Community responses to road traffic noise in Hanoi and Ho Chi Minh City

Vietnam is a developing country in southeast Asia, and its environment has been seriously affected by industrialization and urbanization. In large cities like Hanoi (northern Vietnam) and Ho Chi Minh City (southern Vietnam), noise emission from road traffic has been found to be a serious concern among general public. In 2005 and 2007, two large-scale socio-acoustic surveys of community response to road traffic noise were conducted to investigate human reactions to road traffic noise in these cities; the sample sizes were 1503 people in Hanoi and 1471 in Ho Chi Minh City. The noise exposure levels (L_den) were 70–83 dB in Hanoi and 75–83 dB in Ho Chi Minh City. Noise annoyance was estimated using standardized annoyance scales. For both cities, dose–response relationships were established between L_den and the percentage of highly annoyed respondents. Compared to annoyance responses of European people, Vietnamese were less annoyed by road traffic noise by about 5 dB. Hanoi respondents seemed to be more annoyed by noise than Ho Chi Minh City respondents. Conversation and sleep disturbances were not as serious as expected in either city. Furthermore, window orientation in the home was found to affect activity disturbances.     

Comparison of models to predict annoyance from combined noise in Ho Chi Minh City and Hanoi

Seven models were compared in terms of the ability to predict the annoyance due to the combination of aircraft and road traffic noises on the basis of data collected around airports in Ho Chi Minh City and Hanoi, Vietnam. The 24-h average sound levels L_Aeq,24h and unweighted means of annoyance scores for aircraft, road traffic, and combined noise were used to solve the regression equations for the seven models. The results indicate that road traffic noise exposure and annoyance were more than those of aircraft noise at almost all sites in both Ho Chi Minh City and Hanoi. Among the considered models, the dominant source model yielded the highest coefficients of determination, with R² values of 0.82 and 0.90 for surveys in Ho Chi Minh City and Hanoi, respectively. These results suggest that the dominant source model is the most useful model in the vicinity of those airports in Vietnam where road traffic noise is more dominant than aircraft noise. This is convenient for situations in which dose-response curves are established separately for different noise sources.     

Characteristics of road traffic noise in Hanoi and Ho Chi Minh City, Vietnam

Noise pollution due to road traffic is a major global concern because of its negative impact on the quality of life in communities everywhere. In Vietnam, traffic noise has become an increasingly noticeable and serious problem in large cities like Hanoi and Ho Chi Minh City. To gain more insight into the characteristics of this noise, intensive noise measurements were conducted in Hanoi and Ho Chi Minh City in September 2005 and September 2007, respectively. A comprehensive dataset of noise was obtained that included 24-h noise measurements as well as short-term noise recordings. The volume of traffic was also quantified by reproducing video camera recordings. Noise datasets from both cities were then compared with a dataset of Japanese traffic noise obtained in Kumamoto. The results showed that the traffic noise in Hanoi and Ho Chi Minh City was characterized by relatively high noise exposure levels due to the large number of motorbikes and frequent horn sounds. The sound of horns contributed a definite impact of 0-4 dB on noise exposure in Hanoi and Ho Chi Minh City, where noise levels decreased with the absence of horn sounds. Our results also showed differences in the characteristic traffic noise of Vietnam and Japan.     

Empirical revision of noise mapping

Following the Directive 2002/49/EC the European States shall apply the day–evening–night level, L_den, among others, for revision of the strategic noise mapping. Definition of L_den is based on A-weighted long term average sound levels, L_d, L_e, and L_n, which represent 365 days, evenings, and nights of a typical year. L_den approximation can be found from a few measurements of the A-weighted short term average sound levels, during the day-, evening-, and nights, L_di, L_ei, and L_ni, where i = 1, 2, … , m ? 365. The uncertainty of L_den approximation is calculated.     

Assessing methodologies for calculating road traffic noise levels in Ireland - Converting CRTN indicators to the EU indicators (Lden, Lnight)

To comply with the EU Noise Directive 2002/49/EC, Member States are required to produce strategic noise maps for designated areas, including mapping road traffic noise from major roads. These maps must be presented using the EU indicators L_den and L_night. However, the most common noise indicator used in Ireland at present is the L_A10,18h indicator arising from the use of the Calculation of Road Traffic Noise (CRTN) prediction method. Therefore, a relationship needs to be established between L_A10,18h and L_den and L_night, separately. In addition to noise mapping these indicators are used for noise abatement purposes, so the proposed relationship must be accurate and robust. In 2002, the UK’s Transport Research Laboratory (TRL) published a paper describing mathematical procedures that could be used to convert values of L_A10 to L_den and L_night. These procedures were then adopted for use in Ireland. This paper examines the suitability of the TRL conversion methods 1 and 3 for use under Irish road conditions. Method 2 was not considered in this study, as it was a methodology not applicable in an Irish scenario. Studies concluded that where hourly traffic data are available, the conversion methodology outlined in TRL Method 1 is robust and reproducible. However, in the absence of hourly traffic data where daily traffic counts are used, the relevant conversion procedures produce variable results for both L_den and L_night when applied to Irish road conditions. To reduce the variability, new conversion procedures were developed, specifically for Irish road conditions.     

Traffic background level and signal duration effects on aircraft noise judgment

The effects of traffic background noise on the judged noisiness of aircraft flyover events has been further examined in the study reported here. A series of 72 flyover events were assessed by a jury of 35 observers, during 12 separate listening sessions conducted in a controlled test area designed to simulate typical indoor listening conditions. Each aricraft signal was superimposed on a controlled random traffic background signal having a duration exceeding that of the aircraft event. The primary conclusions reached in this investigation show that the presence of a steady mean traffic background noise can reduce the perceived noisiness of aircraft flyover events, provided that the judgment time available is sufficiently greater than the aircraft event time. For a given peak event level, a reduction in associated background noise of 21 dB(A) is shown to be equivalent subjectively to an increase of 5·5 dB(A) in peak event level, with fixed background conditions. Best linear data regressions were found with an index of the form L₀ + k(L_p ? L₀), where L_p and L₀ are the peak signal and mean background levels, respectively. Although the regressions obtained with the noise pollution index, L_NP, for single event judgments generally showed a lower correlation than the L₀ and (L_p ? L₀) regression variables the score data did show a number of significant trends which are also associated with the L_NP index variations computed for single noise events.     

Laboratory annoyance and different traffic noise sources

The acute annoyance reaction to different noise sources (lorries, aircraft, mopeds and trains) was investigated in a laboratory experiment. Students were exposed to different noise climates at noise levels 70 and 80 dB(A) for 25 minutes, and their reactions were subsequently assessed by using a questionnaire. Their general sensitivity to noise was also evaluated. The results demonstrated that L_eq gave the best correlation with annoyance. However, lorry noise was found to be less disturbing than aircraft noise at the same L_eq value. This was more pronounced if the different noises were compared at equal peak dB(A) levels. The results suggest that other factors such as the irregularity of the noise or the individual experience of the noise are of importance for the annoyance reaction. A relationship was found between the general annoyance score and annoyance reactions in the laboratory. Questionnaires could thus be a suitable tool for identifying noise sensitive persons.     

Estimating the model-specific uncertainty of aircraft noise calculations

Aircraft noise contours are estimated with model calculations. Due to their impact, e.g., on land use planning, calculations need to be highly accurate, but their uncertainty usually remains unaccounted for. The objective of this study was therefore to quantify the uncertainty of calculated average equivalent continuous sound levels (L_Aeq) of complex scenarios such as yearly air operations, and to establish uncertainty maps. The methodology was developed for the simulation program FLULA2. In a first step, the partial uncertainties of modelling the aircraft as a sound source and of modelling sound propagation were quantified as a function of aircraft type and distance between aircraft and receiver. Then, these uncertainties were combined for individual flights to obtain the uncertainty of the single event level (L_AE) at a specified receiver grid. The average L_Aeq of a scenario results from the combination of the L_AE of many single flights, each of which has its individual uncertainties. In a last step, the uncertainties of all L_AE were therefore combined to the uncertainty of the L_Aeq, accounting also for uncertainties of the number of movements and of prognoses. Uncertainty estimations of FLULA2 calculations for Zurich and Geneva airports revealed that the standard uncertainty of the L_Aeq ranges from 0.5 dB (day) to 1.0 dB (night) for past-time scenarios when using radar data as input, and from 1.0 dB (day) to 1.3 dB (night) for future scenarios, in areas where L_Aeq ⩾ 53 dB (day) and L_Aeq ⩾ 43 dB (night), respectively. Different uncertainty values may result for other models and/or airports, depending on the model sophistication, traffic input data, available sound source data, and airport peculiarities such as the specific aircraft fleet or prevailing departure and arrival procedures. The methodology, while established for FLULA2 on Zurich and Geneva airports, may be applied to other models and/or airports, but the partial uncertainties have to be specifically re-established to account for individual models and underlying sound source data.     

Comments on “variational equations for thin elastic shells”

A further study consisting of acoustic and subjective measurements of 552 Chinese firemen at 12 fire stations in Hong Kong has been carried out. Annoyance with aircraft and traffic noise conditions, as expressed by the firemen, was found to correlate well with the acoustic measurements. For aircraft noise the correlation of annoyance with the Number and Noise Index (NNI) was slightly better than with the dB(A) peak value. For traffic noise the similar correlation with the mean sound pressure levels which exceeds 10 % of the sampling period (L₁₀) was slightly better than with the Noise Pollution Level and the Traffic Noise Index. The correlation of the arousal due to the aircraft and traffic noise was similarly found to depend on the NNI and L₁₀ values. However, traffic noise was responsible for more disturbance than aircraft noise. The study demonstrated the desirability of adopting indoor acoustic measurements instead of outdoor measurements for any survey of this kind.