Traffic Noise Prediction for Delhi-NCR using Multiple RegressionModelling Approach

Noise emitted by traffic is, in addition to other causative factors, influenced by the gradient of the highway. In this work, vehicles were classified into 4 categories based on traffic noise levels of individual vehicles and homogeneity. Traffic noise data from individual vehicles was collected at 7 sites with different gradients on a state highway near Mandya city, Karnataka state, India. A gradient-based noise prediction mathematical model was developed for each category of vehicle by multiple regression analysis of data. Results from the model indicated that the gradient has more influence on traffic noise due to heavy vehicles than light and medium classes of vehicles. The models developed herein were calibrated from field observed data.

There is relatively little evidence of health effects of long-term exposure to traffic-related pollution in susceptible populations. We investigated whether long-term exposure to traffic air and noise pollution was associated with all-cause mortality or hospital readmission for myocardial infarction (MI) among survivors of hospital admission for MI. Patients from the Myocardial Ischaemia National Audit Project database resident in Greater London (n = 1 8,138) were followed for death or readmission for MI. High spatially-resolved annual average air pollution (11 metrics of primary traffic, regional or urban background) derived from a dispersion model (resolution 20 m × 20 m) and road traffic noise for the years 2003-2010 were used to assign exposure at residence. Hazard ratios (HR, 95% confidence interval (CI)) were estimated using Cox proportional hazards models. Most air pollutants were positively associated with all-cause mortality alone and in combination with hospital readmission. The largest associations with mortality per interquartile range (IQR) increase of pollutant were observed for non-exhaust particulate matter (PM(10)) (HR = 1.05 (95% CI 1.00, 1.10), IQR = 1.1 μg/m(3)); oxidant gases (HR = 1.05 (95% CI 1.00, 1.09), IQR = 3.2 μg/m(3)); and the coarse fraction of PM (HR = 1.05 (95% CI 1.00, 1.10), IQR = 0.9 μg/m(3)). Adjustment for traffic noise only slightly attenuated these associations. The association for a 5 dB increase in road-traffic noise with mortality was HR = 1.02 (95% CI 0.99, 1.06) independent of air pollution. These data support a relationship of primary traffic and regional/urban background air pollution with poor prognosis among MI survivors. Although imprecise, traffic noise appeared to have a modest association with prognosis independent of air pollution.

SummaryA growing body of research links traffic‐related environmental factors to childhood obesity; however, the evidence is still inconclusive. This review aims to fill this important research gap by systematically reviewing existing research on the relationship between traffic‐related environmental factors and childhood obesity. Based on the inclusion criteria, 39 studies are selected with environmental factors of interest, including traffic flow, traffic pollution, traffic noise, and traffic safety. Weight‐related behaviours include active travel/transport, physical activity (PA), and intake of a high trans‐fat diet or stress symptoms; weight‐related outcomes are mainly body mass index (BMI) or BMI z‐scores and overweight/obesity. Of 16 studies of weight‐related behaviours, significant associations are reported in 11 out of 12 studies on traffic flow (two positively and nine negatively associated with PA), five out of six studies on traffic safety (four positively and one negatively associated with PA), one study on traffic pollution (positively with unhealthy food consumption), and one study on traffic noise (negatively associated with PA). Among 23 studies of weight‐related outcomes, significant associations are reported in six out of 14 studies on traffic flow (five positively and one negatively associated with obesity outcome), seven out of 10 studies on traffic pollution (all positively associated with obesity outcome), and two out of five on traffic noise (all positively associated with obesity outcome). Our findings show that long‐term traffic pollution is weakly positively associated with children's BMI growth, and traffic flow, pollution, and noise could affect weight‐related behaviours. Associations between traffic density and noise and weight status are rather inconclusive.

With the continuous development of social economy and urbanization in our country, noise pollution has become the fourth largest environment pollution in our country but water pollution, air pollution, solid waste pollution, which is the widest influence of traffic noise. In recent years, with the development of automobile industry and urban transportation, road traffic noise pollution in Harbin phenomenon is increasingly prominent, which seriously affected the People's daily life. Through detailed survey we found that the research on road traffic noise pollution situation in Harbin is very less, and part of the evaluation is not accurate. In this paper, firstly the characteristics of road traffic noise and noise evaluation index selection is discussed in detail. The xuefu road sections in Harbin city traffic noise and traffic noise pollution monitoring research in detail strict accordance with national standards. According to monitoring data of xuefu road traffic noise pollution source in time and space distribution analysis, the roughly the time distribution and spatial distribution of xuefu road traffic flow. By the overall analyzed monitoring data characteristic, the xuefu road traffic noise pollution is serious. According the previous analysis and research, the researchers find out the main factors influencing the xuefu road traffic condition, and we puts forward some countermeasures for the noise reduction. Through the study of this paper, that is to Harbin city xuefu road for the future of the road traffic noise monitoring and management work provide favorable basis, and it has good practical application value. It aimed at Harbin city road traffic noise control, urban planning and construction, provide theoretical basis for government decisions.

In order to control noise pollution it is necessary to have a suitable calculation method for traffic noise prediction. Since 1950s many mathematical models for estimation of road traffic noise levels have been developed, and most of the available models are based on regression analysis of experimental data. This paper presents the application of soft computing techniques in traffic noise prediction. Two models for prediction of equivalent A-weighted level of road traffic noise are presented and their predictions are compared to experimental data collected by traffic noise monitoring in the urban environment, as well as to predictions of commonly used traffic noise models. The results obtained by statistical analysis of differences between the measured and the calculated noise levels show that the application of neural networks and optimization methods based on swarm intelligence and evolutionary algorithms may improve process of development, as well as accuracy of traffic noise prediction models.

Background: Epidemiological studies indicate that both traffic noise and outdoor air pollution may be associated with risk for cardiovascular disease. Only few studies consider these two environmental exposures together. Aims: To provide an overview of the latest knowledge on the association between environmental noise and outdoor air pollution on related cardiovascular health effects. To examine whether the evidence suggests that noise and air pollution interact or confound with each other. Methods: A search was conducted on Pubmed electronic database, retrieving the papers on the cardiovascular health effects of combined exposure to noise and outdoor air pollution. The search was limited to peer-reviewed papers published in English between 2005 and February 2013. Results: Few studies have considered the joint effect of traffic noise and air pollution on the cardiovascular system. 13 papers on cardiovascular efects of combined exposure to noise and outdoor air pollution were identified and reviewed. Road traffic noise was the most commonly investigated noise source. The main reviewed cardiovascular health outcomes included: arterial blood pressure and hypertension, coronary heart disease, myocardial infarction and stroke. The effects of additional adjustment for either noise or air pollution varied between studies. Conclusions: Both traffic noise and outdoor air pollution seemed to have an independent effects on cardiovascular health end points. There is a need of more well-designed longitudinal studies to further investigate the combined exposure from noise and outdoor air pollution.

In the Macao Peninsula, the high population density (49,763 inhabitants/km2) and the lack of control over the number of vehicles (460 vehicles/km) have led to an increase in urban pollution. To provide useful information to local government and urban planners, this paper investigates the spatial distribution of traffic noise in the Macao Peninsula. The interactions among urban form, traffic flow and traffic noise are addressed. Considering the spatial nature of urban geometry and traffic, a high-resolution GIS-based traffic noise model system is applied. Results indicate that the Macao Peninsula has fallen into a situation of serious traffic noise pollution. About 60% of traffic noise levels along the major pedestrian sidewalks in the evening peak hour exceed the National Standard of 70 dB(A) in China. In particular, about 21% of traffic noise levels along the pedestrian sidewalks are above the National Standard by 5 dB(A). Noticeably, the high pedestrian exposure to traffic noise in the historical urban area reduces the comfort of tourists walking in the historic centre and is ruining the reputation of the area as a World Cultural Heritage site.

With the rapid development of China's economy and road traffic, the number of motor vehicles in China is increasing year by year, and traffic noise pollution is becoming more serious. Many colleges and universities in our country are located around the urban traffic trunk road, and the campus acoustic environment is seriously affected by the traffic noise. Taking Xuchang University as an example, this paper monitors the traffic noise around and inside the campus according to the relevant standards of noise measurement, uses the equivalent continuous sound level method for acoustic calculation, evaluates the traffic noise monitoring results, and uses the significant difference analysis method to analyze the resulting data, the distribution law of traffic noise inside and outside the campus at different monitoring points and the time distribution characteristics of traffic noise inside and outside the campus in different periods and different days are obtained. Finally, the test results and the actual situation of a university campus is combined, this paper puts forward corresponding suggestions and measures the impact of campus traffic noise on the campus acoustic environment, to provide a reference for the design and implementation of noise reduction measures for the university campus acoustic environment.

Modelling of traffic noise in the vicinity of urban road intersections

NUMERICAL MODELLING OF MEDIAN ROAD TRAFFIC NOISE BARRIERS

In densely populated areas where the communities strive, noise pollution has become a critically alarming problem and must be addressed accordingly. The World Health Organization also recognizes it as a concerning public health issue in the urban areas. More often, the effects of noise towards the residents living in the vicinity areas can include the worsening of life quality and result in physiological effects, among others. We hypothesize that the increasing traffic results in higher noise pollution levels. The aim of this study was to quantify traffic noise pollution using noise pollution level (NPL) and traffic noise index (TNI) at selected cities in Kelantan, namely Tanah Merah and Pasir Mas. A sound level meter (TENMARS TM102) was used to record the noise level, and the traffic volumes were determined by using tally counts. The noises were measured during four different peak hours from 08:00 to 22:00. The recorded noise levels were compared with the Department of Environment (DOE) recommended limit of 60 dB(A) during daytime. The traffic noise pollution level was confirmed to be higher when the traffic volume counts increased. The results suggest proper mitigation measures such as enforcing zoning regulations and promoting public awareness campaigns were needed to reduce the noise pollution level.

This study evaluated vehicular traffic flows and associated traffic noise levels at major junctions in Port Harcourt metropolis. Traffic volume was determined using visual traffic count method. Noise level was determined using an Extech digital sound level meter. Data were statistically analyzed and noise descriptors determined. Average daily traffic flows ranged from 149 to 4224. Average daily total traffic volume was minimum at Eliozu junction and maximum Eleme junction. Traffic noise varied proportionally with traffic flow. The result showed that Average noise levels varied from 70.5dB(A) to 95.9dB(A). The result showed that L10 mean values were 89.0, 90.2 91.7, 94.7 and 97.2dB(A) for Eliozu, Choba, Runuokuta, Runuokoro and Eleme junctions respectively; L50 mean values were 80.5, 81.2, 83.8, 85.2 and 89.3dB(A) for Eliozu, Choba, Runuokuta, Runuokoro and Eleme junctions respectively; L90 mean values were 69.5, 70.1, 70.4, 72.0 and 78.2dB(A) for Choba, Eliozu, Runuokuta, Runuokoro and Eleme junctions respectively. Noise climate (NC) aggregated mean values were 18.9, 19.0, 20.7, 21.3 and 22.7 for Eliozu, Eleme, Choba, Runuokuta and Runuokoro junctions respectively; Leq aggregated mean values were 86.5, 88.3, 91.4, 93.8 and 95.3dB(A) for Eliozu, Choba, Runuokuta, Runuokoro and Eleme junctions respectively; noise pollution index (Lnp) aggregated mean values were 105.4, 109.0, 113.0, 114.3 and 116.6dB(A) for Eliozu, Choba, Runuokuta, Eleme and Runuokoro junctions respectively; traffic noise index (TNI) aggregated mean values were 115.7, 122.3, 124.2, 125.6 and 132.8dB(A) for Eliozu, Choba, Runuokuta, Eleme and Runuokoro junctions respectively. The study showed evidences of high traffic noise pollution at major junctions in Port Harcourt metropolis, which may adversely affect public health and therefore requires intervention by all stakeholgers, including decision makers.

Noise pollution arises as a severe problem to the community and ground transportation has highly impacted noise environment of the neighbourhood. In the present study, six stations were selected from three different locations in order to assess the traffic noise impact among residential area along Jalan Kluang, Batu Pahat. The objectives of this study focused on two main stages: (1) determination of noise indices; and (2) assessment of traffic noise impact. The noise indices such as equivalent continuous noise level (LAeq), noise level exceeded 10% (L10), 90% (L90) and the maximum noise level (LAFmax) of peak hours measurement time were determined using sound level meter (SLM) at all six stations. Besides, traffic noise index (TNI) and noise pollution level (LNP) were calculated based on the measured noise indices to expose the traffic noise impact. Overall, the result of the present study showed that residential area faces traffic noise disturbance partially whereby 50% of the stations exceeded the DOE standard which was 55dBA. Nevertheless, the residents living nearby Jalan Kluang are commonly facing traffic noise nuisance, but stations located further inside residential areas tend to have more residential background noises apart from traffic influence.

The noise pollution in the cities has become of great concern all over the World. This research deals with evaluating the noise levels that was carried out in the study area streets (Karada city). The aims of this research were to evaluate the environmental noise pollution present in Karada city caused by traffic noise, compare the measured noise levels in the city with legislations and to establish the connection between noise and the number of active vehicles. The research work was carried out in three different stages: The first stage dealt with the physical measurements (using Apparatus Device), while the second stage had to do with the field surveys and questionnaires conducted to measure and evaluate the effect of noise caused by vehicular movements in the main streets during traffic rush hours. The third stage was carried out in parallel with the first stage as the amount of traffic in the study area streets was measured. Ultimately, the research concludes with results and recommendations to protect the study area from traffic noise pollution, which in return would be very beneficial to studies that deal with planning a noise-free environment.

A technique has been developed for predicting road traffic noise for environmental assessment, taking into account traffic volume as well as road surface conditions. The ASJ model (ASJ Prediction Model for Road Traffic Noise, 1999), which is based on the sound power level of the noise emitted by the interaction between the road surface and tires, employs regression models for two road surface types: dense-graded asphalt (DGA) and permeable asphalt (PA). However, these models are not applicable to other types of road surfaces. Accordingly, this paper introduces a parameter estimation procedure for ASJ-based noise prediction models, utilizing a harmony search (HS) algorithm. Traffic noise measurement data for four different vehicle types were used in the algorithm to determine the regression parameters for several road surface types. The parameters of the traffic noise prediction models were evaluated using another measurement set, and good agreement was observed between the predicted and measured sound power levels.