What Will Happen if Noise Pollution Continues

Environmental Impacts of Mining

Ravi K. Jain Ph.D., P.E. , ... Jeremy K. Domen M.S. , in Environmental Impact of Mining and Mineral Processing, 2016

Noise Pollution

Noise pollution can be defined as any disturbing or unwanted noise that interferes or harms humans or wildlife. Although noise constantly surrounds us, noise pollution generally receives less attention than water quality and air quality issues because it cannot be seen, tasted, or smelled. Noise generated by mining operations is often of higher intensity than natural noise, and mining operations can occur throughout the night. Common mining and mineral processing activities that contribute to noise pollution include overburden removal, drilling and blasting, excavating, crushing, loading and unloading, vehicular traffic, and the use of generators.

Noise pollution has a negative impact on wildlife species by reducing habitat quality, increasing stress levels, and masking other sounds. Chronic noise exposure is especially disruptive for species that rely on sound for communication or hunting (Bayne et al., 2008). Animals that use noise for hunting, such as bats and owls, and prey species that rely on noise to detect predators may have decreased patterns of foraging, reducing growth and survivability (Barber et al., 2010; Kight and Swaddle, 2011). Additionally, bird species that rely on vocal communication and other various species, such as nocturnal animals, haven been shown to avoid areas with noise pollution (Barber et al., 2010; Bayne et al., 2008). Reductions in bird populations and foraging activities can in turn negatively impact seed dispersion, affecting ecosystem services and diversity (Francis et al., 2012). Because much of the noise pollution in natural habitats is caused by vehicle traffic, generators, and development in general, noise pollution often exacerbates the problems associated with habitat destruction and fragmentation (Barber et al., 2010).

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780128040409000048

Abatement of Noise Pollution

Dilip Kumar , Deepak Kumar , in Sustainable Management of Coal Preparation, 2018

Abstract

Noise pollution is unwanted sound, it needs to be controlled to make the workplace comfortable. This chapter analyses noise mathematically and the effects of multiple sources are examined. Two noises of exactly the same level can have a combined noise level that is 3  dB higher than the individual values. The greater the difference between the two individual noise sources, the lower is the combined noise level. Different people react differently to the same type of noise. A noise level up to 90   dB does not have any appreciable effect. Exposure in excess of 115   dB is not permitted with unprotected ears as it runs the risk of hearing impairment. The average noise level of various equipment used inside the washery generally ranges from 85 to 110   dB. Various control measures for the abatement of noise pollution have been studied. The hierarchy of control for a reduction of hearing loss to personnel is illustrated.

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780128126325000136

ASSESSMENT AND EXPLOITATION

Harsh Gupta , Sukanta Roy , in Geothermal Energy, 2007

Noise

Noise pollution during the construction and operation stages of a geothermal power plant includes those by drilling and maintenance (90–120 dB) and discharge of fluids (~120 dB). Brown (1995, 2000) has compared these noise levels with other common sounds that occur in our day-to-day life. The only way to mitigate the sound pollution is to ensure softening of the noise using appropriate silencers, to levels lower than the pain threshold of human ear (120 dB in the frequency range 2,000–4,000 Hz).

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B978044452875950006X

FILLERS IN DIFFERENT PRODUCTS

George Wypych , in Handbook of Fillers (Fourth Edition), 2016

19.26 NOISE DAMPENING

Noise pollution can be reduced by controlling the dampening characteristics of a material. The dampening material converts the energy of vibration to heat rather than emitting it to air. ²¹⁸ Incorporation of fillers gives such characteristics.

Figure 19.25 shows the effect of filler type on vibration dampening. Mica was the most effective filler in this group because of its platelet structure. Figure 19.26 shows the effect of concentration of mica on dampening properties. The dampening characteristic is improved with higher filler concentration. Overloading with filler spoils the effect (dampening properties of material are reduced if the filler concentration is increased beyond a certain level).

Figure 19.25. The effect of filler type on noise dampening properties of IPN.

[Data from Li, S; Peng, W; Lu, X, Int. J. Polym. Mat., 29, 1-2, 37-42, 1995.] Copyright © 1995

Figure 19.26. The effect of mica concentration on noise dampening properties of IPN.

[Data from Li, S; Peng, W; Lu, X, Int. J. Polym. Mat., 29, 1-2, 37-42, 1995.] Copyright © 1995

Disc drive assembly for computer hard drives has been developed which is designed to dampen noise by use of composite containing chopped glass fiber. ²¹⁹

Magnetically-filled elastomers are employed in vibration dampening devices. ²²⁰ Polyurethane is elastomer in question and it is filled with magnetic filler, such as, strontium ferrite. ²²⁰ Filler particles were aligned in order to perform vibration dampening function. ²²⁰

A hybrid material consisting of open cell aluminum foam as "skeleton" with polymeric material introduced into the open pores can be used for vibration dampening. ²²¹

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B978189519891150021X

Green Streets for Noise Reduction

Ana M. Lacasta , ... Inma R. Cantalapiedra , in Nature Based Strategies for Urban and Building Sustainability, 2018

Introduction

Noise pollution in urban environments is a frequent cause of discomfort, health, and psychological problems ( IGCB, 2010). Potential effects of noise include speech interference, ear discomfort, sleep disturbance alterations in concentration capacity, decrease of productivity, and problems in children's learning (Kang, 2006).

Although there are many sources of noise related with people activities and machinery, road traffic is the most important urban noise. The outdoor noise level that arrives to a receptor depends on the kind and speed of the vehicle, the distance between the source and the receiver, likewise the obstacles between them and the characteristics of the environment that can affect the sound propagation, besides their subjective effects. Many of the measures usually used to control noise in highways or industrial environments, as high noise barriers, cannot be used in dense urban emplacements, because of space limitations, safety, or visual impacts.

Using vegetation in the reduction of urban noise is a concept that has gained much attention around the world. Studies on tree belts (Fang and Ling, 2003; Islam et al., 2012) showed important noise attenuations, being their density, height, length, and width the most effective factors. Width of vegetation belts is also a significant noise reduction factor, because of the increment in the sound absorption and dissipation with larger acoustic pathway.

In urban streets, when the integration of wide tree belts is not a feasible solution, the disposal of hedges and dense shrubs can be a very useful option in reducing noise (Fang and Ling, 2003; Kalansuriya et al., 2009; Van Renterghem et al., 2015). Another possibility is to combine vegetation with solid walls (greenery barriers) (Dunnett and Kingsbury, 2004; Wong et al., 2010; Daltrop and Hodgson, 2012; Hodgson et al., 2013). Both plants and air between the vegetation increase the acoustic attenuation of the wall. Such greenery barriers can be placed in the streets near to the source and the receiver, increasing their effectiveness. Vegetation can reduce the traffic noise level, especially in narrow streets with hard facades. While multiple reflections in buildings lead to an amplification of the noise, the acoustic absorption by plants, placed along the street or covering facades and roofs, avoid such amplification (Van Renterghem et al., 2015).

In addition to the physical noise reduction, vegetation by itself affects noise perception positively. Many studies indicate that an urban sound scene is not perceived in isolation, but other sensory modalities such as vision interact with auditory information and modify the sound perception (Viollon et al., 2002). In this sense, the vision of vegetation elements reduces the noise annoyance (Van Renterghem and Botteldooren, 2016). Other studies indicate that the perception of natural soundscapes, such as bird sounds, decreases the perceived level of traffic noise (De Coensel et al., 2011; Hong and Jeon, 2013; Preis et al., 2015).

This paper summarizes some relevant outcomes found in literature about the physical reduction of noise in green streets, as well as those related with the improvement in the sound perception in presence of vegetation. An example is also presented, corresponding to a greenery barrier in a main artery of the city of Barcelona.

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780128121504000173

Porous alkali-activated materials

Priyadharshini Perumal , ... Mirja Illikainen , in New Materials in Civil Engineering, 2020

15.5.2 Sound absorption

Noise pollution in both indoor and outdoor environments is an increasing problem. For instance, it has been estimated that, in Europe alone, at least one million healthy life-years are lost due to traffic noise [112]. One option to reduce excessive and harmful noises is sound-absorbing materials, in which the acoustic energy is dissipated (into heat energy) via viscous flow, internal friction, and panel vibration [113]. Three main parameters affecting the effectiveness of sound absorption are pore sizes, degree of open porosity, and material thickness [113]. In the case of AAMs, these can be largely controlled by the production method (see Section 15.2 ), aggregate/filler selection, and by selection of the precursor and alkali activator, as discussed below.

The efficiency of sound-absorbing materials is frequently characterized by the absorption coefficient (α), which is the proportion of the absorbed energy on the surface, according to the standards ISO 10534-2 or ASTM E1050. The human hearing range is usually 20–20,000   Hz, but the lowest intensity to reach hearing threshold is required between 2000 and 5000   Hz [114]. Another parameter used to describe acoustic properties is the sound transmission loss (STL), which is the decrease in sound intensity at different frequencies as it passes through a sample.

Pervious AAM concretes or mortars have been studied as sound-absorbing materials. They are prepared by adding lower than optimum amount of binder into aggregate/filler mixture to result in empty voids. For instance, Perna et al. [115] found that kaolinite-based geopolymer mortar (20% binder and 80% aggregate/filler) had maximum sound absorption (α=0.7–0.9) at 1200 and 2000   Hz when the sample thickness was 30 or 20   mm, respectively. Moreover, increasing the content of geopolymer binder (kaolinite and activator) to over 20% of the mixture decreased sound absorption [115]. Gandoman and Kokabi [116] used geopolymer (binder consisting of metakaolin and NaOH) concrete (aggregate/metakaolin=4.5) containing rubber waste (0%–14% of aggregate) and observed sound absorption maximums (highest α=0.35–0.40) at 1000 and 3000–4000   Hz frequencies. When the rubber content increased, the ability to absorb sound also increased [116]. Chang et al. [117] prepared pervious geopolymer concrete from blast furnace slag activated with sodium silicate and hydroxide containing electric arc furnace slag and gravel as aggregates. They observed maximum sound absorption (highest α ≈ 0.45) at 500   Hz and the sound absorption increased as the amount of binder decreased (i.e., the concrete was more pervious with open porosity up to 25%) [117]. Arenas et al. [118] prepared porous AAM concrete (class F fly ash activated with sodium silicate/hydroxide) for an outdoor traffic noise absorber by incorporating coarse demolition waste aggregate into mortar. Their materials exhibited two peaks: at approximately 1000   Hz (α=0.7–0.9) and at 3000   Hz (α=0.45–0.65), increased sample thickness caused better absorption of low frequencies, and the more porous demolition waste aggregates performed better than natural coarse aggregate [118].

Another group of AAM-based acoustic materials has been prepared using the foaming methods described in Section 15.2 . Zhang et al. [104] prepared foamed AAM concrete from fly ash and slag activated with sodium silicate and hydroxide using the prefoaming method (generation of foam from water–surfactant mixture using foam generator). Their material was especially effective at absorbing low frequencies (40–150   Hz) with an α of approximately 0.7–1 [104]. When the slag content was increased, higher frequencies (800–1600   Hz) were absorbed more effectively (α up to ≈ 0.3) [104]. An increase of the foam dose from 5% to 10% decreased absorption at low frequencies but improved absorption at the medium range (600–1000   Hz) [104]. Papa et al. [60,75] prepared foamed AAMs from silica fume with or without metakaolin activated with Na/K hydroxide and silicate. Their materials exhibited two peaks in sound absorption: one at 500–2000   Hz and another at approximately 4500–5500   Hz [60]. The highest absorption (α=0.8–0.9) was obtained with material containing silica fume and metakaolin activated with potassium-based activator: the difference compared to a sodium-based system was significant (in that case α=0.4–0.5), which might have been because of the larger pore sizes (especially 500–1700   μm diameter) of potassium-based material [60]. Mastali et al. [69] prepared fiber-reinforced alkali-activated blast furnace slag foam concrete using lightweight aggregates made by alkali activation of by-product from sponge iron manufacturing (Petrit-T). It was observed that a foam dose of 35% resulted in material with high sound absorption (α=0.8–1) in the range 1600–2500   Hz and the density had a linear correlation with sound absorption coefficient [69]. Stolz, Boluk, and Bindiganavile [119] prepared fiber-reinforced fly ash-based prefoamed alkali-activated concrete. Their material had high sound absorption (α up to 0.7–0.85) at low frequencies (125–250   Hz), but also moderate sound absorption (α up to 0.5) at high frequencies (4000–6300   Hz). The optimum density in terms of noise reduction coefficient (the average of sound absorption coefficients at 250, 500, 1000, and 2000   Hz) was 1130   g/cm³ (lower or higher density decreased sound absorption) [119]. Luna-Galiano et al. [65] used silica fume as a blowing agent for the preparation of fly ash-based sound-absorbing material: their material had α <0.3 with maximum absorption occurring at approximately 400 and 2500   Hz. They observed that an increase in the silica fume content (up to 40%) and curing temperature (up to 70°C) improved the acoustic properties [65].

To summarize, porous AAMs appear as promising materials for the absorption of sound. However, the most efficient sound absorption range of AAM seems to vary from one material to another and is likely due to porosity differences induced by the production method and different precursors.

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780128189610000156

Color Selection for Environments by Industry

Doreen Becker , in Color Trends and Selection for Product Design, 2016

Noise Colors

As noise pollution to continues to increase with the urbanization of culture, noise filtering or masking is becoming more important to enable people to sleep, think and work more efficiently. White noise was probably the first color to be identified and was used in doctors and therapists offices to provide privacy between patients in the office and those in the waiting room. These little white noise machines provide a full spectrum sound that can mask conversations or external street noise. The sound is similar to static from an untuned FM radio and has been used to help people relax. The reason the white "color" was chosen was because, like white light, the sonic frequencies are unfiltered, similar to the visible spectrum where white light transmits all colors unless filtered by a prism. If compared to the colors of the rainbow, red colors have the lowest frequencies and blue colors have the highest frequencies. Dark Red or Brown noise is the lowest color noise (currently) and has a low, rumbling quality to it that is somewhat random and has a distant relationship to Brownian Motion and its random movement of particles through a liquid or gas. Pink noise is a little bit higher in frequency and is similar to the constant sound inside a passenger jet. The opposite of Pink noise is Blue noise. This is a much higher-frequency noise that is quite unpleasant but is quite effective in blocking out unwanted, lower frequencies. It sounds more like a hiss. Most sound machines and apps offer White, Pink and Brown noise but leave out the Blue noise and add in multitonal combinations of waterfalls, rain, birds, etc. to help people relax or focus.

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780323393959000232

Monitoring urban noise

Sanja Grubeša , ... Ivan Djurek , in Start-Up Creation (Second Edition), 2020

15.3.3 Results

A map of noise pollution obtained from the interpolated noise values, where values lower than 65  dB are considered low (colored green), values between 65 and 75   dB are considered slightly elevated (colored yellow), and values greater than 75   dB show high noise levels (colored red), is shown in Fig. 15.16. This particular noise map has been obtained from our previously described MCS trial (Marjanovic et al., 2017). As expected, greater noise values can be observed at crossroads and near busy roads (i.e., noise values higher than 75   dB), whereas parks have noise values lower than 65   dB and therefore represent the quiet areas within a typical urban environment. Bearing in mind all of the aforementioned, a legit question arises, how do we actually know, and can we confirm that all of the obtained data for creating our noise map are precise and accurate? The answer to these questions will be discussed in the following section.

Figure 15.16. Noise level map (Marjanovic et al., 2017).

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780128199466000151

Freight-Transportation Externalities

Fatemeh Ranaiefar , Amelia Regan , in Logistics Operations and Management, 2011

16.3.3 Noise Pollution

Noise pollution is more of a concern in urban than rural transportation systems. Some studies even assume external costs of zero for noise pollution in rural areas [27]. Medium to heavy trucks are 10–18 decibels (dB) louder than passenger cars [27]. Truck noise is annoying to residents and pedestrians. Therefore, truck operations during evening and night hours are restricted or prohibited in some areas. Transport noise above a threshold can increase or cause health problems such as changes in heartbeat frequency, increases in blood pressure, hormonal changes, and sleeping problems. The external costs of noise pollution have been studied extensively in Europe and the United States [10,27,29,31,32].

The external cost of noise is mainly reflected in property values when people are less willing to pay for areas near highways. However, this is independent from the health-related costs of noise pollution.

The level of truck noise pollution is influenced by several factors such as vehicle speeds, traffic flow (free flow vs. stop and go), road surfaces, weather, and vehicle type and conditions. The index used for noise is the energy mean sound level, [dB(A) ⁶ ]. It gives the average sound level over a given period. Noise has a logarithmic relationship with traffic volume. This means that marginal noise pollution decreases as traffic flow increases. In other words, if the current traffic is medium to high, then the marginal noise pollution is small and probably below average, but in low traffic the marginal level can be much higher than average. Time of day, population density exposed to the pollution, and existing noise level are the main drivers of noise cost [32]. Noise levels also decrease with an almost linear relationship with distance from the source. At about 1000   ft (0.3048   km) from the highway, the noise level reaches the background level [27].

In the United States, the first noise-estimation models were developed by the Federal Highway Administration (FHWA) and the National Cooperative Highway Research Program in the late 1960s. The most common model for vehicle traffic is the FHWA's software TNM 2.5 model. Haling and Cohen [16] provide a review of noise-estimation and -prediction models. They also estimate the noise cost produced by trucks of different sizes and carrying different loads using a hedonic price method. This method is based on the reduction of property values caused by vehicle noise emissions. In a hedonic price technique, the actual value of a residential property is dependent on both the physical characteristics of the property and environmental attributes such as pollution levels. Their results show a large variation of noise cost, depending on the type of vehicle, operating conditions, and location of the roadway in relation to residential areas. Haling and Cohen present the results of their estimation by truck type (number of axles and weight), traffic volume, and land development type (urban, rural). Each category is then classified by vehicle speed. The cost estimations vary from 0 to $0.1148 per vehicle-mile ($0.0713 per vehicle-km) in 1993 prices. Mayeres et al. [34] also used the same technique and applied it for Brussels with classification in the results. They estimated that the marginal cost of noise pollution in Brussels is €0.014 per vehicle-km during peak hours and €0.058 per vehicle-km during off-peak hours (using 2005 prices).

The European Commission studies used two approaches (marginal value and average value) to estimate the noise cost. The extensive report, ExternE [31], provides an estimate of the marginal cost of noise pollution. The noise costs for two simulated scenarios in which one has an additional vehicle are calculated. The second approach is based on willingness to pay to have a more quiet environment. This amount is then multiplied by the number of people exposed to the noise. This approach allows us to calculate the average cost of noise pollution. As mentioned before, in moderate traffic the marginal and average costs of noise are approximately the same, whereas in uncongested or heavily congested situations these costs can be very far apart. Nash [29] estimates that the noise cost of heavy good vehicles (HGVs) for daytime versus nighttime and urban versus rural separately. The daytime cost is between €0.08 and €0.26, and the nighttime cost is between €0.23 and €0.78 per vehicle-km for urban areas and the upper bound of €0.03–0.05 per vehicle-km for daytime and nighttime for interurban areas.

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780123852021000165

Environmental Regulations—Inland and Coastal Desalination Case Studies

J. Jaime Sadhwani Alonso , Noemi Melián-Martel , in Sustainable Desalination Handbook, 2018

10.3.4 Impact of Noise

Also to be highlighted is the noise pollution of a desalination plant, which is not mentioned because of its relative remoteness in villages and inhabited areas, which in principle should avoid public nuisances. However, it should be noted especially in small islands or areas with very limited building land, unfortunately is a very common situation in the Levant Spanish, Canary Islands, and Balearic Islands.

Acoustic contamination on seawater reverse osmosis desalination plant, principally, is important, due to the fact high-pressure pumps and energy recovery systems, such as turbines or similar, produce significant level of noise 90   dB (A) [11]. Therefore, they should be located far away from populated areas and equipped with appropriate acoustic technology to reduce noise level.

Read full chapter

URL:

https://www.sciencedirect.com/science/article/pii/B9780128092408000101

hansonbons1962.blogspot.com

Source: https://www.sciencedirect.com/topics/engineering/noise-pollution