE ASPHALT" project led by the City of Paris. The aim of this project is to test three innovative asphalt pavement formulas to fight against noise pollution and global warming at three sites in Paris that are heavily exposed to road noise. Asphalt mixes combine sound, thermal and mechanical properties, in particular durability. 1. Introduction than 1.2 million vehicles with up to 270,000 vehicles per day in some places): Reducing noise generated by road traffic in urban x the publication by Bruitparif of the results of areas involves a combination of several actions. -
Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction
Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction Santosh Paudel Ajay Kumar Shrestha Pradip Singh Maharjan Rameshwar Rijal Computer and Electronics Computer and Electronics Computer and Electronics Computer and Electronics Engineering Engineering Engineering Engineering KEC, TU KEC, TU KEC, TU KEC, TU Lalitpur, Nepal Lalitpur, Nepal Lalitpur, Nepal Lalitpur, Nepal [email protected] [email protected] [email protected] [email protected] Abstract—During the acquisition of an image from its systems such as laser, acoustics and SAR (Synthetic source, noise always becomes integral part of it. Various Aperture Radar) images [3]. algorithms have been used in past to denoise the images. Image denoising still has scope for improvement. Visual This paper introduces different types of noise to be information transmitted in the form of digital images has considered in an image and analyzed for various spatial become a considerable method of communication in the and transforms domain filters by considering the image modern age, but the image obtained after transmission is metrics such as mean square error (MSE), root mean often corrupted due to noise. In this paper, we review the squared error (RMSE), Peak Signal to Noise Ratio existing denoising algorithms such as filtering approach (PSNR) and universal quality index (UQI). and wavelets based approach, and then perform their comparative study with bilateral filters. We use different II. BACKGROUND noise models to describe additive and multiplicative noise The Wavelet Transform (WT) is a powerful tool of in an image. Based on the samples of degraded pixel signal and image processing, which has been neighborhoods as inputs, the output of an efficient filtering successfully used in many scientific fields such as signal approach has shown a better image denoising performance.