Home Email this page Print this page Bookmark this page Decrease font size Default font size Increase font size
Noise & Health  
 CURRENT ISSUE    PAST ISSUES    AHEAD OF PRINT    SEARCH   GET E-ALERTS    
 
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Email Alert *
Add to My List *
* Registration required (free)  
 


 
   Abstract
  Introduction
  Material and methods
  Results
  Conclusion
   References
   Article Figures
   Article Tables
 

 Article Access Statistics
    Viewed592    
    Printed24    
    Emailed0    
    PDF Downloaded0    
    Comments [Add]    

Recommend this journal

 


 
  Table of Contents    
ORIGINAL ARTICLE  
Year : 2019  |  Volume : 21  |  Issue : 101  |  Page : 142-154
Analysis of urban road traffic noise exposure of residential buildings in hong kong over the past decade

1 School of Civil Engineering, Central South University, Changsha; Department of Building Services Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
2 Department of Building Services Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
3 School of Civil Engineering, Central South University, Changsha, China

Click here for correspondence address and email
Date of Submission25-Jun-2018
Date of Decision11-Dec-2019
Date of Acceptance17-Feb-2020
Date of Web Publication25-Jul-2020
 
  Abstract 


Introduction: With the development of transportation system and the economy, the rapidly increasing number of automobiles brings the associated problem of road traffic noise, especially in metropolitan and densely populated high-rise cities like Hong Kong. In Hong Kong, approximately one million people are affected by severe road traffic noise. Excessive noise exposure is hazardous to the health and wellbeing of people and therefore has drawn progressively more attention in Hong Kong. The Calculation of Road Traffic Noise (CRTN) has been adopted as the sole tool to evaluate road traffic noise in the form of descriptor LA10. The accuracy and suitability of the CRTN method for predicting road traffic noise in Hong Kong were evaluated in this study by comparing the prediction results and measured traffic noise levels. The results show that the CRTN method was able to provide adequate predictions with correlation coefficients of 0.8032 and 0.7626 between the predicted and measured LA10 for 2007 and 2017 respectively. The predicted traffic noise levels on different floors of seven selected residential buildings in 2017 were compared with those predictions for the same buildings in 2007. The worsening traffic noise exposure in these residential buildings was analysed and some suggestions and counter-measures to alleviate the traffic noise problems are put forward. Since the situation of Hong Kong is an example of what may happen in other cities, the present longitudinal study of the road traffic noise in Hong Kong hopes to contribute to a better urban acoustic environment worldwide. Context: Excessive noise exposure is hazardous to the health and wellbeing of people and therefore has drawn progressively more attention in Hong Kong. The urban road traffic noise exposure of residential buildings in Hong Kong over the past decade has been analysed. Aims: This study aims to assess the road traffic noise exposure of residential buildings over the past decade. Settings and Design: Measurements of traffic noise levels at some selected residential buildings were first conducted in 2007, and then repeated at the same buildings in 2017. Material and Methods: The CRTN was adopted to predict the traffic noise levels based on the recorded traffic flow data. Results: The exposure of these buildings to road traffic noise is higher in 2017 than in 2007. The study illustrates that the deterioration of the urban acoustic environment may not be caused by an increased total number of vehicles, but that heavy vehicles are dominantly responsible for the increased traffic noise levels. Restriction of vehicle velocity for urban street canyons is useless for road traffic noise control. Conclusions: This study shows the deterioration of traffic noise levels is mainly due to the increased heavy vehicles instead of the increased total number of vehicles. The alleviation of traffic noise levels by velocity restriction may not be obvious for urban street canyons and may only work with a certain velocity range.

Keywords: Calculation of road traffic noise, residential buildings, road traffic noise, urban

How to cite this article:
Cai C, Mak CM, He X. Analysis of urban road traffic noise exposure of residential buildings in hong kong over the past decade. Noise Health 2019;21:142-54

How to cite this URL:
Cai C, Mak CM, He X. Analysis of urban road traffic noise exposure of residential buildings in hong kong over the past decade. Noise Health [serial online] 2019 [cited 2020 Sep 28];21:142-54. Available from: http://www.noiseandhealth.org/text.asp?2019/21/101/142/290732



  Introduction Top


As a result of rapid urbanization, the urban population proportion was estimated to be about 54% in 2014 and is expected to increase further in the next three decades. According to the United Nations Population Division, the world’s population is expected to increase from 7.4 billion in 2015 to 9.6 billion in 2050, while the urban population is expected to increase from 3.9 billion to 6.3 billion in the same period.[1] It indicates that not only will almost all of the expected population growth be in urban areas but also that some of the rural population will be attracted to urban areas. The increasing demands on all kinds of resources in cities will add pressure to the urban environment. Enhancing the habitability of cities will be a great challenge for authorities.[2],[3],[4],[5],[6],[7],[8] Urban road traffic noise is considered as one of the most severe issues among various environmental pollution problems in cities, especially for metropolitan and densely populated cities.[9],[10],[11],[12],[13],[14],[15] It affects a large number of inhabitants in physiological and psychological aspects and its effects can be cumulative.[16],[17],[18] Urban road traffic noise has been found to be related to people’s productivity, performance, and satisfaction.[19],[20],[21],[22] Some investigations illustrate that road traffic noise is harmful to human health and may cause some chronic diseases, for instance hypertension and ischemic heart disease.[17],[23] The World Health Organization holds urban traffic noise responsible for adding a burden to the health of one million lives per annum in the European Union. More than 30% of the world population is suffering from exposure to excessive road traffic noise and the deteriorated situation is becoming increasingly apparent.[24]

Being a major metropolitan city and one of the most densely populated high-rise cities in the world, Hong Kong has been confronting the immense road traffic noise problem for the past few decades. The road traffic noise problem in Hong Kong is due to a combination of factors including the scarcity of habitable land, a lack of concerns for the environment in previous planning, an ever-increasing population and the associated housing demand.[25],[26],[27],[28] The concentrated and large-scale road traffic network benefits the inhabitants with easy transportation and guaranteed logistics and support for the growth of the economy. However, limited land source constrains residential buildings to adjacent roads, bridges, and flyovers. As a consequence, it results in a serious noise pollution problem for residents. The Hong Kong Planning Standards and Guidelines highlight a road traffic noise benchmark of 70 dB(A), measured in the form of descriptor LA10 (1 hour), according to the Calculation of Road Traffic Noise (CRTN) method.[29] Approximately one million people in Hong Kong are exposed to excessive road traffic noise, labelling Hong Kong as one of the noisiest cities in the world.[30] With the aim of fulfilling huge housing demand under the adversely limited land resources, the essential urban re-development of Hong Kong will lead to a more compact urban form with an increasing number of high-rise buildings, which will aggravate the urban acoustic environment and complicate counter-measures of road traffic noise abatement.[31],[32],[33]

The Environmental Protection Department (EPD) of the Hong Kong Government, established in 1986, has been working hard to resolve the urban road traffic noise problem. As prevention is always better than cure, the effective planning and evaluation of roads and new residential developments in the early stages of design is the best method of protecting residents from excessive traffic noise in the future. Therefore, the EPD announced a guideline note, providing general criteria for preparation of a Road Traffic Noise Impact Assessment (RTNIA) under the Environment Assessment Ordinance (EIAO) in 2005.[29] The guidance note highlights some possible traffic noise mitigation designs and measures and makes reference to the CRTN prediction method, a traffic noise prediction model that has been adopted by the EPD for years. The CRTN method was initially developed by Delany et al.[34] for predicting traffic noise in the United Kingdom and then officially adopted by the Welsh Department of Transport in 1988 as well as authorities of Australia, New Zealand, and Hong Kong.[35],[36]. It is the sole tool recommended by the Hong Kong government for the assessment of road traffic noise. Since there are no simple answers to the road traffic noise problem in Hong Kong, the EPD published a comprehensive plan to track road traffic noise in 2006.[30] Hong Kong has made tremendous efforts to improve the urban acoustic environment. Various noise measures have been implemented in Hong Kong in the past decades, including noise barriers, low noise material resurfacing, noise shielding walls, environmental impact assessment, pedestrianisation schemes, legislative control of individual vehicles and internal layout design. However, road traffic noise issues have still been raised by the Legislative Council, the district councils, and the media due to the public’s impatience of high traffic noise levels in Hong Kong.

The longitudinal studies of the road traffic noise over time may be the best means to determine temporal change in noise exposure. However, lack of resources and commonly accepted methodology results in very few longitudinal studies of the road traffic noise. This study aims to assess the road traffic noise exposure of residential buildings over the past decade. Measurements of traffic noise levels at some selected residential buildings were first conducted in 2007, and then repeated at the same buildings in 2017. The traffic flow data were recorded synchronously during each measurement. The accuracy and suitability of the CRTN method for predicting road traffic noise in Hong Kong were evaluated in the present study by comparing the prediction results and measured traffic noise levels. The predicted traffic noise levels on different floors of these residential buildings for 2017 were compared with those predictions (at the same buildings) for 2007. The measured traffic noise levels and the traffic flow data recorded for predictions may vary from day to day. Thus, the changes in road traffic noise exposure of residential buildings in Hong Kong over the past decade presented in this paper may not be accurate due to limited resources. However, the present investigation allows a glimpse of the temporal changes in urban road traffic noise over time. It is significant for prevention and mitigation of urban road traffic noise in Hong Kong as well as other cities. Since the situation of Hong Kong is an example of what may happen in other cities, the present study hopes to contribute to policies of urban road traffic noise control for better urban acoustic environment worldwide.


  Material and methods Top


Situation of Hong Kong over the past decade

With over 7.4 million people in a territory of 1108 square km, Hong Kong is the fourth-most densely populated region in the world as of the year 2017. The topography of Hong Kong is hilly to mountainous with steep slopes. Only about 24% of the land area (266 square km) has been developed, and 7% of the total land area is for residential purposes. About 40% of the land area is reserved as country parks and nature reserves.[37] It is unsurprising that Hong Kong is the world’s most dense and vertical city with the largest number of skyscrapers and 36 of the world’s tallest residential buildings. A large-scale traffic network has been developed in Hong Kong and is among the most heavily used in the world. Although the large-scale road traffic network benefits the inhabitants and economy, insufficient land constrains residential buildings to adjacent road traffic and restricts the progress of the road traffic network. The development of traffic network has been sluggish in the past ten years. According to the Highways Department of the Hong Kong government, in 2007 there were 1984 km of road, of which 441 km were on Hong Kong Island, 452 km in Kowloon, and 1091 km in the New Territories. By December 2017, the total length of roads only increased to 2107 km (442 km on Hong Kong Island, 472 km in Kowloon and 1,193 km in the New Territories). [38] Only 123 km of new roads have been built in the past decade (growth rate of 6.2%), and most of the newly-built roads are in the New Territories (generally considered as suburb area), as illustrated in [Figure 1].
Figure 1: Comparisons of the total length of roads in Hong Kong over the past decade.

Click here to view


Nevertheless, the number of licensed vehicles in Hong Kong has increased rapidly from about 565,061 in 2007 to about 766,200 in December 2017.[39] Licensed vehicles are categorized as ‘private cars’ and ‘others’. The category ‘others’ includes all types of buses, taxis, goods vehicles, special purpose vehicles and government vehicles. It can be observed from [Figure 2] that the increased number of private cars account for nearly all of the total increased number of vehicles. Over this time, the total number of vehicles increased by 36.6%, and the number of private cars increased by 48.5%. The proportion of vehicles classed as private cars increased from 65.9% in 2007 to 72.1% in 2017. Considering the slow progress in the development of the road traffic network of Hong Kong over the past decade, the rapidly increasing number of vehicles will aggravate the immense pre-existing road traffic noise problem. Therefore, the analysis of temporal changes in road traffic noise of Hong Kong over the past decade is necessary and will be conducive to possible traffic noise mitigation designs and measures.
Figure 2: The growth characteristics of vehicle numbers in Hong Kong over the past decade.

Click here to view


CRTN model

A road traffic noise prediction model that assesses the likely effects of traffic noise on people and the environment is required to aid in the early stages of design of roads and dwellings. A road traffic noise prediction model is also significant in the assessment of existing or envisaged changes to the traffic noise conditions. Six methods of road traffic noise prediction are commonly used around the world, including the FHWA method in the US, the CRTN method in UK, the RLS 90 method in Germany, the STL-86 method in Switzerland, the ASJ method in Japan and MITHRA method in France.[36] Many alternative methods of road traffic noise prediction have been developed and adopted in different countries and areas, for example, in the Nordic countries, Mainland China, Taiwan, Italy and Iran.[40],[41],[42],[43],[44] The mathematically-based CRTN method is among the first systematic schemes that consider certain physical conditions and the surrounding environmental characteristics of the traffic roadway when estimating road traffic noise. The CRTN method has been the sole tool for the assessment of road traffic noise by local authorities in Hong Kong. Therefore, this study will employ the CRTN method to evaluate and characterise the road traffic noise in Hong Kong over the past decade in the form of descriptor LA10. The descriptor LA10 in the CRTN model is one type of the A-weighted percentile levels LN, which are statistical parameters defined as the noise level exceeded for N% of the measurement time.

The CRTN method assumes the traffic noise source as a continuous line source with a constant speed (taking no account of vehicle acceleration) at a height of 0.5 m above the carriageway and 3.5 m from the nearside carriageway edge. At the reception point, with a reference distance of 10 m from the kerb (which is defined as the edge of the traffic lanes excluding the hard shoulders, hard strips and bus lay-bys), the CRTN method suggests the expression for the basic noise level LA10 as:



where Q is the total number of vehicles per hour, V represents the traffic speed, P is the percentage of heavy vehicles (weighting over 1525 kg) and G is the road gradient (expressed as a percentage).

The basic noise level LA10 considers the traffic flow, traffic speed, traffic composition, and road gradient. Additional corrections for the actual assessment point and other topographic data are taken into account in the CRTN model for the estimation of LA10. For the calculation of the predicted LA10 at different floor levels of a building, as illustrated in [Figure 3], the distance correction of the CRTN method is based on the shortest distance d’ between the reception point and the effective source point given by (where dis the shortest horizontal distance and is assumed to be at least 4 m, h is the relative height of the reception point from the source line) and is expressed as:



Considering the compact form of the urban area, a correction for reflection from the opposite facade facing the reception point is a significant factor and is calculated by:



where is the sum of the angles subtended by all reflection facades on the opposite side of the road, and is the total angle of the view at the reception point. More detailed calculation of additional corrections can be found in Ref. 36 and will not be presented here for simplification. Therefore, the predicted LA10 noise level can be obtained by combining the basic noise level given in Eq. (1) and corrections given in the CRTN method according to actual situation.
Figure 3: Illustration of the positions of the reception point and effective source point.

Click here to view



  Results Top


Predicted and measured results of traffic noise levels at different floor levels of selected residential buildings in Hong Kong

The accuracy and suitability of the CRTN method for predicting road traffic noise in Hong Kong has been evaluated by comparing the predicted values with measured traffic noise levels. Measurements of LA10 at different floor levels of some selected residential buildings in Hong Kong were first conducted in 2007, and then repeated at the same buildings in 2017. The measured traffic noise level LA10 and the traffic flow data for the predictions of LA10 using CRTN method were obtained in a one-hour period during the morning peak traffic. However, the measurements of LA10 and recorded traffic flow data may vary from day to day, especially between the working days and holidays. For more common cases of road traffic noise, the working days were chosen to conduct measurements in this paper. The approximate locations of these selected buildings are shown in [Figure 4]. These are typical residential areas with concentrated dwellings and convenient road traffic conditions, and no new dwellings and roads are developed in these areas over the past decade. The residential buildings selected for prediction and measurement were chosen in order to avoid complicated traffic conditions such as multiple connected streets or roads in urban areas where there may have been various non-traffic noise sources and traffic control signals. Other possible noise sources such as playgrounds, construction areas, railways, and noisy markets are distant from the selected residential buildings to ensure the accuracy of the traffic noise measurement. For traffic noise measurements of different floor levels of buildings, the measurement point was set to be a 1 meter from the exterior building facades. The sound analyser type B&K 2260 was used to obtain the traffic noise descriptor LA10. A digital video camera was used to record the traffic flow in order to count the total traffic and the number of heavy and light vehicles synchronously during each measurement. [Table 1] shows the predicted and measured noise levels LA10 at different buildings in the years 2007 and 2017, as well as the traffic count for each measurement.
Figure 4: Map and photographs of the locations of selected residential buildings.

Click here to view
Table 1: Traffic count during LA10 measurements and predictions based on the traffic conditions

Click here to view


Comparisons of predicted traffic noise levels with measurements are illustrated in [Figure 5]. The predictions using the CRTN model correlate well with the LA10 measured in both 2007 and 2017 (with a correlation coefficient (R2) of 0.8032 and 0.7626 for 2007 and 2017, respectively). It should be noted that occasional strong winds and occasional noise from vehicle brakes and pedestrians were unavoidable throughout the measurements. Therefore, this indicates that traffic noise levels predicted by the CRTN method correlate closely with the measured levels despite the change of the traffic conditions over the years. The accuracy and suitability of the CRTN method presented here also generally agree with the previous study of Mak et al.[28],[45]
Figure 5: Predicted LA10 using the CRTN method against the measurements for each of the years 2007 and 2017.

Click here to view


Prediction of traffic noise LA10 at different floor levels of selected residential buildings in Hong Kong

Being a major metropolitan city and one of the most densely populated high-rise cities in the world, Hong Kong is also labelled as one of the noisiest cities in the world. The road traffic is considered as the dominant source for the noisy urban acoustic environment. Due to limited land source and rapid development in the past, the residential buildings in Hong Kong are constrained to be within short distance of existing roads. The lack of concern for the environment in previous planning and increased demand for traffic in the past decade have resulted in high levels of exposure to traffic noise in residential buildings with no immediate noise mitigation solutions. Hong Kong has made tremendous efforts to alleviate the traffic noise problem. This study aims to evaluate the urban traffic noise in Hong Kong over the past decade. All seven selected residential buildings directly face a road without any complicated traffic conditions or other possible noise sources. The approximate locations of these buildings are illustrated in [Figure 4] and measured traffic data used for traffic noise level prediction is given in [Table 1]. Comparisons of predicted traffic noise levels at different floors of these residential buildings in 2007 and 2017 are shown in [Figure 6].
Figure 6: Predicted LA10 of different floors of buildings in 2007 and 2017 (solid lines represent predictions of year 2017, and dashed lines represent predictions of year 2007).

Click here to view


It can be observed from [Figure 6] that the predicted LA10 values exceeded the benchmark of 70 dB(A) in both 2007 and 2017 at nearly all floor levels of all the residential buildings except site 2. For the investigated residential buildings, the traffic noise levels of 2017 were greater than those of 2007. This indicates that it is not easy to achieve the benchmark of 70 dB(A) at all floor levels of existing buildings and that the road traffic noise problems are getting more serious, despite the great efforts of the government. As illustrated in [Figure 6], it can be generally considered that there has been no increase of traffic noise levels at site 3. A slight increase in traffic noise levels can be found at sites 1, 2, 5 and 7. However, rapid increases of 4 dB(A) and 3 dB(A) in traffic noise levels can be observed at site 4 and site 6 respectively. Considering the traffic noise levels at site 4 and site 6 were already exceeding the benchmark in 2007, the traffic noise levels of these two sites in 2017 were terrified. The different changes in traffic noise levels of these residential buildings motivate us to seek the reasons and possible solutions.

Analysis of the change of the traffic noise levels

The comparisons of traffic conditions between the years 2007 and 2017 at sites 4 and 6 are illustrated in [Table 2]. For site 6, the total number of vehicles increased by 20% between 2007 and 2017. The mean velocity of vehicles decreased slightly from 37.8 km/h to 34.6 km/h. Generally, the flow of the traffic is unimpeded despite of the increased number of vehicles. However, the increased number of vehicles has caused the road traffic noise exposure of the building to deteriorate. The average traffic noise levels at different floors within the building on site 6 (Prince Edward Road) increased by over 3 dB(A). The total number of vehicles classed as heavy vehicles increased from 705 to 1681, and the percentage of heavy vehicles increased from 23.5% to 34.6%. [Figure 7] compares the predicted LA10 of year 2007 and 2017 with predicted values based on two different hypotheses. Hypothesis 1 assumes that the number of heavy vehicles and the mean velocity in 2017 remains the same as it was in 2007 without altering the total number of vehicles in 2017. Hypothesis 2 changes the mean velocity of the Hypothesis 1 into the level in 2017. It can be observed from [Figure 7] that the heavy vehicles account for almost all of the increased traffic noise levels, and the mean velocity nearly has no effects on the traffic noise levels. Although the total number of vehicles increased by 20%, there would have been almost no increase in traffic noise levels with restricted heavy vehicles.
Table 2: Comparisons of traffic conditions across different years for sites 4 and 6

Click here to view
Figure 7: Comparisons of predicted LA10 at site 6 in 2007 and 2017 and predictions based on hypotheses.

Click here to view


The effects of the number of heavy vehicles on the traffic noise levels are more obvious at site 4. As shown in [Table 2], the total number of vehicles and their mean velocity recorded at site 4 have remained almost the same over the past decade. The increased heavy vehicles result in the additional 4 dB(A) of traffic noise at all floor levels. This indicates that the deterioration of the urban acoustic environment may not be caused by an increased total number of vehicles, but that heavy vehicles are dominantly responsible for the excessive traffic noise levels. In order to investigate the effects of the mean velocity on the traffic noise levels, two hypotheses consider different mean velocities based on the traffic conditions at site 4 in 2017. [Figure 8] indicates that the mean velocity has little effect on the traffic noise levels at site 4.
Figure 8: Comparisons of predicted LA10 at site 4 in 2007 and 2017 and predictions based on hypotheses.

Click here to view


The traffic noise levels at site 3 remains the same in 2017 as it was in 2007. Although the percentage of vehicles classed as heavy vehicles increased from 38.2% to 41.5%, as shown in [Table 3], the number of heavy vehicles decreased as did the total number of vehicles. However, the mean vehicle velocity increased from 48.1 km/h to 58.9 km/h. The beneficial effects of a decreased number of heavy vehicles and total vehicles have been counteracted by this increase in mean velocity. [Figure 9] compares the predicted LA10 values for 2007 and 2017 with predicted values based on two different hypotheses. Hypothesis 1 and Hypothesis 2 assume a change of mean velocity to 50 km/h and 45 km/h respectively by 2017. Unlike the situation of sites 4 and 6, a distinct decrease in traffic noise levels can be observed between predicted LA10 for 2017 and both of these two hypotheses. However, the difference in traffic noise levels between Hypothesis 1 and Hypothesis 2 are not obvious. Therefore, it indicates that a reasonable restriction of vehicle velocity can alleviate the noisy of urban acoustic environment without effects on the smooth flow of traffic.
Table 3: Comparisons of traffic conditions across different years at site 3

Click here to view
Figure 9: Comparisons of the predicted LA10 of different floor levels located at site 3.

Click here to view


The effects of mean velocity on the traffic noise levels are different among sites 3, 4 and 6. Reduction of mean velocity contributes to the mitigation of traffic noise levels at site 3, however, it has little effects on the traffic noise levels at sites 4 and 6. [Figure 10] shows the plan view of selected buildings at sites 3,4 and 6 respectively. Site 4 and site 6 are located in typical urban street canyons flanked by high-rise buildings, while there is no reflection facade opposite site 3. It indicates that the urban street canyons are not sensitive to variation of the mean velocity. Restriction of mean velocity for urban street canyons is useless for road traffic noise control. On the contrary, it will obstruct traffic flow and may generate additional noise due to vehicle brakes and engine start.
Figure 10: Plan view of buildings at sites 3, 4 and 6 respectively (shadow zones represent areas without reflection facades).

Click here to view


The slight increase in traffic noise levels can be found at sites 1, 2, 5 and 7. For site 1, both the increased number of heavy vehicles and increased mean velocity account for the increase in traffic noise levels. The respective effects of increased number of heavy vehicles and increased mean velocity on the traffic noise levels at site 1 are demonstrated in [Figure 11]. The two hypotheses for site 1 are given in [Table 4]. The increased number of heavy vehicles has a far worsen impact on the road traffic noise than the increased mean velocity. Nevertheless, the combination of reduction in mean velocity and modest growth in heavy vehicle numbers makes a slight increase in traffic noise levels at sites 2 and 5. It should be noted that the number of heavy vehicles at site 7 rose sharply, as shown in [Table 4]. Fortunately, the increased traffic noise levels are not as severe as at sites 4 and 6 due to the large reduction of mean velocity. This may be the reason that the speed limit of the Island Eastern Corridor (site 7) is 70 km/h rather than 80 km/h as it is for other roads of the same route, especially considering that the Island Eastern Corridor is the only expressway on Hong Kong Island. However, as shown in [Figure 6], the traffic noise levels at all floors at site 7 exceeded 76 dB(A) in 2017. Effective measures should be taken to eliminate the immense traffic noise problem. Restriction of heavy vehicles is a proven and effective method, as illustrated above. Considering the significant role of the Island Eastern Corridor in Hong Kong Island, we propose a hypothesis in which the heavy vehicles increase by 50% at site 7 between 2007 and in 2017 (without changing of the total number of vehicles in 2017), then the total number of heavy vehicles would be 1057 in 2017 instead of 1923. With this hypothesis, the traffic noise levels of 2017 are almost the same as those in 2007, as illustrates in [Figure 12]. Although the traffic noise problem at site 7 is still severe according to the hypothesis, it indicates that the acoustic environment will not deteriorate with time through some appropriate measures. The traffic conditions at site 7 shows that an appropriate decrease in mean traffic velocity will not prevent the smooth flow of traffic and will contribute to the elimination of traffic noise levels.
Figure 11: Comparisons of the changes in traffic noise levels at site 1 due to increased number of heavy vehicles or the increased mean velocity.

Click here to view
Table 4: Comparisons of traffic conditions across different years and sites

Click here to view
Figure 12: Comparisons of the predicted LA10 of different floor levels located at site 7.

Click here to view


Suggestions

According to aforementioned cases in this study, the urban road traffic exposure of residential buildings in Hong Kong seems to deteriorate over the past decade. The deterioration of the urban acoustic environment may not be caused by an increased total number of vehicles. The increased number of heavy vehicles is dominantly responsible for increased traffic noise levels, especially for street canyons, such as at sites 4 and 6 of this study. Reasonable limitations of vehicle velocity can contribute to the elimination of traffic noise levels without effects on the traffic flow at some sites. To prevent worsening of road traffic noise and improve the urban acoustic environment, some suggestions are given:
  1. Despite the requirement of authorities to evaluate the effects of traffic noise for newly-built roads and residential buildings, noise may not be a primary factor in the early planning stage compared with engineering feasibility and cost of construction. The worsening of road traffic noise in the future is not taken into full consideration. Thus, mitigation measures such as roadside barriers or covers have to be taken in order to alleviate the excessive road traffic noise. However, lack of reservation places is problematic to retrofit existing roads with these measures. Although noise barrier is a kind of effective and economical method to reduce traffic noise, only about 3 kilometres of noise barriers have been built in Hong Kong over the past decade, including installations on newly-built roads. Therefore, noise mitigation measures for worsening traffic noise in the future should be emphasized in the planning stage.
  2. Traffic management schemes should be more restricted in residential areas so that heavy vehicles or vehicles that do not need to go into residential areas or urban street canyons would be banned from doing so during sensitive hours. The effects of mean velocity on the road traffic noise are related to the structure of street. Reasonable limitations of vehicle velocity should be explored where practicable on a case-by-case basis. An appropriate decrease in mean traffic velocity will not prevent the smooth flow of traffic and will contribute to the elimination of traffic noise levels.
  3. Vehicles with a weight greater than 1525 kg are qualified as heavy vehicles in the CRTN method. With growth of the economy and the development of the automobile industry, private vehicles increasingly tend to be heavy vehicles. However, heavy vehicles have adverse effects on a satisfactory acoustic environment that would protect people against excessive road traffic noise and provide a better quality of life to the public. The increased number of heavy vehicles rather than the increased number of total vehicles is dominantly responsible for increased traffic noise levels. Therefore, legislative control of private heavy vehicles is needed.



  Conclusion Top


This study aims to assess the road traffic noise exposure of residential buildings in Hong Kong over the past decade. The accuracy and suitability of the CRTN method are evaluated first by comparing prediction results and measured traffic noise levels. The results show that the CRTN method could provide adequate predictions with correlation coefficients (R2) of 0.8032 and 0.7626 between the predictions and measurements of LA10 for 2007 and 2017 respectively. It indicates that the CRTN method is still reliable although it was released about 30 years ago. Based on the traffic flow data recorded synchronously during each measurement, the predicted traffic noise levels on different floors of seven selected residential buildings in 2017 are compared with those predictions in 2007. The exposure of these building to road traffic noise is higher in 2017 than in 2007. The study illustrates that the deterioration of the urban acoustic environment may not be caused by an increased total number of vehicles, but that heavy vehicles are dominantly responsible for the increased traffic noise levels. However, heavy vehicle numbers have increased quickly over the past decade due to the growth of the economy and development of the automobile industry. A decreased vehicle velocity can contribute to the elimination of traffic noise levels. However, the alleviation of traffic noise levels by velocity restriction may not be obvious for urban street canyons and may only work with a certain velocity range. Meanwhile, velocity restriction may act against the smooth flow of traffic. Therefore, a reasonable limitation of vehicle velocity should be explored where practicable on a case-by-case basis. Since the situation of Hong Kong is an example of what may happen in other cities, the present longitudinal study of the road traffic noise in Hong Kong hopes to contribute to policies of urban road traffic noise control for better urban acoustic environment worldwide.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
The Unit ed Nations Department of Economic and Social Affairs. World Urbanization Prospects-The 2012 Revision. New York 2014.  Back to cited text no. 1
    
2.
Zhou W, Wang J, Qian Y, Pickett STA, Li W, Han L. The rapid but invisible changes in urban greenspace: a comparative study of nine Chinese cities. Sci Total Environ 2018;627:1572-84.  Back to cited text no. 2
    
3.
Hoornweg D, Sugar L, Gomez CL. Cities and greenhouse gas emissions: moving forward. Environ Urban 2011;20:1-21.  Back to cited text no. 3
    
4.
Cai D, Fraedrich K, Guan Y, Guo S, Zhang C. Urbanization and the thermal environment of Chinse and US-American cities. Sci Total Environ 2017;589:200-11.  Back to cited text no. 4
    
5.
Sanchez EGM, Renterghem VT, Thomas P, Botteldooren D. The effect of street canyon design on traffic noise exposure along roads. Build Environ 2016;97:96-110.  Back to cited text no. 5
    
6.
Carvalho DS, Fidélis T. The perception of environmental quality in Aveiro, Portugal: A study of complaints on environmental issues submitted to the City Council. Local Environ 2009;14:939-61.  Back to cited text no. 6
    
7.
To WM, Lai TM, Lo WC, Lam KH, Chung WL. The growth pattern and fuel life cycle analysis of the electricity consumption of Hong Kong. Environ Pollut 2012;165:1-10.  Back to cited text no. 7
    
8.
Corburn J. Toward the Healthy City: People, Places, and the Politics of Urban Planning., Cambridge: MIT Press 2009.  Back to cited text no. 8
    
9.
Xie H, Kang J. Relationships between environmental noise and social-economic factors: case studies based on NHS hospitals in Greater London. Renew Energ 2009;34:2044-53.  Back to cited text no. 9
    
10.
Petraitis E, Pranskevicius M, Idzelis RL, Vaitiekunas P. Predictive modelling of environmental noise levels in Lithuanian urban areas. Environ Eng Manag J 2011;10:1935-41.  Back to cited text no. 10
    
11.
Murphy E, King EA. Strategic environmental noise mapping: Methodological issues concerning the implementation of the EU Environmental Noise Directive and their policy implications. Environ Int 2010;36:290-8.  Back to cited text no. 11
    
12.
Brainard JS, Jones AP, Bateman IJ, Lovett AA. Exposure to environmental urbannoise pollution in Birmingham, UK. Urban Stud 2004;41:2581-600.  Back to cited text no. 12
    
13.
Mak CM, Wang Z. Recent advances in building acoustics: an overview of prediction methods and their applications. Build Environ 2015;91:118-26.  Back to cited text no. 13
    
14.
To WM, Mak CM, Chung WL. Are the noise levels acceptable in a built environment like Hong Kong? Noise Health 2015;17:429-39.  Back to cited text no. 14
[PUBMED]  [Full text]  
15.
Hong JY, Jeon JY. Relationship between spatiotemporal variability of soundscape and urban morphology in a multifunctional urban area: a case study in Seoul, Korea. Build Environ 2017;126:382-95.  Back to cited text no. 15
    
16.
de Kluizenaar Y, Janssen SA, van Lenthe FJ, Miedema HM, Mackenbach JP. Long-term road traffic noise exposure is associated with an increase in morning tiredness, J Acoust Soc Am 2009;126:626-33.  Back to cited text no. 16
    
17.
Bodin T, Albin M, Ardö J, Stroh E, Östergren PO, Björk J. Road traffic noise and hypertension: results from a cross-sectional public health survey in southern Sweden. Environ Health 2009;8:38.  Back to cited text no. 17
    
18.
Marquis-Favre C, Premat E, D. Aubree D. Noise and its effects—a review on qualitative aspects of sound. Part I: notions and acoustic ratings. Acta Acoust United Acoust 2005;91:613-25.  Back to cited text no. 18
    
19.
Kang S, Ou D, Mak CM. The impact of indoor environmental quality on work productivity in university open-plan research offices. Build Environ 2017;124:78-89.  Back to cited text no. 19
    
20.
Al Horr Y, Arif M, Kaushik A, Mazroei A, Katafygiotou M, Elsarrag E, Occupant productivity and office indoor environment quality: a review of the literature. Build Environ 2016;105:369-89.  Back to cited text no. 20
    
21.
Ma KW, Wong HM, Mak CM. A systematic review of human perceptual dimensions of sound: meta-analysis of semantic differential method applications to indoor and outdoor sounds. Build Environ 2018;133:123-50.  Back to cited text no. 21
    
22.
Meng Q, Kang J. Effect of sound-related activities on human behaviours and acoustic comfort in urban open spaces. Sci Total Environ 2016;573:481-93.  Back to cited text no. 22
    
23.
Babisch W, Ising H, Gallacher JE. Health status as a potential effect modifier of the relation between noise annoyance and incidence of ischaemic heart disease. Occup Environ Med 2003;60:739-45.  Back to cited text no. 23
    
24.
World Health Organization. Burden of Disease from Environmental Noise, Quantification of Healthy Life Years Lost in Europe. Bonn, Germany 2011.  Back to cited text no. 24
    
25.
Lam KC, Chung YT. Differential exposure of urban population to road trafficnoise in Hong Kong. Trans Res Part D: Transport Environ 2012;17:466-72.  Back to cited text no. 25
    
26.
Lam KC, Chan PK, Chan TC, Au WH, Hui WC. Annoyance response to mixedtransportation noise in Hong Kong. Appl Acoust 2009;70:1-10.  Back to cited text no. 26
    
27.
To WM, Ip CW, Lam CK., Yau TH. A multiple regression model for urban traffic noise in Hong Kong. J Acoust Soc Am 2002;112:551-6.  Back to cited text no. 27
    
28.
Mak CM, Leung WK, Jiang GS. Measurement and prediction of road traffic noise at different levels of a high-rise residential building. Build Serv Eng Res T 2010;31:131-9.  Back to cited text no. 28
    
29.
Hong Kong Environmental Protection Department. Guidance Note No 12/ 2005, Environmental Impact Assessment Ordinance, Cap. 499. Hong Kong; 2005.  Back to cited text no. 29
    
30.
Hong Kong Environmental Protection Department. Draft comprehensive plan to tackle road traffic noise. Hong Kong; 2006.  Back to cited text no. 30
    
31.
Kang J. Numerical modeling of the sound fields in urban squares. J Acoust Soc Am 2005;117:3695-706.  Back to cited text no. 31
    
32.
Lee PJ, Kang J. Effect of height-to-width ratio on the sound propagation in urban streets. Acta Acoust United Acoust, 2015;101:73-87.  Back to cited text no. 32
    
33.
Horoshenkov KV, Hothersall DC, Mercy SE. Scale modelling of sound propagation in a street canyon. J Sound Vib 1999;223:795-819.  Back to cited text no. 33
    
34.
Delany ME, Harland DG, Hood RA, Scholes WE. The prediction of noise levels L10 due to road traffic, J Sound Vib 1976;48:305-25.  Back to cited text no. 34
    
35.
Department of Transport Welsh Office. Calculation of road traffic noise. London; 1988.  Back to cited text no. 35
    
36.
Steele CM. A critical review of some traffic noise prediction models. Appl Acoust 2001;62:271-87.  Back to cited text no. 36
    
37.
Hong Kong Planning Department. Land Utilization in Hong Kong 2015, Planning Data. Hong Kong, China; 2016.  Back to cited text no. 37
    
38.
Hong Kong Highways Department. Retrieved from www.hyd.gov.hk/en/road_and_railway/road_projects/index.html  Back to cited text no. 38
    
39.
Hong Kong Census and Statistics Department. Retrieved from www.censtatd.gov.hk/home/  Back to cited text no. 39
    
40.
Chang TY, Lin HC, Yang WT, Bao BY, Chan CC. A modified Nordic prediction model of road traffic noise in a Taiwanese city with significant motorcycle traffic. Sci Total Environ 2012;432:375-81.  Back to cited text no. 40
    
41.
Givargis S, Karimi H. A basic neural traffic noise prediction model for Tehran’s roads. J Environ Manage 2010;91:2529-34.  Back to cited text no. 41
    
42.
Bendtsen H. The Nordic prediction method for road traffic noise. Sci Total Environ 1999;8:235-331.  Back to cited text no. 42
    
43.
Li B, Tao S, Dawson RW. Evaluation and analysis of traffic noise from urban roads in Beijing. Appl Acoust 2002;63:1137-42.  Back to cited text no. 43
    
44.
Cannelli GB, Gluck K, Santoboni S. A mathematical model for evaluation and prediction of the mean energy level of traffic noise in Italian town. Acustica 1983;53:31-6.  Back to cited text no. 44
    
45.
Mak CM, Leung WS. Traffic measurement and prediction of the barrier effect on traffic noise at different building levels. Environ Eng Manag J 2013;12:449-56  Back to cited text no. 45
    

Top
Correspondence Address:
Cheuk Ming Mak
Department of Building Services Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/nah.NAH_36_18

Rights and Permissions


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top