State-of-the-art review on the assessment and modelling of traffic noise intensity on roadside dwellers: The Port Harcourt, Nigeria case

1. Introduction

Urban areas are associated with high levels of noise pollution generated from different sources which may include; industrial and commercial activities, traffic flow (vehicle, engine, and pavement noise), etc., which are promoted by large human population as presented by Onuu (1999) in a research on a review of environmental noise control. According to WHO (2005); in the hand book of partner profile on road safety collaboration, noise pollution in urban cities is considered the third most hazardous type of pollution. Intense noise pollution in urban areas is recognized worldwide as a major problem affecting city livability, and its continued growth is accompanied by an increased number of complaints from people exposed to noise. Negative impacts of high levels of noise pollution have accrued and diverse health effects including mental and physical, ranging from annoyance to difficulty in falling asleep or rest deprivation, high blood pressure, hearing loss, cardiovascular problems as well as reduced human productivity in terms of determination and effort presented by Piccolo at al. (2005); which researched also on environmental noise and found the potential dangers of noise to the total well-being of humans and the environment, Okoro et al. (2016); which researched on levels of sound and found the health-index of the people with respect to noise level, and Anomohanran (2013); which researched on environmental noise pollution assessment and found that noise pollution due to vehicle traffic is heavier during peak periods and affects the psychological well-being of the people beyond the time of influence. The effects of noise in developing countries are just as widespread as those in developed countries, and the long-term consequences for health came. In Nigeria, one of the major contributors to urban noise pollution is traffic noise. The fast-growing vehicle population in urban cities of Nigeria in recent years has resulted in a considerable increase in traffic on roads causing alarming noise pollution in urban and suburban areas of the country. Noise level increases as traffic volume increases in agreement with the findings of Olayinka and Abdulahi (2008) in their statistical analysis of the day-time and night-time noise levels in Ilorin Metropolis, Nigeria. Traffic noise is generated by individual vehicles and the volume of traffic. For individual vehicles, noise combines sounds produced by the engine, transmission, exhaust, the interaction between the tires and road pavement, air turbulence, and body and load rattles. Existing evidence indicating that traffic noise pollution may have negative impacts on human health has justified research to provide a better understanding of noise pollution problems and control. In many Nigeria cities, of which Port Harcourt is one, traffic noise is a major contributor of environmental pollution and now it has become a permanent part of urban and suburban life. In Port Harcourt, a lot of commercial activities are performed along roadways featuring a very high population of persons, some of such persons include open marketers, shop owners which usually trade in wide open spaces with several entrances having less or no control policies. Highways usually pass near, within, or around open markets for commuters to travel along and to enable the hauling of goods in and out of the markets. Port Harcourt has open markets scattered all over where commuters (passers-by) attempt to purchase common products such as food stuffs (snacks or fruits), utensils, toiletries, etc., from hawkers who heave around decelerating and accelerating vehicles around these market areas. This creates a congested traffic situation which leads to the creation of negative transport externalities. The commonest externalities of road side dwellers, including those in open markets, are excessive noise and air pollution from exhaust pipes and background trading activities generated and delivered from within and around the market area, and high travel costs (time and fuel) caused by traffic congestion as shown by Ugwuanyi et al. (2004), in a work which dealt also on environmental noise pollution assessment in a Nigerian city. Sources of this noise pollution include; voices from crowded buyers and sellers, loud speakers used for advertising goods and services, vehicular traffic, and various mechanical devices employed for production/processing activities within these open markets as proposed by Essandoh and Armah (2011) in a finding noise levels in ambient conditions and Okoro et al. (2016) in sound levels. Buyers, sellers, passers-by, and residents within open market areas are exposed to high noise intensity generated from the open markets, which is presented in Onuu (1999), in a research community response to road traffic noise. This noise has a negative impact on the health condition of people who are exposed to it, since it has been established that prolonged exposure to sound louder than 85 dB(A) can cause damage to one's hearing system (WHO, 2005). Therefore, this calls for an investigation to establish a safe noise threshold for roadside traders and commuters passing as they expose themselves to traffic noise pollution. The absence of data on the level of noise pollution emitted from traffic around Port Harcourt has posed a challenge to concern persons from knowing the state of health of road side dwellers. Lack of noise data makes it impossible to ascertain if roadside traders and commuters are actually at risk regarding the level of traffic noise pollution. This therefore prompted efforts to embark on a study to assess the level of noise pollution emitted by commuters passing through Port Harcourt roadways. This study attempts to confront town planners, transport engineers, and policy makers with facts and figures on the magnitude and impact of noise pollution jointly generated by commercial activities and traffic flow around Port Harcourt through the assessment and development of traffic noise prediction models. Noise pollution is one of the major environmental pollutants that are encountered in daily life and has direct effects on human performance. One of such noise pollution is that generated from traffic. In the framework of urban settlements, the assessment and evaluation of traffic noise is essential. According to Djamel (2002), the quality of human life, is heavily influenced by a continuous exposure to acoustical noise exceeding a certain threshold, usually defined in the dedicated country regulation or in international standards. This problem of traffic noise generation cannot be overemphasized because a lot of people are often unavoidably exposed to traffic noise in an urban environment, which has been identified to affect hearing, nervous system, psyche, spoken communication, sleep and performance. Since noise acts as a stressor, an increased burden on the body leads to higher energy consumption and greater wear. It is thus suspected that noise can primarily favor diseases in which stress plays a contributory role, such as cardiovascular diseases, which can then be manifested in the form of hypertension, myocardial infarction, angina pectoris, or even apoplexy as presented by Piccolo et al. (2005), which researched on environmental noise evaluation. Inadequate data of noise emissions and its effect on the citizenry has led to poor planning and control of traffic to reduce this menace. The peculiarities of localities, in terms of roads, kind of vehicles, construction procedures, and weather features necessitated this traffic noise study in Port Harcourt. The city is relatively large, having an increase in population growth rate. Port Harcourt has expanded continuously in all directions in the past decades. Many significant changes have been experienced in terms of urbanization, industrialization, expansion of road network, and infrastructure. The city has been subjected to persistent road traffic and commercial activities due to the increase in development and expansion of the economy. Therefore, the evaluation of the noise impact due to acoustical noise in this relatively large city has become a necessity. This can be achieved both by a measurement campaign and/or by a software simulation. The latter requires a very precise mathematical modelling of the environment, of the sources and of the propagation law of sound. This project seeks to present an overview on the development of traffic noise models for Port Harcourt roadways in two formats. The first format would present a model review to predict traffic noise in terms of traffic volume, speed, and distance in meters from the center of the roadway using a multiple linear regression analysis. The second format would relate traffic noise intensity to time of noise measure using time series analysis, regression analysis, and normal and uniform probability distribution functions.The main aim of this research work is to review the assessment and modelling of the traffic noise intensity of roads in the Port Harcourt Metropolis case through the categorization of roads selected for traffic noise study, measuring the traffic noise intensities along selected routes (flexible and rigid routes) with respect to traffic volume, speed and distance away from the center of the roadway on one hand and with respect to time of noise measurement on the other hand, developing predictive models for noise level estimation in two model formats along selected routes, validation of models developed using an appropriate statistical tool, and comparing noise level generated from both categories of roadways (flexible and rigid).

2. Relevant reviews

2.1. Concept of noise and noise pollution

Noise is usually the term attributed to mean any kind of irritating and obtrusive sound and noise pollution is one of the environmental concerns of the general populace. A consideration of the differences between individuals and the inclusion of acoustical and non-acoustical factors makes it, however, difficult to assess the effects of noise on people as presented in a research by Ouis (2001), on a review which exposed road traffic noise influence on anger. Noise is in general the term by which it is designated any undesirable sound. A more technical and objective distinction between an acoustical signal in its wider sense and noise stems from the fact that the former has particular characteristics both in time and frequency, and which are usually lacking in the case of noise. However, there still is some ambiguity when a reference is made to traffic noise because of the obvious fact that although random, traffic noise has its distinguished spectral and temporal uniformity. In broad terms, these effects can be classified into three main categories: psychological (attitudinal), social (behavioral) and physiological effects as proposed by Daniel (1998) on cause and effects of noise pollution. Noise is thought to evoke physiological responses which are characteristic for stress as described by DeJoy (1984) during a report on the status of research on the cardiovascular effects of noise; A Ph.D thesis of Saadu (1996) on community and occupational noise survey, which enumerate how noise study has led several researchers to consider the hypothesis that long-term noise exposure contributes to the genesis of serious diseases. However, the controversy of the results of epidemiological studies permits to keep this hypothesis only under the assumption of a particular noise sensitivity presented by the affected population as presented in a research by Griefahn and Di Nisi (1992) on noise effect on mood and cardiovascular functions during noise. However, it is a well-established fact that across measurement techniques and cultures, noise-reaction relationships show a remarkable similarity (Job, 1988, Saadu et al., 1998).Noise pollution is defined as excess noise that may be hazardous to human or animal life according to WHO (2005). Major sources of outdoor noise include automobiles, industrial and commercial activities, power generator plants, aircrafts, trains, etc. as described by Onuu (1999). Previous researches stipulate that the major contributor of urban noise is traffic (Anila and Bino, 2013, Menkiti, 2001) and (George and Okeke, 2015). The intensive noise pollution in urban areas is associated with the rapid human population growth with corresponding traffic growth rate as depicted by Oluwasegun et al. (2015). The source of outdoor noise worldwide has been attributed to be caused mainly by machines and transportation systems, motor vehicles, aircraft, and trains as presented in a research in Chicago by Micheal and Gary (1973). The fact that you can't see, taste or smell, it may help explain why it has not received as much attention as other types of pollution, or water pollution, the air around us is constantly filled with sound, yet most of us would probably not say we are surrounded by noise. Although for some, the persistent escalating sources of sound can often be considered annoyance which can have major consequences. Fig. 1 presents a simplified model for the main relationships between noise, its effects, and the social context of people as demonstrated by Nelson (1987). Fig. 1 shows that noise may be represented as the cause of some direct effects and/or more delayed reactions in the form of annoyance.

2.2. Concept of traffic noise pollution

According to Murray and Donald (1971), traffic noise pollution can be best defined as “unwanted traffic noise”. In addition, the sensitivity of the human ear to noise depends on a number of contextual factors, which typically include wind factor, humidity, traffic density, etc. Nevertheless, it is generally accepted that a 55 dB(A) sound will be disturbing, whereas a 65 dB(A) noise level will be deemed intolerable, causing severe sleep disturbance (OECD, 1979). It is reasonable to affirm that, in urban areas, road traffic noise is the most relevant source as described by Li (2017) since airports are usually placed outside the downtown and railways are usually designed to move out from the center of the city as presented by Abramovic (2018) and rarely cross the residential districts as presented by Guarnaccia (2013). This is a problem that has been magnified by the phenomenal traffic increase on the highway systems of this country. Road traffic noise has been identified as a very big challenge for urban planners and environmental engineers to overcome in cities according to the research done by Li et al. (2002). While the physical and psychological effects of highway noise are not of the same magnitude as found in many industries, road traffic noise does constitute an environmental issue for people living near a highway. Continuous high levels of noise can cause serious stress on the auditory and non-auditory and nervous system of the city dwellers as presented in a research by Alam et al. (2006) and Murthy and Khanal (2007). It is also a leading cause of great annoyance for the exposed population due to the poor conditions of engine exhaust etc. as described by Baaj et al. (2001). Because their wheels clattered on paving stones, chariots in ancient Rome were banned from the streets at night to prevent noise that disrupted sleep and caused annoyance to the citizens which was presented by Goines and Hagler (2007). Centuries later, some cities in Medieval Europe either banned horse-drawn carriages and horses from the streets at night or covered the stone streets with straw to reduce noise and to ensure peaceful sleep for the residents.’ In more recent times in Philadelphia, the framers of the Constitution covered nearby cobblestone streets on earth to prevent noise-induced interruptions in their important work. These examples pinpoint two major effects of noise from which men of all ages have sought relief: interruption of sleep and interference with work that requires concentration. It is interesting that noises emanating from the various types of roadways of today are still among the most important sources of environmental noise, even though the types of noise are not those that existed in Rome, Medieval Europe, or 18''' century Philadelphia. Our modem roadways (including road, rail, and air) and the products of modem technology produce increasing levels of unwanted noise of varying types and intensities throughout the day and night that disturb sleep, concentration, and other functions as shown by Lee and Fleming (2007). This noise affects us without our being consciously aware of it. Unlike our eyes, which we can shut to exclude unwanted visual input, we cannot voluntarily shut our ears to exclude unwanted auditory input. Our hearing mechanisms are always “on” even when we are asleep as stimulated by Babisch (2006). Currently all developing countries like Nigeria are facing threats due to vehicular noise pollution. Migration of people from rural to urban areas, expansion of cities, population, and infrastructural growth, and urbanization are important factors resulting in motorization and consequent increase in levels of various urban pollution which was presented by Banerjee et al. (2009). The urban population of Nigeria has increased considerably over the years with a population of 87, 680, 500 in the year 2015 and it's predicted to increase to 132, 547, 150 in the year 2025 according to the United Nations (2015). This increase in population coupled with the increase in the number of motor vehicles is showing alarming levels of traffic congestion, air pollution, and noise pollution and road accidents. Urban traffic noise is one of the most critical types of noise and normally considered more interfering than other types of noise as proposed by Unweltbundesamt (2000) and by Zannin et al. (2003).

2.3. Elements/components of traffic noise

The total noise emanating from a given traffic combines noise coming from different components. These components are generally grouped as in-vehicle noise components and out-vehicle noise components. Lamure (1986) identified the in-vehicle noise components as those from vehicle engine, transmission and silencers, and tire-pavement contact.

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Engine noise component

The explosions inside the cylinders and the impact of the piston against the cylinder walls excite the block and various engine accessories, including in particular the different cases and housings. The latter include the sump case and the rocker arm cover, which often account for a significant proportion of the total noise coming from the engine. The amount of noise radiated by the engine depends on its speed and the load to which it is being submitted, the latter determining the torque that is being produced. The load does not have much effect in the case of a diesel engine, except for delayed indirect injection, which can lead to a 5 dB reduction in the noise leve1 when the engine is lightly loaded. For frequencies above about 500 Hz, the noise level, L in dB(A) at a given frequency increases in proportion to 30logN as described by Lamure (1986).

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Transmission and silencer components

There is no good understanding of the noise coming from the gear box and the transmission, and it is considered that the mechanical excitation here can be partially due to the engine. It is known, moreover, that on certain cars coming from the complete transmission system can be dominant. The simplest sound-proofing measure that can be applied in connection with this noise is the provision of a screen beneath the complete transmission system, which can usually take the form of a simple extension of the screen fitted beneath the engine.

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Tire-pavement contact component

Therefore, far as the generation of noise is concerned, we can classify the surface texture of the road surface in terms of the power spectral density’ of the longitudinal profile for wavelengths ranging from 2 to 200 mm by Sandberg and Descornet (1980) which was proposed. Surfaces having a high texture level give rise in particular to the radial excitation of the tire and the type 1 phenomenon dominates. Surfaces having a low texture level, on the other hand, give rise in particular to type II and type III disturbances. The importance of the impedance of the road surface is also not very well understood. All that is known is that road surfaces having high mechanical impedance (hydraulic concrete or an old bituminous surface) tend to give rise to a greater degree of noise than road surfaces of moderate impedance such as, recently laid bituminous surfaces, although the difference in noise level is not considerable, amounting to only a few decibels as proposed by Lamure (1986).

2.4. Causes of traffic noise

It is a well-established fact that vehicular traffic noise is a major source of community annoyance especially near highways carrying fast traffic. Many people consider truck noise to be the principal offender. Numerous components of noise sources contribute to the overall truck noise. These sources, however, can logically be grouped into major categories as under.

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Power plant and transmission Noise sources

Vehicle noise comes from the engine, transmission, exhaust, and suspension, and is greatest during acceleration, on upgrades, during engine braking, on rough roads, and in stop-and-go traffic conditions. Poor vehicle maintenance is a contributing factor to this noise source.

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Driver behavior

Drivers contribute to road noise by using their vehicle's horns, by playing loud music, by shouting at each other, and by causing their tires to squeal as a result of sudden braking or acceleration.

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Running gear Noise Sources

Tire road interaction differential propeller shaft. Noise from the power plant increases as the engine speed increases, while the noise from the tire increases as the vehicle speed increases. Trucks tend to operate at a nominally constant engine speed, so that engine and exhaust noise do not vary appreciably with vehicle speeds tire-pavement interaction becomes the dominant source of noise is a highly complicated function of such variables as tire characteristics, engine exhaust characteristics, road surface, and vehicle design and condition. As a tire rolls over a road surface, it displaces macroscopic and microscopic volumes of air. The ‘macroscopic’ applies to volume displacements of the same order as the volume of the tire itself and ‘microscopic’ applies to much smaller volume displacements of the same order as the volume of the tire itself, and ‘microscopic’ applies to much smaller volumes. These air displacements generated pressure disturbances in the surrounding air. Pressure disturbances in the audio frequency range and of sufficient amplitude are responsible for the production of noise along the roadway. Road traffic noise in particular is caused by the combination of rolling noise arising from tire-road interaction and propulsion noise comprising engine noise, exhaust systems, and transmission intake. It is generally estimated that tire-road interaction is the main source of noise above 55 km/h for most cars and above 70 km/h for trucks, depending on the age, weight, and driving conditions of the vehicle. Significant progress has been achieved in both sources of noise through new tire designs, such as randomized tread pattern, narrow lateral grooves, etc., and quieter engines through acoustic shielding of the engine and multiple muffler systems. However, there remains much scope for progress - particularly as quieter cars will never eliminate erratic driving behavior, technical defects, or even traffic density, which together can have a multiplying effect on noise emission. In addition, an overall increase in road traffic and the progressive introduction of heavier vehicles have tended to counterbalance the real progress achieved through better car and tire technology.

2.5. Impacts of traffic noise

According to WHO (2005) which documented seven categories of adverse health effects or impacts of noise pollution on humans. The WHO guideline provides an excellent, reasonably up-to-date, and comprehensive overview of noise-related issues, as do other recent reviews on this subject.

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Hearing Impairment

Hearing is essential for well-being and safety. Hearing impairment is typically defined as an increase in the threshold of hearing as clinically assessed by audiometry. Impaired hearing may come from the workplace, from the community, and from a variety of other causes (e.g., trauma, ototoxic drugs, infection, and heredity). There is general agreement that exposure to sound levels less than 70 dB does not produce hearing damage, regardless of the duration of exposure as described by Berglund and Lindvall (1995). There is also a general agreement that exposure for more than 8 h to sound levels in excess of 85 dB is potentially hazardous; to place this in this context, 85 dB is roughly equivalent to the noise of heavy truck traffic on a busy road.’ With sound levels above 85 dB, damage is related to sound pressure (measured in dB) and to time of exposure. The major cause of hearing loss is occupational exposure, although other sources of noise, particularly recreational noise, may produce significant deficits. Studies suggest that children seem to be more vulnerable than adults to noise-induced hearing impairment by Berglund and Lindvall (1995). Noise induced hearing impairment may be accompanied by abnormal loudness perception (loudness recruitment), distortion (paracusis) and tinnitus. Tinnitus may be temporary or may become permanent after prolonged exposure as presented by Berglund and Lindvall (1995). The eventual results of hearing loss are loneliness, depression, impaired speech discrimination, impaired school and job performance, limited job opportunities, and a sense of isolation as described by Suter (1991). The WHO recommends that unprotected exposure to sound levels greater than 100 dB (for example, the sound of a jackhammer or a snowmobile) should be limited in duration (4 h) and frequency (four times/yr) (Berglund and Lindvall, 1995). The threshold for pain is usually given as 140 dB, a level readily achieved in today's boom-cars. Impulse noise exposure (gunfire and similar sources of intense noise of brief duration) should never exceed 140 dB in adults and 120 dB in children. Firecrackers, cap pistols, and other toys can generate sufficient sound levels to cause sudden and permanent hearing loss which was presented in a research by Brookhouser (1996). Levels greater than 165 dB, even for a few milliseconds, are likely to cause acute cochlear damage as described by Berglund and Lindvall (1995). It is important to remember to counsel patients that the ears do not “get used” to loud noise.

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Interference with spoken communication

In 1974, in an attempt to protect public health, and welfare against the adverse effects of noise, the EPA published so-called safe levels of environmental noise that would permit normal communication both in and out of doors.'' Noise pollution interferes with the ability to comprehend normal speech and may lead to a number of personal disabilities, handicaps, and behavioral changes. These include problems with concentration, fatigue, uncertainty, lack of self-confidence, irritation, misunderstandings, decreased working capacity, disturbed interpersonal relationships, and stress reactions. Some of these effects may lead to increased accidents, disruption of communication in the classroom, and impaired academic performance as proposed in a study by Stansfeld and Matheson (2003) and Berglund and Lindvall (1995). Particularly vulnerable groups include children, the elderly, and those not familiar with the spoken language (Berglund and Lindvall, 1995).

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Sleep Disturbances

Uninterrupted sleep is known to be a prerequisite for good physiologic and mental functioning in healthy individuals as mentioned by Hobson (1989). Environmental noise is one of the major causes of disturbed sleep as described by Berglund and Lindvall (1995) and Stansfeld and Matheson (2003). When sleep disruption becomes chronic, the results are mood changes, decrements in performance, and other long-term effects on health and well-being as presented in a conference by Suter (1991). Much recent research has focused on noise from aircraft, roadways, and trains. It is known, for example, that continuous noise in excess of 30 dB disturbs sleep. For intermittent noise, the probability of being awakened increases with the number of noise events per night as proposed by Berglund and Lindvall (1995). The primary sleep disturbances are difficulty falling asleep, frequent awakenings, waking too early, and alterations in sleep stages and depth, especially a reduction in REM sleep. Apart from various effects on sleep itself, noise during sleep causes increased blood pressure, increased heart rate, increased pulse amplitude, vasoconstriction, changes in respiration, cardiac arrhythmias, and increased body movement as presented in a research on sleep by Hobson (1989). For each of these, the threshold and response relationships may be different. Some of these effects (waking, for example) diminish with repeated exposure; others, particularly cardiovascular responses, do not, this was stimulated by Ohrstrom and Bjorkman (1998). Secondary effects (so-called aftereffects) measured the following day include fatigue, depressed mood and well-being, and decreased performance as described by Carter (1996). Decreased alertness leading to accidents, injuries, and death has also been attributed to lack of sleep and disrupted circadian rhythms as proposed by Coren (1996). Long-term psychosocial effects have been related to nocturnal noise. Noise annoyance during the night increases total noise annoyance for the following 24 h, particularly sensitive groups including the elderly, shift workers, persons vulnerable to physical or mental disorders, and those with sleep disorders as described by Berglund and Lindvall (1995). Other factors that influence the problem of night-time noise include its occurrence in residential areas with low background noise levels and combinations of noise and vibration such as produced by trains or heavy trucks. Low frequency sound is more disturbing, even at very low sound pressure levels; these low frequency components appear to have a significant detrimental effect on health as proposed in a research by Leventhal (2004).

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Cardiovascular Disturbances

A growing body of evidence confirms that noise pollution has both temporary and permanent effects on humans (and other mammals) by way of the endocrine and autonomic nervous systems. It has been postulated that noise acts as a nonspecific biologic stressor eliciting reactions that prepare the body for a “tight or flight” response as mentioned by Berglund and Lindvall (1995) and Babisch (2005). For this reason, noise can trigger both endocrine and autonomic nervous system responses that affect the cardiovascular system and thus may be a risk factor for cardiovascular disease which was proposed by Lsing and Kruppa (2004) and also by Babisch (2005). These effects begin to be seen with long-term daily exposure to noise levels above 65 dB or with acute exposure to noise levels above 80–85 dB as proposed by Berglund and Lindvall (1995). Acute exposure to noise activates nervous and hormonal responses, leading to temporary increases in blood pressure, heart rate, and vasoconstriction. Studies of individuals exposed to occupational or environmental noise show that exposure of sufficient intensity and duration increases heart rate and peripheral resistance, increases blood pressure, increases blood viscosity and levels of blood lipids, causes shifts in electrolytes, and increases levels of epinephrine, norepinephrine and cortisol as presented by Suter (1991). Sudden unexpected noise evokes reflex responses as well. Cardiovascular disturbances are independent of sleep disturbances; noise that does not interfere with the sleep of subjects may still provoke autonomic responses and secretion of epinephrine, norepinephrine, and cortisol presented by Ohrstrom and Bjorkman (1998). Temporary noise exposure produces readily reversible physiologic changes. However, noise exposure of sufficient intensity, duration, and unpredictability provokes changes that may not be so readily reversible. Studies that have been done on the effects of environmental noise have shown an association between noise exposure and subsequent cardiovascular disease as described by Berglund and Lindvall (1995) and Babisch (2005). Even though the increased risk for noise-induced cardiovascular disease may be small, it assumes public health importance because both the number of people at risk and the noise to which they are exposed continue to increase which was proposed by Berglund and Lindvall (1995).

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Disturbances in mental health

Noise pollution is not believed to be a cause of mental illness, but it is assumed to accelerate and intensify the development of latent mental disorders. Noise pollution may cause or contribute to the following adverse effects: anxiety, stress, nervousness, nausea, headache, emotional instability, argumentativeness, and sexual impotence, changes in mood, increase in social conflict, neurosis, hysteria, and psychosis. Population studies have suggested associations between noise and mental health indicators, such as rating of well-being, symptom profiles, the use of psychoactive drugs and sleeping pills, and mental hospital admission rates. Children, the elderly, and those with underlying depression may be particularly vulnerable to these effects because they may lack adequate coping mechanisms as described by Berglund and Lindvall (1995). Children in noisy environments find noise annoying and report a diminished quality of life which was proposed by Stansfeld and Matheson (2003). Noise levels above 80 dB are associated with both an increase in aggressive behavior and a decrease in behavior helpful to others as described by Korte et al. (1980); Konenci (1975); Mathews and Cannon (1975). The news media regularly report violent behavior arising out of disputes over noise; in many cases these disputes ended in injury or death. The aforementioned effects of noise may help explain some of the dehumanization seen in the modern, congested, and noisy urban environment as presented by Suter (1991).

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Impaired task performance

The effects of noise pollution on cognitive task performance have been well studied. Noise pollution impairs task performance at school and at work, increases errors, and decreases motivation as proposed by Evans and Lepore (1993). Reading attention, problem solving, and memory are most strongly affected by noise. Two types of memory deficits have been identified under experimental conditions: recall of subject content and recall of incidental details. Both are adversely influenced by noise. Deficits in performance can lead to errors and accidents, both of which have health and economic consequences as indicated by Berglund and Lindvall (1995). Cognitive and language development and reading achievement are diminished in noisy homes, even though the children's school may be no noisier than average which was proposed by Bronzaft (2000). Cognitive development is impaired when homes or schools are near sources of noise such as highways and airports as proposed by Lee and Fleming (2007); Evans and Lepore (1993). Noise affects learning, reading, problem solving, motivation, school performance, and social and emotional development as described by Suter (1991); Stansfeld and Matheson (2003); Bronzaft (2006). These findings suggest that more attention needs to be paid to the effects of noise on the ability of children to learn and on the nature of the learning environment, both in school and at home. Moreover, there is concern that high and continuous environmental noise may contribute to feelings of helplessness in children as presented in a research by Evans and Lepore (1993); Bronzaft (2006). Noise produces negative after-effects on performance, particularly in children. It appears that the longer the exposure, the greater the effect. Children from noisy areas have been found to have heightened sympathetic arousal indicated by increased levels of stress-related hormones and elevated resting blood pressure which was proposed by Bronzaft (2006). These changes were larger in children with lower academic achievement. As a whole, these findings suggest that schools and daycare centers should be located in areas that are as noise-free as possible as presented by Berglund and Lindvall (1995).

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Negative social behavior and annoyance reactions

Annoyance is defined as a feeling of displeasure associated with any agent or condition believed by an individual to adversely affect him or her. Perhaps ii better description of this response would be aversion or distress. Noise has been used as a noxious stimulus in a variety of studies because.se as it produces the same kind of effects as other stressors as described by Babisch (2005). Annoyance increases significantly when noise is accompanied by vibration or by low frequency components as presented by Leventhal (2004). The term annoyance does not begin to cover the wide range of negative reactions associated with noise pollution; these include anger, disappointment, dissatisfaction, withdrawal, helplessness, depression, anxiety, distraction, agitation, or exhaustion. Lack of perceived control over noise intensifies these effects as proposed by Berglund and Lindvall (1995); Stansfeld and Matheson (2003). Social and behavioral effects of noise exposure are complex, subtle, and indirect. These effects include changes in everyday behavior (for instance, closing windows and doors to eliminate outside noise; avoiding the use of balconies, patios, and yards; and turning up the volume of radios and television sets); changes in social behavior (for instance, aggressiveness, unfriendliness, nonparticipation. or disengagement): and changes in social indicators (for instance, residential mobility, hospital admissions, drug consumption, and accident rates); and changes in mood (increased reports of depression) as presented by Berglund and Lindvall (1995).

3. Modelling traffic noise

Traffic noise modelling describes the process of theoretically estimating noise levels within a region of interest under a specific set of conditions. The specific set of conditions for which the noise is being estimated will be a fixed representations or ‘snapshot’ of the physical environment of interest. However, in practice, the physical environment will usually not be fixed, but will be characterized by constantly varying conditions. These variations in real world conditions will subsequently cause the actual sound field to vary in time and space. Thus, it is important to recognize that the output of any traffic noise model will only represent an estimate for a ‘snapshot’ of the range of actual traffic noise levels that could occur in time and space.

Recognizing that modelling is a means of estimating noise for a specific set of conditions, attention is now directed to defining what these conditions are. The key conditions that a noise model relates to are: (a) n approximations of the noise source or sources, for which the associated environmental noise levels are of interest, (b) an approximation of the physical environment through which noise will transmit from the noise source(s) to the location or region of interest. This includes the ground terrain, the built environment, and atmospheric conditions (e.g., wind, temperature, humidity). (c) An approximation of the way in which sound will travel from the input noise source(s) via the input physical environment, to the receiver location or region of interest.Thus, producing a traffic noise model involves defining a series of noise sources to be investigated; describing the acoustically significant features of the environment through the weight sound will propagate to the receiver, and then applying a calculation method that accounts for these descriptions to produce an estimated noise level at a location or region of interest.

3.1. Empirical review

This section presents in detail, a review of the recent works of past researchers on the subject matter of traffic noise assessment and modelling. A gap analysis indicating the need of carrying out a traffic noise study and modelling in Enugu Metropolis was also performed.

3.1.1. Review on traffic noise study

There has been so much research on the study of traffic noise both locally and abroad. Some of the ground-breaking researches made in this area of study are hereby outlined. According to Menkiti (1976) which highlighted that there were many deaf people in Nigeria caused by exposure to loud noise. The survey carried out by Menkiti (2001) on the factors that constitute road traffic noise in the Nigerian environment concluded that people were bothered more outside their homes and that the awareness of pedestrian danger as a factor is very low. Onuu and Menkiti, 1996 analyzed the spectra of road traffic noise for parts of south-eastern Nigeria and concluded that this type of noise dominates the low frequency range (500–800 Hz). Onuu (1999), observed that road traffic noise constitutes the largest proportion of environmental noise in urban areas. He therefore observed that any meaningful noise abatement program must first and foremost be directed towards road traffic noise, which is a major subject of environmental acoustics. Hibbs and Larson (1996) presented a comprehensive technical report published by FHWA which covered the areas of tire pavement noise and safety performance. The report presents information on the pavement research status in California, Colorado, Iowa, Michigan, Minnesota, North Dakota, Virginia, and Wisconsin, as well as in foreign countries. In Colorado, an experimental project on I-70 east of Denver consisted of nine different texture sections. Friction, noise, texture, and profile tests were taken in 1994 before the pavement was open to traffic, and again in 1995. The study found that the longitudinal astro-turf dragging section had the lowest noise and the lowest friction number. The longitudinally lined section also produced a low noise level. The variable transverse tinning sections had the highest friction values, but they were also the noisiest sections. In Michigan, a 2 km section of pavement with an exposed aggregate surface treatment (constructed according to the German/Austrian design guidelines) was built in 1993 adjacent to a 2 km section of pavement with Michigan's standard concrete mix design with its standard transversely tined (25 mm spacing) texture. As it was mentioned in the FHWA report by Hibbs and Larson (1996), the noise measurements taken in 1993–1994 showed very little difference in the overall exterior noise level, as well as in 1/3 octave band frequencies between the two sections. The exposed aggregate pavement is included in this study and referred to as European texture. A significant finding was reported in the Minnesota study where the test sections consisted of various transversely lined pavements. Based on the noise results, it was concluded that the noise frequency could differ greatly from one texture to the next without a change in the overall noise levels. This finding highlights the inadequacy of the highway noise measurement and analysis procedures previously used for detecting annoying tonal characteristics. Nine test sections were constructed in 1993 to I-94 at Eagles Nest in North Dakota. The textures were tested for exterior and interior noise. Results indicated that the skewed tinning and variable spaced tinning produced the lowest exterior noise levels. Anomohanran et al. (2004), in his study of noise level in Agbor observed that the noise situation in Agbor is caused by big trucks, luxurious buses, and by commercial activities, and they called on the government to restrict the sighting of schools and hospitals along the major express way because of the high noise values observed from this location. A classic long-term study by Graff (1958) shows the development of cardiovascular diseases under high noise exposure. The average sound level was 95 dB (A) and the peak level reached 120 dB (A). According to Ochsner (2003), both the amount of noise and the length of time one is exposed to determine its ability to damage hearing. She said sounds that are louder than 85 dB are potentially hazardous. Hearing loss often occurs gradually, becoming worse over time. For this reason, many people do not become aware of their hearing loss until it is too late to avoid permanent damage. Most people in Nigeria with the inclusion of Enugu Metropolis.

3.1.2. Review on traffic noise modelling

Numerous traffic noise predictive models have been derived and currently used in different areas and countries globally. According to Quartieri et al. (2009), of these models are statistical model, England's CoRTN procedure, Germany's RLS 90 model, Italian CNR model, and the French's NMPB model.

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Basic statistical models

First attempts of making a traffic noise prediction can be collocated into the 1950/1960 decades; they mainly evaluate the percentile L50, defined as the sound level exceeded by the signal in 50% of the measurement period. These models refer principally to a fluid continuous flux, considering a common constant velocity with no distinction between vehicle typologies. One of the first models, developed in 1952, is the one reported in Handbook of Acoustic Noise Control Anon (1952). This model (Eq. (1)) states that the 50 percentile of traffic noise for a speed of 35–45 mph (about 55–75 Km/h) and distances greater than 20 feet (about 6 m). $L_{50} = 68 + 8.5 L o g (Q) - 20 L o g (d)$ where;

Q = traffic volume in vehicles per hour and

d = distance from the observation point to center of the traffic lane in feet; no specification is included about vehicles and road type.

In the following years, research findings by Nickson (1965) in “Can Community Reaction to Increased Traffic Noise be Forecast?” and Lamure (1965) presented a new model (Eq. (2)) in, a new parameter is included to relate the model to the experimental data. $L_{50} = C + 10 L o g (\frac{Q}{d})$ where C is a constant value that can be evaluated making an analysis of experimental data and L50 is the sound level in dBA.

Johnson et al. (1968) later presented a new TNM (Eq. (3)) taking into account the mean speed of vehicles in mph, v. $L_{50} = 3.5 + 10 L o g (\frac{Q v^{3}}{d})$

This model presents a good agreement with the experimental data for a percentage of heavy vehicles from 0% to 40%. It also includes some corrective factors for ground attenuation and gradient.Some years later, Galloway et al. (1969) improved this model taking into account the percentage of heavy vehicles P. Their expression at the L50 level in dBA is as presented in Eq. (4). $L_{50} = 20 + 10 L o g (\frac{Q v^{2}}{d}) + 0.4 P$

The models developed in the next years introduced the equivalent level Leq as a sound level indicator. One of the most used is the Burgess Model (Burgess, 1977) applied for the first time in Sydney in Australia. Using the same notation of the previous expression, the sound level is given by Eq. (5). $L_{50} = 55.5 + 10.2 L o g (Q) + 0.3 P - 19.3 L o g (d)$

Another most used calculation formula is called “Griffiths and Langdon Method” (Griffiths and Langdon, 1968). In particular, they propose the evaluation of an equivalent levels starting from the percentile level as shown in Eq. (6). $L_{e q} = L_{50} + 0.018 {(L_{10} - L_{90})}^{2}$ where the statistical percentile indicator has the expression; (Eqs. (7), (8), (9))) $L_{10} = 61 + 8.4 L o g (Q) + 0.15 P - 11.5 L o g (d)$ $L_{50} = 44.8 + 10.8 L o g (Q) + 0.12 P - 9.6 L o g (d)$ $L_{90} = 39.1 + 10.5 L o g (Q) + 0.06 P - 9.3 L o g (d)$ where Q, P, and d have the same meaning of the previous formula.

Several years later, Fagotti et al. (1995) improved the previous models introducing the motorcycle and bus flux, QM and QBUS. The formula they propose is shown in Eq. (10). $L_{e q} = 10 Log (Q_{L} + Q_{M} + 8 Q_{P} + 88 Q_{B U S}) + 33.5$

Another model was formulated by the French “Center Scientifique et Technique du Batiment” (C.S.T.B.) (19,991), which proposed a predictive formula (Eq. (11)) of the equivalent emission level, based on the average acoustic level (L50). $L_{e q} = 0.65 L_{50} + 28.8$

The value of L50 is calculated taking into account only the equivalent vehicular flow (Qeq) in accordance with Eq. (12). $L_{50} = 11.9 L o g Q_{e q} + 31.4$ for urban roads and highways with vehicular flow lower than 1000 vehicles/hour; $L_{50} = 15.5 L o g Q_{e q} - 10 Log L + 36$

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England standard: CoRTN procedure

The CoRTN procedure (Calculation of Road Traffic Noise) has been developed by the Transport and Road Research Laboratory and the Department of Transport of the United Kingdom (Anon, 1975) and has been modified in 1988 (DOT, 1988). It estimates the basic noise level L10 both with 1 h and 18 h reference time. This level is obtained at a reference distance of 10 m from the nearest carriageway edge of a highway. The parameters involved in this model are: traffic flow and composition, mean speed, gradient of the road, and type of road surface. The basic hypothesis of the model is a moderate wind velocity and a dried road surface.

The CoRTN procedure is divided in five steps:

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Divide the road scheme into one or more segments, such that the variation of noise level within the segment is less than 2 dBA;
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Calculate the basic noise level 10 m away from the near-side carriageway edge for each segment. It depends on the velocity, traffic flow, and composition. The traffic is considered as a linear source positioned at 0.5 m from the road surface and at 3.5 m from the carriage edge;
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Evaluate the noise level for each segment, taking into account the attenuation due to the distance and screening of the source line;
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Adjust the noise level taking into account: the reflection due to buildings and facades on the other side of the road and the reflective screen behind the reflection point and size of the source segment (view angle);
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Join the contributions from all segments to give the predicted noise level at the reception point for the whole road scheme.

Operatively, the basic hourly noise level is predicted at a distance of 10 m from the nearest carriageway, according to Eqs. (14), (15)). $L_{10} (1 h) = 42.2 + 10 L o g (q)$ $L_{10} (18 h) = 29.1 + 10 L o g (Q)$ where; q and Q are the hourly traffic flow (vehicles/hour) and 18-h flow (vehicles/hour), respectively. Here it is assumed that the basic velocity is v = 75 km/h, the percentage of heavy vehicles is P = 0, and the road's gradient is G = 0%. It is also assumed that the source line is 3.5 m from the edge of the road with carriageways separated by less than 5.0 m.

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German standard: RLS 90 model

In the Guideline for Noise Protection on Streets (RLS, 1990), the RLS90 traffic noise model has been defined as an improvement of the oldest standard RLS81 (RLS, 1981). RLS90 is an effective calculation model, able to determine the noise rating level of road traffic and, at the current day, is the most relevant calculation method used in Germany as presented by Quartieri et al. (2009). The model requires an input of data regarding the average hourly traffic flow, separated into motorcycles, heavy and light vehicles, the average speed for each group, the dimension, geometry, and type of the road and of any natural and artificial obstacles. This model takes also into account the main features, which influence the propagation of noise, such as obstacles, vegetation, air absorption, reflections, and diffraction. In particular, it makes it possible to verify the noise reduction produced by barriers and takes into account also the reflections produced by the opposite screens. In addiction, this is one of the few models present in the literature that is able to evaluate the sound emission of a parking lot. The starting point of the calculation is an average level LmE measurable at a distance of 25 m from the center of the road lane. This LmE(25) is a function of the number of vehicles per hour, Q and of the percentage of heavy trucks P (weight >2.8 tons), under idealized conditions (i.e. a speed of 100 km/h, a road gradient below 5% and a special road surface). Analytically, LmE(25) is given by Eq. (16). $L_{m E}^{(25)} = 37.3 + 10 L o g [Q (1 + 0.082 P)]$

The next step is to quantify the various deviations from these idealized conditions by means of corrections for the “real speed”, the actual road gradient or the actual surface, etc. In particular, correction depends upon whether a day (6:00–22:00 h) or night (22:00–6:00 h) is considered. Therefore for each lane, the mean level in dBA Lm is calculated as given by Eq. (17). $L_{m} = L_{m E}^{(25)} + R_{S L} + R_{R S} + R_{R F} + R_{E} + R_{D A} + R_{G A} + R_{T B}$ where;

$R_{S L}, R_{R S}, R_{R F}, R_{E}, R_{D A}, R_{G A} a n d R_{T B}$ , are correction factors to be determined.

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Italian C.N.R. model

Nowadays, the Italian legislation does not suggest any traffic noise model of reference, but the most used by technicians is the one developed by the Italian “Consiglio Nazionale delle Ricerche” (CNR) (Canelli and Gluck, 1983) and then improved by Cocchi et al. (1991). This model represents a modification of the German standard RLS 90, adapted to the Italian framework; a relation between the traffic parameters and the mean sound energy level is supposed and the traffic flow is modeled as a linear source placed in the center of the road. Therefore the equivalent sound level in dBA is given by Eq. (18). $L_{A e q} = α + 10 L o g (Q_{L} + β Q_{P}) - 10 L o g (\frac{d}{d_{0}}) + Δ L_{V} + Δ L_{F} + Δ L_{B} + Δ L_{S} + Δ L_{G} + Δ L_{V B}$ where; QL and QP are the traffic flow in 1 h, related to light and heavy vehicles, respectively, d0 is a reference distance of 25 m and d the distance between the lane center and the observation point on the road's edge. $Δ$ Ls' are correction factors that can be determined. From the foregoing paragraphs of this section, the importance of traffic noise study and modelling cannot be overstated. From the detailed review above, it can be noted that a comprehensive traffic noise study is lacking in Port Harcourt and indeed the entire South-South region of Nigeria. Hence, this study seeks to address this situation by considering two pavement (flexible and rigid) types in Port Harcourt Metropolis. A traffic noise study is not complete without traffic noise model development. As noticed from the review of past studies, there are many traffic noise models available for different countries with these models considering different parameters as considered by the researchers. This study seeks to develop traffic noise models in two different formats. The first format would consider traffic speed, traffic volume, and distance of the noise measuring instrument from the center of the roadway as independent variables, while the second format would only consider the time of noise measurement. Multiple linear regression analysis was adopted in format one modelling, while for the format two modelling, in addition to regression analysis, time series analysis was adopted in the modelling process. These developed models were also checked against other probability distribution models such as normal, lognormal, and uniform probability distribution functions.

4. Planned development of noise models forthe Port Harcourt case

4.1. Design of study

This research will be directed towards the development of traffic noise intensity models for roads within the Port Harcourt Metropolis. The two major and commonly used types of pavements in Nigeria, flexible and rigid pavements, will be considered in this study. For the essence of this study, five (5) routes will be selected and categorized with the help of Google maps. The five (5) routes will consist of three (3) major and very busy flexible routes with similar characteristics, which will simulate the behavior of other major routes in Port Harcourt. The other two routes (one rigid and one flexible), which are less busy routes, will be selected to simulate and compare the traffic noise generated from both types of pavements. Field measurements of traffic noise intensity will be done using the sound level meter at a height of one (1) meter above the ground level. The spot speed of vehicles will be collected using the spot speed stop watch manual method. Traffic noise intensity models will be developed using two model formats; the speed, traffic volume, and distance from the center of roadway format and the time format. Model format one will employ multiple linear regression while model format two will use simple regression and time series analysis in model development. Probability distribution models (normal, log-normal, and uniform distributions) will also be employed to study the most likely distribution pattern of the observed traffic noise intensity. A comparative study will be carried out to assess the difference in traffic noise generation between flexible and rigid pavements. All models developed in this study will be validated using the R2 statistics for the essence of ranking.

4.2. Description and categorization of the study area

4.2.1. Description of the study area

Port Harcourt popularly referred to as Pitakwa or the Garden City, is the capital and largest city of Rivers State, Nigeria. It lies along the Bonny River and is located in the Niger Delta. As of 2016, the Port Harcourt urban area has an estimated population of 1,865,000 inhabitants, up from 1,382,592 as of 2006 (Arizona-Ogwu and Chinedu, 2011).As of 2009, its total population was estimated at 2,000,000 making it one of the largest metropolitan areas in Nigeria. However, that number has greatly increased according to recent studies. The city is located on latitude 4.8156° N and longitude 7.0498° E with an average altitude of about 12 m above mean sea level. From an area of 15.54 km2 in 1914, Port Harcourt grew uncontrolled to an area of 360 km2 in the 1980s (Izeogu, 1989).Port Harcourt is highly congested as it is the only major city of the Rivers State. Many significant changes have been experienced in terms of urbanization, industrialization, expansion of road network, and infrastructure. The city has been subjected to persistent road traffic and commercial activities due to the increase in development and expansion of the economy. Fig. 2 presents the map of Port Harcourt according to the traffic flow situation in the city.