Road Traffic Noise Levels Research Articles

Road Traffic Noise Levels Estimations by means of Fully Dynamic and Microscopic Approach Compared to CNOSSOS-EU Model

In the last decades, research has been dedicated to refining models for assessing traffic noise levels, supporting national authorities in addressing associated problems. Progress has shifted from basic statistical models to dynamic ones, typically composed of a Noise Emission Model (NEM) for estimating source sound power levels and a sound propagation model for assessing equivalent noise levels at specific receiver points. These models commonly integrate the average speed of traffic flow as an input variable, placing them in the macroscopic or mesoscopic realm. The CNOSSOS-EU model (reference in Europe) exemplifies this macroscopic approach. However, due to the modular structure of road traffic noise models, a NEM can be coupled with different sound propagation models, facilitating the transition from macroscopic to microscopic assessments. This paper demonstrates the coupling of a NEM with two distinct simplified sound propagation models: CNOSSOS-EU and the sum of Sound Exposure Levels (SEL) models. The latter approach showcases the potential of using individual on-road vehicle speeds as inputs, enabling microscopic noise assessments. Vehicle speed data is extracted from video records, while the goodness of the proposed approaches is tested by comparing the models' estimations with ground truth values recorded on a National Road in Portugal.

Long-term exposure to road traffic noise and acute lower respiratory infections in the Danish Nurse Cohort

BackgroundLong-term exposure to road traffic noise is associated with cardiovascular disease, but the evidence on respiratory diseases is just emerging. We aimed to examine the association between long-term exposure to road traffic noise and the incidence of acute lower respiratory infections (ALRIs) in adults. MethodsWe followed 23,141 female nurses (age ≥ 44 years) from the Danish Nurse Cohort from baseline (1993 or 1999) to their first hospital contact (inpatient, outpatient, or emergency room) for ALRI, death, emigration or the end of 2015. The residential annual mean levels of road traffic noise (Lden) during the follow-up were estimated using the Nord2000 model. We applied time-varying Cox models to estimate the association of 3-year mean exposure to Lden with ALRIs incidence and piecewise analysis to estimate the threshold of Lden. We examined the robustness of the results by adjusting for residential exposure to air pollution, and the effect modification by attained age, socioeconomic status (SES), comorbidity, and lifestyle. ResultsDuring 18.5 years of follow-up, 2,004 nurses developed ALRIs. In a linear model, we detected a statistically significant positive association between Lden and ALRI, with a hazard ratio (HR) of 1.11 (95 % confidence interval (CI): 1.04, 1.17) per 9.2 dB (interquartile range, IQR). We observed non-linear association with a threshold at 57 dB, above which the HR was 1.25 (95 % CI: 1.09, 1.43) per IQR. Further adjustment for PM2.5 reduced the HRs slightly to 1.21 (95 % CI: 1.04, 1.40). The associations were stronger for nurses with asthma, and in those with lowest SES. ConclusionWe present novel findings in support of the association between long-term exposure to road traffic noise and ALRIs, independent of air pollution, suggesting noise as a risk factor for infectious respiratory diseases.

Long-term exposure to air pollution and road traffic noise and incidence of dementia in the Danish Nurse Cohort.

We examined the association of long-term exposure to air pollution and road traffic noise with dementia incidence in the Danish Nurse Cohort. Female nurses were followed for dementia incidence (hospital contact or medication prescription) from 1993/1999 to 2020. Air pollution and road traffic noise levels were estimated at nurses' residences, and their associations with dementia were examined using Cox regression models. Of 25,233 nurses 1409 developed dementia. Particulate matter with a diameter of ≤2.5µm (PM2.5) was associated with dementia incidence, after adjusting for lifestyle, socioeconomic status, and road traffic noise (hazard ratio [95% confidence interval] 1.35 [1.15-1.59] per interquartile range of 2.6µg/m3). There was no association of PM2.5 with dementia in physically active nurses. Association with road traffic noise diminished after adjusting for PM₂.₅ (1.02 [0.93-1.11] per 7.6dB). Long-term exposure to air pollution increases risk of dementia, and physical activity may moderate this risk. Long-term exposure to air pollution was associated with increased risk of dementia among female nurses from the Danish Nurse Cohort. Association of air pollution with dementia was independent of road traffic noise. Association of road traffic noise with dementia diminished after adjusting for air pollution. Physical activity moderated adverse effects of air pollution on dementia.

Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: study protocol of a randomized controlled trial

BackgroundStress is a widespread phenomenon and reality of everyday life, entailing negative consequences for physical and psychological wellbeing. Previous studies have indicated that exposure to greenspaces and nature-based interventions are promising approaches to reducing stress and promoting restoration. However, an increasing percentage of the population lives in urban regions with limited opportunities to spend time in greenspaces. In addition, urban settings typically feature increased levels of noise, which represents a major environmental stressor. Although various studies have compared the effects of exposure to greenspaces versus urban built environments, evidence of the effects of noise in this context is very limited. Psychophysiological benefits of exposure to greenspaces compared to urban built environments reported in earlier studies might be less (or at least not only) due to features of the greenspaces than to additional stressors, such as road traffic noise in the urban built environment. Hence, differences in the effects attributed to greenness in previous studies may also be due to potentially detrimental noise effects in comparison settings. This paper reports the study protocol for a randomized, controlled intervention study comparing the effects of walking in forest versus urban built environments, taking road traffic noise exposure during walks in the respective settings into account.MethodsThe protocol envisages a field study employing a pretest–posttest design to compare the effects of 30-min walks in urban built environments and forests with different road traffic noise levels. Assessments will consist of self-reported measures, physiological data (salivary cortisol and skin conductance), an attention test, and noise, as well as greenness measurements. The outcomes will be restoration, stress, positive and negative affect, attention, rumination, and nature connectedness.DiscussionThe results will inform about the restorative effect of walking in general, of exposure to different types of environments, and to different noise levels in these sites. The study will provide insights into the benefits of walking and nature-based interventions, taking into account the potential detrimental effects of noise exposure. It will thus facilitate a better understanding of low-threshold interventions to prevent stress and foster wellbeing.Trial registrationISRCTN48943261; Registered 23.11.2023.

Urban environment in pregnancy and postpartum depression: An individual participant data meta-analysis of 12 European birth cohorts

BackgroundUrban environmental exposures associate with adult depression, but it is unclear whether they are associated to postpartum depression (PPD). ObjectivesWe investigated associations between urban environment exposures during pregnancy and PPD. MethodsWe included women with singleton deliveries to liveborn children from 12 European birth cohorts (N with minimum one exposure = 30,772, analysis N range 17,686–30,716 depending on exposure; representing 26–46 % of the 66,825 eligible women). We estimated maternal exposure during pregnancy to ambient air pollution with nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10), road traffic noise (Lden), natural spaces (Normalised Difference Vegetation Index; NDVI, proximity to major green or blue spaces) and built environment (population density, facility richness and walkability). Maternal PPD was assessed 3–18 months after birth using self-completed questionnaires. We used adjusted logistic regression models to estimate cohort-specific associations between each exposure and PPD and combined results via meta-analysis using DataSHIELD. ResultsOf the 30,772 women included, 3,078 (10 %) reported having PPD. Exposure to PM10 was associated with slightly increased odds of PPD (adjusted odd ratios (OR) of 1.08 [95 % Confidence Intervals (CI): 0.99, 1.17] per inter quartile range increment of PM10) whilst associations for exposure to NO2 and PM2.5 were close to null. Exposure to high levels of road traffic noise (≥65 dB vs. < 65 dB) was associated with an OR of 1.12 [CI: 0.95, 1.32]. Associations between green spaces and PPD were close to null; whilst proximity to major blue spaces was associated with increased risk of PPD (OR 1.12, 95 %CI: 1.00, 1.26). All associations between built environment and PPD were close to null. Multiple exposure models showed similar results. DiscussionThe study findings suggest that exposure to PM10, road traffic noise and blue spaces in pregnancy may increase PPD risk, however future studies should explore this causally.

Association between residential exposure to road traffic noise and cognitive and motor function outcomes in children and preadolescents

BackgroundExposure to environmental noise is increasing in recent years but most of the previous literature in children has evaluated the effect of aircraft noise exposure at schools on cognition. ObjectiveTo assess whether residential exposure to road traffic noise during pregnancy and childhood is associated with cognitive and motor function in children and preadolescents. MethodsThe study involved 619 participants from the Spanish INMA-Sabadell cohort and 7,115 from the Dutch Generation R Study. We used noise maps to estimate the average day-evening-night road traffic noise levels at each participant’s residential address during pregnancy and childhood periods. Validated tests were administered throughout childhood in both cohorts to assess non-verbal and verbal intelligence, memory, processing speed, attentional function, working memory, cognitive flexibility, risky decision-making, and fine and gross motor function. Linear models, linear mixed models, and negative binomial models were run depending on the outcome in cohort-specific analysis and combined with a random-effects meta-analysis. All models were adjusted for several socioeconomic and lifestyle variables and results corrected for multiple testing. ResultsAverage road traffic noise exposure levels during pregnancy and childhood were 61.3 (SD 6.0) and 61.5 (SD 5.4) dB for the INMA-Sabadell cohort and 54.6 (SD 7.9) and 53.5 (SD 6.5) dB for the Generation R Study, respectively. Road traffic noise exposure during pregnancy and childhood was not related to any of the cognitive and motor function outcomes examined in this study (e.g. −0.92 (95 % CI −2.08; 0.24) and 0.20 (95 % CI −0.96; 1.35) in overall estimates of memory and fine motor function, respectively, when road traffic noise increases by 10 dB during childhood). ConclusionsThese findings suggest that child’s cognitive or motor functions are not affected by residential exposure to road traffic noise. However, more studies evaluating this association at school and home settings as well as noise events are needed.

Comparative study on interpolation method for road traffic noise level behind buildings in Japanese city blocks for noise mapping

In this study, for reducing the computational load in estimating noise levels behind buildings in Japanese city blocks for noise mapping, 29 interpolation methods were examined for estimating road traffic noise maps for city blocks facing flat roads or elevated roads. As a result, when using the same interpolation method for all city blocks, the RBF -radial basis function- interpolation (radial basis function: thin plate) has the highest interpolation accuracy for city blocks facing flat roads, and the same method has also the highest interpolation accuracy for elevated roads regardless of their height. For safe side estimation, the IDW -inverse distance weighting- method (p=3) is the most appropriate.

Road traffic noise monitoring in a Smart City: Sensor and Model-Based approach

This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and speed), supporting or replacing noise sensor networks, and addressing noise pollution concerns. RTNM is composed of two parts: i) the Vehicle Noise Specific Power model (for the assessment of the sound power level – Lw of passenger cars, considering speed and motorization information) coupled with the CNOSSOS model (for the estimation of heavy-duty vehicles’ Lw); and ii) a sound propagation model (to evaluate noise levels at the receiver point). Inputs for the RTNM are retrieved from a radar installed in Aveiro city, Portugal, while the model estimations are validated by comparing them with levels recorded by a noise sensor installed close to the radar. It was found that the resulting noise estimations are robust, with associated mean absolute percentage errors not exceeding 5.8%.

Calculation of Road Traffic Noise, Development of Data, and Spatial Interpolations for Traffic Noise Visualization in Three-dimensional Space

Road traffic noise visualization is vital in three-dimensional (3D) space. Designing noise observation points (NOPs) and the developments of spatial interpolations are key elements for the visualization of traffic noise in 3D. Moreover, calculating road traffic noise levels by means of a standard noise model is vital. This study elaborates on the developments of data and spatial interpolations in 3D noise visualization. In 3D spatial interpolation, the value is interpolated in both horizontal and vertical directions. Eliminating flat triangles is vital in the vertical direction. Inverse distance weighted (IDW), kriging, and triangular irregular network (TIN) are widely used to interpolate noise levels. Because these interpolations directly support the interpolation of three parameters, the developments of spatial interpolations should be applied to interpolate noise levels in 3D. The TIN noise contours are primed to visualize traffic noise levels while IDW and kriging provide irregular contours. Further, this study has identified that the TIN noise contours fit exactly with NOPs in 3D. Moreover, advanced kriging interpolation such as empirical Bayesian kriging (EBK) also provides irregular shape contours and this study develops a comparison for such contours. The 3D kriging in EBK provides a significant approach to interpolate noise in 3D. The 3D kriging voxels show a higher accurate visualization than TIN noise contours.

Automated simulation for household road traffic noise exposure: Application and field evaluation in a high-density city

Road traffic noise is a major nuisance and a risk factor of poor health for urban dwellers. A household's exposure to road traffic noise, especially in a high-density city, can be easily modified by the surrounding built environment, i.e., roads, traffic, buildings, and the topography. This leads to high variation in noise exposure between neighboring households. Existing simulation-based studies were limited in accounting for the variation of noise exposure at the household level, which restricted their applications in public health research involving large numbers of participants. This paper describes a novel simulation-based workflow to assess household traffic noise exposure, which consists of 1) a source model to assess road traffic noise levels, 2) a propagation model to simulate noise propagation in complex three-dimensional urban areas, 3) an automation algorithm to process large quantity of simulation runs and data. The workflow has been evaluated using field studies conducted in Sham Shui Po District, Hong Kong, with reasonably good agreements between simulated and measured data. It was then used to assess road traffic noise exposure for a sample of 6158 households enrolled in the FAMILY Cohort, representing Hong Kong's population dwelling in the dense urban areas. Results showed that 83% of sampled households have been exposed to excessive road traffic noise above the World Health Organization (WHO) standard, or 30% above the local standard. The estimated burden of disease is over 45,000 disability-adjusted life-years (DALYs) from both high annoyance and sleep disturbance for households in Hong Kong. The study has contributed to the methodologies and datasets in evaluating noise exposure in high-density cities, which can further support urban noise mitigation policies and planning and population-based health studies in the next steps.

Long-Term Exposure to Road Traffic Noise and Incident Heart Failure: Evidence From UK Biobank

BackgroundEvidence on road traffic noise and heart failure (HF) is limited, and little is known on the potential mediation roles of acute myocardial infarction (AMI), hypertension, or diabetes. ObjectivesThe purpose of this study was to evaluate the impacts of long-term road traffic noise exposure on the risk of incident HF considering air pollution, and explore the mediations of the previously mentioned diseases. MethodsThis prospective study included 424,767 participants without HF at baseline in UK Biobank. The residential-level noise and air pollution exposure was estimated, and the incident HF was identified through linkages with medical records. Cox proportional hazard models were used to estimate HRs. Furthermore, time-dependent mediation was performed. ResultsDuring a median 12.5 years of follow-up, 12,817 incident HF were ascertained. The HRs were 1.08 (95% CI: 1.00-1.16) per 10 dB[A] increase in weighted average 24-hour road traffic noise level (Lden), and 1.15 (95% CI: 1.02-1.31) for exposure to Lden >65 dB[A] compared with the reference category (Lden ≤55 dB[A]), respectively. Furthermore, the strongest combined effects were found in those with both high exposures to road traffic noise and air pollution including fine particles and nitrogen dioxide. Prior AMI before HF within 2 years’ time interval mediated 12.5% of the association of road traffic noise with HF. ConclusionsMore attention should be paid and a preventive strategy should be considered to alleviate the disease burden of HF related to road traffic noise exposure, especially in participants who survived AMI and developed HF within 2 years.

Modification of green space on the associations between long-term road traffic noise exposure and incident intracerebral hemorrhage: A prospective cohort study

BackgroundIntracerebral hemorrhage (ICH) is a subtype of stroke that would cause high mortality and disability. Environmental factors may play an important role in the incident risk of ICH. Evidence on how long-term road traffic noise exposure affects incident ICH is still scarce, and whether green space has a modification effect is unknown. We conducted a prospective analysis based on UK Biobank to assess the longitudinal association between road traffic noise exposure and incident ICH, and the potential modification of green space. MethodsAlgorithms based on medical records and linkage were utilized to identify ICH incident cases in the UK Biobank. The Common Noise Assessment Methods in Europe noise model was used to calculate the road traffic noise exposure at the residential level. The relationship between weighted average 24-h road traffic noise level (Lden) and incident ICH was assessed using Cox proportional hazard models, and the modification effect of green space was examined using stratified analysis with interaction terms. ResultsOver a median follow-up of 12.5 years, 1 459 incident ICH cases were ascertained in the 402 268 baseline individuals. After adjustment for potential confounders, Lden was significantly related to an elevated risk of incident ICH with a hazard ratio (HR) of 1.14 (95% CI: 1.01, 1.28) for a 10 dB [A] increment. The detrimental influence of Lden on ICH remained stable after adjustment for air pollution. Furthermore, green space modified the association between Lden exposure and incident ICH (Pinteraction = 0.035), and no association was found for higher green space. ConclusionsLong-term residential road traffic noise exposure was associated with an increased risk of ICH, but only for those who live in areas with less green space, indicating that green space may alleviate the negative impacts of road traffic noise exposure on ICH.

The Echoes of Noise: Residential Exposure to Traffic and Risk of Tinnitus.

The Echoes of Noise: Residential Exposure to Traffic and Risk of Tinnitus.

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.

The effects of exposure to road traffic noise at school on central auditory pathway functional connectivity

As the world becomes more urbanized, more people become exposed to traffic and the risks associated with a higher exposure to road traffic noise increase. Excessive exposure to environmental noise could potentially interfere with functional maturation of the auditory brain in developing individuals. The aim of the present study was to assess the association between exposure to annual average road traffic noise (LAeq) in schools and functional connectivity of key elements of the central auditory pathway in schoolchildren. A total of 229 children from 34 representative schools in the city of Barcelona with ages between 8 and 12 years (49.2% girls) were evaluated. LAeq was obtained as the mean of 2-consecutive day measurements inside classrooms before lessons started following standard procedures to obtain an indicator of long-term road traffic noise levels. A region-of-interest functional connectivity Magnetic Resonance Imaging (MRI) approach was adopted. Functional connectivity maps were generated for the inferior colliculus, medial geniculate body of the thalamus and primary auditory cortex as key levels of the central auditory pathway. Road traffic noise in schools was significantly associated with stronger connectivity between the inferior colliculus and a bilateral thalamic region adjacent to the medial geniculate body, and with stronger connectivity between the medial geniculate body and a bilateral brainstem region adjacent to the inferior colliculus. Such a functional connectivity strengthening effect did not extend to the cerebral cortex. The anatomy of the association implicating subcortical relays suggests that prolonged road traffic noise exposure in developing individuals may accelerate maturation in the basic elements of the auditory pathway. Future research is warranted to establish whether such a faster maturation in early pathway levels may ultimately reduce the developing potential in the whole auditory system.

Outdoor residential noise exposure and sleep in preadolescents from two European birth cohorts

ObjectiveTo examine whether outdoor residential exposure to annual average road traffic and multiple (i.e., road traffic, railway, aircraft, industry) noise levels is related with preadolescents' sleep using maternal-reported and wrist-actigraphy data in two European birth cohorts. MethodsThis cross-sectional study used data of 1245 preadolescents from the Dutch Generation R Study and 232 from the Spanish INMA-Sabadell cohort with a mean age of 12.3 years old. We used noise maps to assess average outdoor road traffic and multiple noise levels (day-evening-night noise indicator, LDEN) at each child's residential address for the year before the sleep assessment. Sleep disturbances were reported by mothers through the Sleep Disturbance Scale for Children and objectively recorded using GeneActiv wrist-actigraphy during seven subsequent days. Linear and Poisson regression models adjusted for several potential confounding variables were performed. ResultsThe mean (SD) exposure to road traffic noise was 53.2 dB (7.3) in the Generation R Study and 61.3 dB (5.9) in the INMA-Sabadell cohort. Exposure to road traffic was related with reduced total sleep time and longer wake after sleep onset (e.g. −3.62 min (95%CI -6.87; −0.37) and 6.88 min (95%CI 1.15; 12.61) per an increase of 10 dB in road traffic noise, respectively) collected by wrist-actigraphy. We observed no association between road traffic exposure and maternal-reported sleep disturbances. Results were similar for multiple noise exposure. ConclusionsThese findings indicate that sleep may be compromised for preadolescents living in areas highly exposed to outdoor residential noise. Future studies using longitudinal designs to further explore these associations during the different stages of sleep development across childhood and adolescence are warranted. Also, wrist-actigraphy measurements which provide more accurate information and may be complementary to the parental- and self-reported data should be considered.

How Do Road Traffic Noise and Residential Greenness Correlate with Noise Annoyance and Long-Term Stress? Protocol and Pilot Study for a Large Field Survey with a Cross-Sectional Design.

Urban areas are continuously growing, and densification is a frequent strategy to limit urban expansion. This generally entails a loss of green spaces (GSs) and an increase in noise pollution, which has negative effects on health. Within the research project RESTORE (Restorative potential of green spaces in noise-polluted environments), an extended cross-sectional field study in the city of Zurich, Switzerland, is conducted. The aim is to assess the relationship between noise annoyance and stress (self-perceived and physiological) as well as their association with road traffic noise and GSs. A representative stratified sample of participants from more than 5000 inhabitants will be contacted to complete an online survey. In addition to the self-reported stress identified by the questionnaire, hair cortisol and cortisone probes from a subsample of participants will be obtained to determine physiological stress. Participants are selected according to their dwelling location using a spatial analysis to determine exposure to different road traffic noise levels and access to GSs. Further, characteristics of individuals as well as acoustical and non-acoustical attributes of GSs are accounted for. This paper presents the study protocol and reports the first results of a pilot study to test the feasibility of the protocol.

Factors influencing tyre/road noise under torque

High levels of road traffic noise negatively impact public health in many parts of Europe, especially in cities. The introduction of electric mobility is often seen as one of the best measures to reduce noise exposition in urban environments. Compared to internal combustion engine vehicles (ICEV), there is an increased importance of tyre/road noise for electric vehicles (EV) because of the reduced masking by the powertrain noise. This effect increases further under acceleration. Firstly, it is known that in most cases tyre/road noise is higher under torque than for free rolling. Secondly, in situations which are characterized by increased driving torque, the lack of masking from powertrain noise for EVs is especially evident when compared to ICEVs. The aim of the LIFE E-VIA project is to reduce road traffic noise in cities by providing noise optimized road surfaces and tyres for EVs. Because of the mentioned effects, not only constant speed driving needs to be considered but also accelerated driving. Consequently, within E-VIA noise measurements from an indoor drum and a test track have been used to investigate the impact of different tyre parameters and operating conditions on the change of tyre/road noise under acceleration when compared to free rolling.

Interlaboratory comparison of ISO 23351-1: speech level reduction of furniture ensembles

We examined the effects of both wind turbine noise and road traffic noise on symptoms and diseases close to wind turbines and in a control area. Wind turbine sound levels were between 1739 dB LAeq and met the Finnish regulation (40 dB). Daytime road traffic noise levels were 32.5-63.5 dB. Altogether 676 residents responded to the questionnaire. Higher wind turbine sound level was associated with more likely reporting wind turbine noise annoyance and not with reporting of symptoms or chronic diseases. Higher road traffic sound level was associated with higher probability of road traffic noise annoyance, migraine or headache, dizziness, impaired hearing, pressure in ears, tachycardia or heart palpitations, and heart disease. The health effects of wind farms seem to be limited to noise annoyance in areas where all residents are exposed to sound levels under 40 dB. The study has strong practical relevance since new wind power areas always fulfill this 40 dB regulation in Finland. On the other hand, more than 600.000 inhabitants are exposed to road traffic noise levels above 55 dB in Finland. Because high road traffic noise is associated with elevated prevalence of symptoms and a disease in our study, and several previous international studies have found even more serious health effects of road traffic noise, this issue deserves higher concern in the future.

A machine learning- and compressed sensing-based approach for surrogate modelling in environmental acoustics: towards fast evaluation of building façade road traffic noise levels

State-of-the-art urban road traffic noise propagation simulation methods such as the CNOSSOS-EU framework rely on ray tracing to estimate noise levels at specific locations on façades, so-called receiver points; this method is computationally expensive and its cost increases with the number of receiver points, which limits the spatial accuracy of such simulations in the context of real-time or near-real-time urban noise simulation applications. This contribution aims to investigate the applicability of multiple data-driven methods to the surrogate modelling of traffic noise propagation for fast façade noise calculation as an alternative to these traditional, ray-tracing-based methods. The proposed approach uses compressed sensing to select a small subset of receiver points from which the data set of the entire façade may be reconstructed, associated with a Kriging model and neural networks, used to predict noise levels for these sensors. The prediction performance of each of these steps is evaluated on an academic test case, with two levels of complexity based on the dimensionality of the problem.