Road Traffic Noise Pollution Research Articles

Impact of car electrification on urban noise pollution: a microscopic traffic simulation study

Noise pollution is a major health and well-being concern, being associated with, e.g., sleep disturbance, annoyance, and cardiovascular diseases. Road traffic is, by far, the largest contributor to this noise pollution in urban areas. With the growing electrification of cars in developed countries, it is commonly believed that road traffic noise pollution and the associated problems will disappear shortly in urban areas. However, at high speeds, the difference in noise emissions between internal-combustion-engine and electric vehicles is not significant, and at low speeds, the propulsion noise of electric cars is not negligible. Few works have quantitatively estimated the actual impact of passenger cars electrification on population exposure to noise, especially considering such aspects as the gradual transition towards a fully electric fleet. The present contribution thus aims to assess the evolution of population exposure to noise pollution, using a microscopic traffic simulation on the scale of an entire city, noise mapping, and various indicators, at multiple stages of this transition.

Multidimensional analysis of road traffic noise and probable public health hazards in Barisal city corporation, Bangladesh

Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

Road traffic noise pollution and prevalence of ischemic heart disease: modelling potential association and abatement strategies in noise-exposed areas.

In many developing countries with surging vehicular traffic and inadequate traffic management, excessive road traffic noise exposure poses substantial health concerns, linked to increased stress, insomnia and other metabolic disorders. This study aims to assess the linkage between sociodemographic factors, traffic noise levels in residential areas and health effects using a cross-sectional study analyzing respondents' perceptions and reports. Noise levels were measured at 57 locations in Srinagar, India, using noise level meter. Sound PLAN software was employed to generate noise contour maps, enabling the visualization of noise monitoring locations and facilitating the assessment of noise levels along routes in proximity to residential areas. Correlation analysis showed a strong linear relationship between field-measured and modelled noise (r2 = 0.88). Further, a questionnaire-based survey was carried out near the sampling points to evaluate the association of ischemic heart disease with traffic noise. Residents exposed to noise levels (Lden > 60dB(A)) were found to have a 2.24 times higher odds ratio. Compared to females, males reported a 16% higher prevalence of the disease. Multi-faceted policy strategies involving noise mapping initiatives, source noise standards, traffic flow urban mobility optimization, smart city initiatives and stringent litigatory measures could significantly reduce its detrimental impact on public health. Finally, this study envisions a region-specific strong regulatory framework for integrating noise pollution mitigation strategies into the public health action plans of developing nations.

Design and validation of a new asphalt mixture to reduce road traffic noise pollution in urban areas

The aim of this paper is to design a novel asphalt mixture capable of reducing tyre/pavement interaction noise for urban roads at speeds around 50 km/h. To this end, various mechanical and functional tests were carried out both in the laboratory and on a real road in the city of Florence. The asphalt mixture developed improves the noise reduction properties without compromising its mechanical performance or neglecting its rolling and skid resistance. To this end, various mechanical tests were carried out in the laboratory, including water sensitivity or wheel tracking tests, as well as functional tests relating to texture, flow resistance and sound absorption. Full-scale measurements were carried out using the CPX method. The result of the study was a novel asphalt mixture that reduced noise by 3 dB compared to a conventional road.

Development and Testing of an Active Noise Control System for Urban Road Traffic Noise

As urbanization accelerates, the increasing number of vehicles and travel demands contribute to escalating road traffic noise pollution. Although passive noise control techniques such as noise barriers and green belts effectively mitigate noise, they occupy urban space, exacerbating the scarcity and high cost of already congested city areas. Emerging as a novel noise reduction strategy, active noise control (ANC) eliminates the need for physical isolation structures and addresses the noise within specific frequency ranges more effectively. This paper investigates the characteristics of urban road traffic noise and develops an ANC prototype. Utilizing the Least Mean Squares (LMS) algorithm, we conduct active noise control tests for various types of single- and dual-frequency noise within the prototype’s universal platform to validate its actual noise reduction capabilities. The study demonstrates that urban road traffic noise is mostly in the mid- to low-frequency range (below 2000 Hz). The developed ANC prototype significantly reduces single- or dual-frequency noise within this range, achieving a maximum noise reduction of nearly 30 dB(A). Future research should expand noise reduction tests across more frequency bands and assess the noise reduction effectiveness against real road traffic noise.

Road Traffic Noise and Incidence of Primary Hypertension: A Prospective Analysis in UK Biobank

BackgroundThe quality of evidence regarding the associations between road traffic noise and hypertension is low due to the limitations of cross-sectional study design, and the role of air pollution remains to be further clarified. ObjectivesThe purpose of this study was to evaluate the associations of long-term road traffic noise exposure with incident primary hypertension; we conducted a prospective population-based analysis in UK Biobank. MethodsRoad traffic noise was estimated at baseline residential address using the common noise assessment method model. Incident hypertension was ascertained through linkage with medical records. Cox proportional hazard models were used to estimate hazard ratios (HRs) for association in an analytical sample size of over 240,000 participants free of hypertension at baseline, adjusting for covariates determined via directed acyclic graph. ResultsDuring a median of 8.1 years follow-up, 21,140 incident primary hypertension (International Classification of Diseases-10th Revision [ICD-10]: I10) were ascertained. The HR for a 10 dB[A] increment in mean weighted average 24-hour road traffic noise level (Lden) exposure was 1.07 (95% CI: 1.02-1.13). A dose-response relationship was found, with HR of 1.13 (95% CI: 1.03-1.25) for Lden >65 dB[A] vs ≤55 dB[A] (P for trend <0.05). The associations were all robust to adjustment for fine particles (PM2.5) and nitrogen dioxide (NO2). Furthermore, high exposure to both road traffic noise and air pollution was associated with the highest hypertension risk. ConclusionsLong-term exposure to road traffic noise was associated with increased incidence of primary hypertension, and the effect estimates were stronger in presence of higher air pollution.

Characterization of Road Traffic Air and Noise Pollution at Mymensingh City

The study aimed to investigate the concentration of atmospheric particulate matter (PM2.5, PM10), carbon monoxide (CO) and sound level from traffic areas in Mymensingh city. Airveda air quality monitor, Testo 317-3 and sound level meter were used to measure the concentration of particulate matter (PM2.5, PM10), CO and sound level, respectively. Samples were collected from twenty-one locations for consecutive seven days. The PM2.5 concentration fluctuated from 63 µgm-3 to 231.14 µgm-3 where the average concentrations were 99.85 µgm-3, 109.54 µgm-3 and 114.70 µgm-3 during morning, noon and afternoon, respectively. The highest PM2.5 was found at Bypass Mor (231.14 µgm-3) during the afternoon whereas the lowest concentration was observed at Notun Bazar (63 µgm-3) during noon. The PM10 concentration varied from 94.57 µgm-3 to 614.57 µgm-3 and the average concentration of PM10 was 163.78 µgm-3, 205.04 µgm-3 and 157.14 µgm-3 during morning, noon and afternoon, respectively. The lowest concentration was found at Trishal bus stand (94.57 µgm-3) during the morning. The CO concentration ranged from 1.28 ppm to 84.86 ppm whereas the mean values were 16.23 ppm, 24.80 ppm, and 17.35 ppm during morning, noon and evening, respectively. The highest concentration was found at Bridge Mor (84.86 ppm) during noon whereas the lowest concentration was found at Kewatkhali (1.28 ppm) during afternoon. The sound levels varied from 46.6 dB to 123.6 dB whereas the mean values were 76.80 dB, 81.41 dB and 91.08 dB during morning, noon and evening, respectively. The highest sound level was found at Burapeer Mazar (102.22 dB) whereas the lowest sound occurred at Jobbar Mor (49.91 dB). Considering the concentration of particulate matter, CO and sound level, it is concluded that these values were higher than the values observed in most of the European cities and also exceeded the Bangladesh National Ambient Air Quality Standard.

Environmental risk factors of incident distal sensorimotor polyneuropathy: Results from the prospective population-based KORA F4/FF4 study

Distal sensorimotor polyneuropathy (DSPN) is a common condition in older populations with high prevalence of obesity and type 2 diabetes. We hypothesised that the risk of DSPN is increased by multiple ubiquitous environmental risk factors, particularly in people with obesity. This study was based on 423 individuals aged 62–81 years without DSPN who participated in the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4 survey (2006–2008) in Southern Germany. During 6.5 years of follow-up, 188 participants developed clinical DSPN as assessed by the Michigan Neuropathy Screening Instrument. Environmental exposures, including air temperature, surrounding greenness (assessed with the normalized difference vegetation index [NDVI]), long-term road traffic noise and air pollution, were assessed at participants' residences. The cumulative risk index (CRI) evaluated the joint effects of co-occurring exposures on DSPN risk based on effect estimates from multi-exposure Poisson regression models. The models were adjusted for age, sex, height, waist circumference, smoking, alcohol consumption, physical activity, education and neighbourhood socioeconomic status. In the entire cohort, the co-occurrence of an interquartile range (IQR) decrease in temperature of the warm season and NDVI in a 100-m buffer and of an IQR increase in night-time average traffic noise and in annual average particle number concentration (PNC) was positively associated with incident DSPN (CRI [95 % CI] 1.39 [1.02, 1.91]). Effect estimates for exposure combinations were generally higher in individuals with obesity (CRI 1.34–2.01) than in those without obesity (CRI 0.90–1.33). The four-exposure model showed a twofold increased risk of DSPN among obese (CRI [95 % CI] 2.01 [1.10, 3.67]), but not among non-obese individuals (CRI [95 % CI] 1.18 [0.83, 1.67]). Thus, ubiquitous environmental exposures jointly augment the risk of DSPN in the older population. Lower air temperature in the warm season, less greenness, and higher noise levels and ultrafine particle concentrations identified people with obesity as a particularly vulnerable subgroup.

Assessment of road traffic noise pollution in Basudevpur – A case study on the sub‐urban area of Bhadrak, Odisha, India

AbstractThe long‐term effect of noise pollution is a serious environmental concern for those living near the road due to numerous anthropogenic activities. A calibrated sound level meter was used to assess the noise levels in a semi‐urban area known as Basudevpur Town (16 squares) in March 2021. A sample of the general public (126) was taken and interviewed using a questionnaire to find out how they felt about noise pollution and its impact on their health. A relationship between demographic characteristics and public health issues was discovered in the current investigation. The ANNOVA test was used to determine the association between noise exposure and the probable impacts of this noise exposure on this town, and the Chi‐square test in SPSS 20.0 was utilized to study the relationships between demographic character and irritation. The study reveals that the noise levels were greater than the CPCB's permissible limit for Indian road conditions. The primary source of noise, according to the study, is road traffic noise. The impact of noise pollution on the public's sleep problem was also analyzed. In order to limit noise pollution and its influence on public health, proper administrative planning is necessary.

Road traffic noise and incidence of primary hypertension: a prospective analysis in UK Biobank

BACKGROUND AND AIM The quality of evidence regarding the associations between road traffic noise and hypertension is low due to the limitations of cross-sectional study design, with the role of air pollution remains to be further clarified. To evaluate associations of long-term road traffic noise and air pollution exposure with incident primary hypertension, we conducted a prospective population-based analysis in UK Biobank. METHODS Road traffic noise was estimated at baseline residential address using the Common Noise Assessment Method model. Incident primary hypertension (ICD 10: I10) was ascertained through linkage with medical records for ~250 000 participants. Cox proportional hazard models were used to estimate hazard ratios (HRs) for association between road traffic noise and incident primary hypertension, adjusting for covariates determined via directed acyclic graph. RESULTS During a median of 8.1 years follow up, 21 185 incident primary hypertension cases were ascertained. The HR for a 10 dB[A] increase in mean weighted average 24-h road traffic noise level (Lden) exposure was 1.08 [95% confidence interval (CI) 1.02, 1.14]. A dose-response relationship was found, with HRs of 1.14 (95% CI: 1.04, 1.26) for Lden &amp;#x3e; 65dB[A] vs. &amp;#x2264; 55dB[A] (P trend &amp;#x3c;0.05), and HRs of 1.15 (95% CI: 1.04, 1.26) for night-time road traffic noise (Lnight)&amp;#x3e;55 dB[A] vs. &amp;#x2264; 45dB[A] (P trend &amp;#x3c;0.05). The associations were all robust to adjustment for fine particulate matter (PM2.5) and nitrogen dioxide (NO2). Furthermore, the highest incident hypertension risk was seen in the group who were exposed to the highest level of both road traffic noise and air pollution. CONCLUSIONS Long-term exposure to road traffic noise was associated with increased incidence of primary hypertension, and the effect estimates were greater in those with higher air pollution exposure. KEYWORDS road traffic noise; air pollution; primary hypertension; prospective analysis

Influence of the Urban Intersection Reconstruction on the Reduction of Road Traffic Noise Pollution

The authors present the unsolved issue of the contextual design of urban intersections (UI) from the point of view of traffic noise emissions around residential buildings in the Slovak context. Noise barriers are very rarely used in urban areas, due to such reasons as lack of space for their installation, traffic safety (view at intersections), architectural factors, as well as the fact that they represent a barrier for pedestrians and cyclists. The use of low-noise asphalt pavements is limited in urban areas primarily due to the high cost of production and maintenance of these covers, their limited durability in a colder climate, and lower efficiency compared to the roads outside urban areas. From this aspect of urban intersection design, the authors focused on the identification of individual factors associated with the significant reduction (2–8 dB) of traffic noise induced by the reconstruction of non-signalized urban intersections into roundabouts. The authors draw attention to the fact that both new surfaces of intersection branches and the change in traffic processes contribute to the aforementioned reduction. This finding was obtained by comparing direct measurements of noise levels and the results of their predicted values using validated 3D models in the CadnaA program. Noise emissions were measured by a noise analyzer (NOR-1210), and traffic noise emissions were predicted by the “Nouvelle Méthode de Prévision du Bruit” methodology (NMPB-1996). Based on the analysis of the measured and predicted traffic noise levels, the authors objectivized the share of reduction in traffic processes in the 2–3 dB range. The authors objectified the share of traffic noise reduction due to the change in traffic processes as being in the 2.2–3.3 dB range. The presented research results could contribute to a quantifiable reduction in the noise load in the external environment of residential buildings using the contextual design of intersections.

Spatial distribution of health-risky road traffic noise pollution in Dessie City, North East Ethiopia.

Dessie is the trade center for northeast Ethiopia. High traffic flow plus overacting of promotion made the city noisy. There is a shortage of relevant evidence that enforces policy makers to design intervention plans. Therefore, this study aimed to explore the health-risky road traffic noise pollution in Dessie City, Ethiopia. The study was conducted by purposive selection of the study area and sampling sites of the city from May 31, 2021 -June 6, 2021. Noise level recordings were taken by a digital Sound Meter and location data was collected by Global Positioning System. Residential, health facility, commercial, and mixed sites were identified by field observation. A total of 20 noise sampling points were included. The sampling points were selected by considering World Health Organization guideline. The measurements were taken twice a day at peak hours, between 8:00-11:00am and 4:00-7:00pm on all days of the week. The sound level meter was placed at a height of 1.5m and 2m from the curb. A total of 280 sound level records were conducted over one week. Among twenty noise recording sites, more than 50% of them registered as excessive noisy sites for all types of site categories (health facility, residential, commercial, and mixed areas). For the seven days, average noise recordings were in the range of 66-72 dB at 83% of mixed areas; 33% of health facilities; 25% of residential areas, and 86% of commercial areas. The highest levels of noise pollution were seen at the Bus-station, Buanbuawuha Square, Tekuam, Arada, Ethio General Hospital, Ersha-seble, and Menafesha areas. This study shows that the average noise level measurement within a week exceeded the permissible limits set by Ethiopia and the World Health Organization. It helps for policy development and timely actions against noise pollution and as baseline information for further investigation.

Appraisal of Urban Road Traffic Noise in tier-II City (Surat City), India

Urban road traffic noise pollution has always been identified as a severe problem that affects urban populants. In developing nation, road traffic noise pollution depends on the composition of heterogeneous traffic composition. These traffic compositions contain vehicles, which have different sizes, speeds variations, a different dimension of vehicles. Environmental noise measurements have been carried out during day-time and night-time in different locations of tier-II city of India. The noise levels have been continuously measured over 24 h periods using kimo DB 300 class-2 noise level meter. The data contained in this research paper represent 768 measurement hours. All the information has been used to investigate the time patterns of the noise levels under a wide range of different conditions and to study the relationships between noise levels and traffic in urban areas. Maximum LAeq was observed 73.3 dB(A) at B14 location and the minimum was recorded 65.7 dB(A) at C3 location, which was greater than the central pollution control board (CPCB) prescribed limits during night time. A major reason for the generation of road traffic noise is due to the equal composition of 2-wheeler and 4-wheeler on the arterial road and heavy vehicles were recorded during morning peak and evening peak even though they are prohibited during peak hours.

Modelling and Prediction of Road Transportation Noise Pollution in Some Capital Cities in Eastern Nigeria by Use of Artificial Neural Network

The study attempts to model and predict road transportation noise pollution in five capital cities in Eastern Nigeria. The capital cities are Calabar, Uyo, Umuahia, Owerri and Port Harcourt. Feed-forward neural network (FNN) with negative back-propagation algorithm was used to do this. The software used was NeuroXL. The ability of this software to handle multiple non-linear relationships makes it ideall y suited for this work. The input data used were total road traffic volume, road traffic mix, road traffic noise pollution response data, and distances from road centre-line to measurement points. The output data used was A-weighted energy mean sound level (L A eq). Models based on this negative back- propagation neural network were trained, validated and tested using data collected. The performance of the model was tested by an error measure, root mean square error (RMSE). RMSE is low as expected, ranges from 1.007 - 1.814, showing that the model is good for the prediction of road traffic noise data. The correlation between observed and predicted noise levels (LAeq) was also obtained, and ranges between +0.757 to +0.974, showing that there is no significant difference between observed and predicted noise levels, thereby, proving the model accurate and reliable. Keywords : Artificial neural network, back-propagation, road traffic noise modeling, road traffic noise prediction, NeuroXL software DOI: 10.7176/JEES/11-10-06 Publication date: October 31 st 2021

Long-Term Exposure to Road Traffic Noise and Air Pollution, and Incident Atrial Fibrillation in the Danish Nurse Cohort

Background:Associations between long-term exposure to air pollution and road traffic noise have been established for ischemic heart disease, but findings have been mixed for atrial fibrillation (AF).Objectives:The goal of the study was to examine associations of long-term exposure to road traffic noise and air pollution with AF.Methods:Time-varying Cox regression models were used to estimate associations of 1-, 3-, and 23-y mean road traffic noise and air pollution exposures with AF incidence in 23,528 women enrolled in the Danish Nurse Cohort (age at baseline in 1993 or 1999). AF diagnoses were ascertained via the Danish National Patient Register. Annual mean weighted 24-h average road traffic noise levels () at the nurses’ residences, since 1970, were estimated using the Nord2000 model, and annual mean levels of particulate matter with a diameter () and nitrogen dioxide () were estimated using the DEHM/UBM/AirGIS model.Results:Of 23,528 nurses with no prior AF diagnosis at the cohort baseline, 1,522 developed AF during follow-up. In a fully adjusted model (including ), the estimated risk of AF was 18% higher [hazard ratio (HR); 95% confidence interval (CI): 1.18; 1.02, 1.36] in nurses with residential 3-y mean levels vs. , with similar findings for 1-y mean exposures. A increase in 3-y mean was associated with incident AF before and after adjustment for concurrent exposure to road traffic noise (HR 1.09; 95% CI: 1.00, 1.20 and 1.08; 95% CI: 0.97, 1.19, respectively). Associations with 1-y mean exposures were positive but closer to the null and not significant. Associations with were null for all time periods before and after adjustment for road traffic noise and inverse when adjusted for concurrent .Conclusion:Our analysis of prospective data from a cohort of Danish female nurses followed for up to 14 y provided suggestive evidence of independent associations between incident AF and 1- and 3-y exposures to road traffic noise and . https://doi.org/10.1289/EHP8090

Risk of cardiovascular mortality, stroke and coronary heart mortality associated with aircraft noise around Congonhas airport, S\xe3o Paulo, Brazil: a small-area study

BackgroundNoise pollution is increasingly recognised as a public health hazard, yet limited evidence is available from low- and middle-income countries (LMIC), particularly for specific sources. Here, we investigated the association between day-night average (Ldn) aircraft noise and the risk of death due to cardiovascular disease (CVD), stroke and coronary heart disease (CHD) at small-area level around São Paulo‘s Congonhas airport, Brazil during the period 2011–2016.MethodsWe selected 3259 census tracts across 16 districts partially or entirely exposed to ≥50 dB aircraft noise levels around the Congonhas airport, using pre-modelled 5 dB Ldn noise bands (≤50 dB to > 65 dB). We estimated the average noise exposure per census tract using area-weighting. Age, sex and calendar year-specific death counts for CVD, stroke and CHD were calculated by census tract, according to the residential address at time of death. We fitted Poisson regression models to quantify the risk associated with aircraft noise exposure, adjusting for age, sex, calendar year and area-level covariates including socioeconomic development, ethnicity, smoking and road traffic related noise and air pollution.ResultsAfter accounting for all covariates, areas exposed to the highest levels of noise (> 65 dB) showed a relative risk (RR) for CVD and CHD of 1.06 (95% CI: 0.94; 1.20) and 1.11 (95%CI: 0.96; 1.27), respectively, compared to those exposed to reference noise levels (≤50 dB). The RR for stroke ranged between 1.05 (95%CI: 0.95;1.16) and 0.91 (95%CI: 0.78;1.11) for all the noise levels assessed. We found a statistically significant positive trend for CVD and CHD mortality risk with increasing levels of noise (p = 0.043 and p = 0.005, respectively). No significant linear trend was found for stroke. Risk estimates were generally higher after excluding road traffic density, suggesting that road traffic air and noise pollution are potentially important confounders.ConclusionsThis study provides some evidence that aircraft noise is associated with increased risk of CVD and CHD mortality in a middle-income setting. More research is needed to validate these results in other LMIC settings and to further explore the influence of residual confounding and ecological bias. Remarkably, 60% of the study population living near the Congonhas airport (~ 1.5 million) were exposed to aircraft noise levels > 50 dB, well above those recommended by the WHO (45 dB), highlighting the need for public health interventions.

Spatio-temporal changes of road traffic noise pollution at ecoregional scale

Noise pollution is a pervasive factor that increasingly threatens natural resources and human health worldwide. In particular, large-scale changes in road networks have driven shifts in the acoustic environment of rural landscapes during the past few decades. Using sampling plots from the Spanish Landscape Monitoring System (SISPARES), 16 km2 each, we modelled the spatio-temporal changes in road traffic noise pollution in Ecoregion 1 of Spain (approximately 66,000 km2). We selected a study period that was characterised by significant changes in the size of the road network and the vehicle fleet (i.e. between 1995 and 2014) and used standard and validated acoustic computation methods for environmental noise modelling (i.e. European Directive, 2002/49/EC) within sampling plots. We then applied a multiple linear regression to expand noise modelling throughout the whole of Ecoregion 1. Our results showed that the noise level increased by 1.7 dB(A) in average per decade in approximately 65% of the territory, decreased by 1.3 dB(A) per decade in about 33%, and remained unchanged in 2%. This suggests that road traffic noise pollution levels may not grow homogeneously in large geographical areas, maybe due to the concentration of large fast traffic flows on modern motorways connecting towns. Our research exemplifies how landscape monitoring systems such as cost-effective approaches may play an important role when assessing spatio-temporal patterns and the impact of anthropogenic noise pollution at large geographical scales, and even more so in a global context of constricted resources and limited availability of historical data on traffic and environmental noise monitoring.

Current Status and Problems of Road Traffic Noise and Air Pollution

Current Status and Problems of Road Traffic Noise and Air Pollution

Modelling and analysis of an urban road traffic noise pollution: Case study of an interrupted flow traffic noise at a signalized intersection

This research aimed at modeling interrupted flow traffic noise at a signalized intersection. In this study, a new approach has been used with SPSS and Arc GIS with urban road traffic noise. The measured parameters of noise include noise equivalent level, volume of light vehicles, heavy vehicles and time mean speed. The results showed that the Spearman correlation coefficient between the interface and the level of the noise at morning, evening and total time were 0.241, 0.35 and 0.45. The spearman correlation coefficient between the interface of the buildings and the intersection of all types of vehicles passing through the morning, evening and total time equal to 0.534, 0.328 and 0.409. The results showed that noise level can be predicted through the speed variables of the vehicles, the volume of vehicles passing heavy and the use of land use of buildings. According to the results and the noise maps for the modeling noise of the estimated coefficients from the equation, it can be found that in building highway in urban, closer look on internationally acting Green Building Labels and noise protection is seen as an important part of the social sustainability aspects of a building.

Spatially differentiated profiles for road traffic noise pollution across a state road network

Diverse traffic noise profiles across road networks remain largely undifferentiated using standardised noise indicators. To address this issue, this work derives two supplementary noise indicators for road traffic noise. The first supplementary indicator differentiates the impacts of daytime and night-time noise exposure. The second supplementary indicator differentiates heavy vehicle noise from commuter traffic noise. Both indicators are expressed in terms of hourly contributions of light and heavy vehicle noise associated with standard traffic noise indicators. The Spearman’s rank correlation coefficient is used to evaluate the strength of the relationship between the standard and supplementary noise indicators. Using the supplementary noise indicators, spatial variability of light and heavy vehicle traffic composition across urban and rural parts of a state road network in Australia is presented. The findings add a useful spatial dimension to large scale road traffic noise impact studies to better inform epidemiology and noise management action plans.