Traffic noise-induced changes in wake-propensity measured with the Odds-Ratio Product (ORP)

Nocturnal traffic noise can disrupt sleep and impair physical and mental restoration, but classical sleep scoring techniques may not fully capture subtle yet clinically relevant alterations of sleep induced by noise. We used a validated continuous measure of sleep depth and quality based on automatic analysis of physiologic sleep data, termed Wake Propensity (WP), to investigate temporal changes of sleep in response to nocturnal noise events in 3-s epochs. Seventy-two healthy participants (mean age 40.3 years, range 18-71 years, 40 females, 32 males) slept for 11 nights in a laboratory, during which we measured sleep with polysomnography. In 8 nights, participants were exposed to 40, 80 or 120 road, rail and/or aircraft noise events with maximum noise levels of 45-65 dB L_AS,max during 8-h sleep opportunities. We analyzed sleep macrostructure and event-related change of WP during noise exposure with linear mixed models. Nocturnal traffic noise led to event-related shifts towards wakefulness and less deep, more unstable sleep (increase in WP relative to pre-noise baseline ranging from +29.5% at 45 dB to +38.3% at 65 dB; type III effect p < 0.0001). Sleep depth decreased dynamically with increasing noise level, peaking when L_AS,max was highest. This change in WP was stronger and occurred more quickly for events where the noise onset was more rapid (road and rail) compared to more gradually time-varying noise (aircraft). Sleep depth did not immediately recover to pre-noise WP, leading to decreased sleep stability across the night compared to quiet nights, which was greater with an increasing number of noise events (standardized β = 0.053, p = 0.003). Further, WP was more sensitive to noise than classical arousals. Results demonstrate the usefulness of WP as a measure of the effects of external stimuli on sleep, and show WP is a more sensitive measure of noise-induced sleep disruption than traditional methods of sleep analysis.

Evaluation of noise barriers based on geometries and materials: a review

The acoustical properties of a barrier are highly dependent on the physical properties of the material and the internal structure of the material. The acoustical material can curtail the quality of sound or enhance the dispersion, depending on the application being considered. The efficient acoustic performance of noise barriers possessing different shapes and materials including waste materials is reviewed for field implementation to achieve the low-cost sustainable noise barrier application in the Indian context. The review analysis of research papers demonstrates that the acoustic performance of barriers is dependent on different shapes, materials, and textures as well as onsite geometry. Based on the review study, T-shaped barriers with a soft top surface are found to be efficient at noise attenuation. For transparent barriers, perceived loudness and noise annoyance are assessed lower than that for opaque barriers and utilization of waste materials viz. plastic, rubber, bottom coal ash, etc. gives high noise attenuation along with low-cost efficiency. Noise pollution levels are high from prescribed noise limits in most of the Indian metropolitan cities. The Indian government is working on mitigation strategies of noise pollution as well as abiding laws regarding noise standards for the zone (residential, industrial, commercial, and silences) wise. In contrast, some communities which are adjacent to roads are suffering from high noise levels in the ambience. Therefore, it requires a coherent strategy for long-term measures intended at minimizing exposure of noise hence providing much more comfort to live, work, and shop near high-traffic roads. Noise barriers are highly beneficial in mitigating the emitted noise from the traffic.

Home environment and noise disturbance in a national sample of multi-family buildings in Sweden-associations with medical symptoms

BACKGROUND

Poor acoustic conditions at home can have negative health impact. The aim was to investigate home environment factors and medical symptoms associated with noise disturbance.

METHODS

All adults (≥18 y) registered in selected apartments in Sweden were invited to participate in a questionnaire survey including medical questions and personal factors. Totally 5775 adults participated (response rate 46%). Information on home environment was obtained through an indoor environment questionnaire. Two-level logistic regression models (individual, municipality) were performed to estimate associations.

RESULTS

Totally 11.9% reported noise disturbance in general at home. Noise disturbance from voice/radio/TV/music/similar sounds from neighbours (13.2%), scraping sound/footsteps/thumping from neighbours (16.5%) and road traffic (16.1%) were common. Younger age and smoking were related to more noise disturbance and more medical symptoms. Noise disturbance was related to tiredness, headache and difficulty concentrating (OR = 1.70-8.19). Renting the apartment (OR = 2.53) and living above ground floor (OR = 1.37) were related to more noise disturbance in general. Living in newer buildings (constructed from 1986 to 2005) was related to less noise disturbance in general (OR = 0.40-0.59). A warmer climate (OR = 1.95), higher municipality population density (OR = 1.24), a longer living time (OR = 1.34), construction year (1961-1975) (OR = 2.42), renting (OR = 1.80-2.32), living above ground floor (OR = 1.45) and having a bathroom fan (OR = 1.84) were associated with increased noise disturbance from neighbours. Factors associated with increased noise disturbance from installations or ventilation/fans/heat pumps included a warmer climate, higher municipality population density, construction year (1961-1995), renting and any mechanical ventilation. Higher municipality population density, construction year (especially 1961-1985) and renting were associated with more noise disturbance from traffic (OR = 1.77-3.92). Renting the apartment (OR = 1.73) and living above ground floor (OR = 1.60) were related to more severe traffic noise disturbances. Noise disturbance in general was partly a mediator of the effects of old buildings, renting the apartment and lack of mechanical ventilation on medical symptoms (% of total effect mediated by noise disturbance: 19-44.8%).

CONCLUSIONS

Noise disturbance can be associated medical symptoms. Younger age, smoking, a warmer climate, higher municipality population density and different building factors (e.g. renting the apartment, construction period 1961-1985) can be associated with noise disturbance.

Association of road traffic noise exposure and prevalence of coronary artery disease: A cross-sectional study in North India

Epidemiological studies have established that noise from transportation sources exceeding the safe limits elevates the risk for cardiovascular diseases. The results however have remained heterogeneous. The present study was conducted to investigate the association between road traffic noise exposure and prevalence of coronary artery disease besides sub-group analysis was performed for identifying the most susceptible population. Traffic noise exposure was measured using the L_den metric in both continuous and categorical forms. A cross-sectional study was performed and information about sociodemographic, lifestyle, and health-related factors was collected. Noise level < 60 dB(A) representing the quiet areas was used as the reference group. Univariate and multivariate logistic regressions were performed to estimate the odds for self-reported coronary artery disease concerning road traffic noise after adjusting for confounding variables. The residents living in noisy areas were found to have a 2.25 times higher risk per 5 dB(A) increment in the noise levels (95% CI = 1.38 to 3.67). Males were at a higher risk of CAD (OR = 2.61; 95% CI = 1.84 to 3.72) as compared to females (OR = 2.07; 95% CI = 1.37-3.13). The subgroup analysis revealed that being sensitive to noise, belonging to a higher age group, reporting higher stress levels, and poor sleep quality were associated with higher risk. The study also provides evidence that exposure to noise levels greater than 60 dB(A) is associated with the prevalence of coronary artery disease in adults.

Long-term residential exposure to air pollution is associated with hair cortisol concentration and differential leucocyte count in Flemish adolescent boys

BACKGROUND

Exposure to air pollution and traffic noise are associated with adverse health outcomes in adolescents. Chronic endocrine stress and systemic inflammation have been hypothesized to underlie the adverse health effects. Simultaneous assessment of inflammation and chronic endocrine stress in epidemiological studies is lacking. The aim of the study was to investigate biomarkers of chronic endocrine stress and inflammation in relation to long-term residential exposure to air pollution and traffic noise in adolescents.

METHODS

In Flemish adolescents (14-15 years), we determined hair cortisol concentration (HCC) as a chronic stress biomarker in 3-cm scalp-near hair sections (n = 395), and leucocyte and leucocyte subtype counts (neutrophils, monocytes, lymphocytes) as inflammatory biomarkers in peripheral blood (n = 385). Daily particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂) and black carbon (BC) concentrations were modelled at the residential address and averaged over 3-month and 1-year periods prior to sampling. Residential traffic noise level was estimated and classified in 5 dB intervals. Sex-specific associations between residential exposures and effect biomarkers were studied using linear regression models, adjusted for a priori selected covariates.

RESULTS

In boys, HCC increased with a factor 1.30 (95% CI: 1.10, 1.54) for an increase in 1-year mean NO₂ from the 25th to 75th percentile (p75/p25), after adjustment for age, BMI, personal and neighborhood socioeconomic status. The corresponding estimate for PM₁₀ was 1.24 (95% CI: 1.02, 1.51). Total leucocyte count in boys, adjusted for the aforementioned covariates and recent health complaints, was positively associated with PM_2.5, PM₁₀, NO₂ and BC. In particular, the neutrophil count increased with a factor 1.11 (95% CI: 1.03, 1.19) for a (p75/p25)-factor increase in 1-year mean BC, corresponding estimates for PM_2.5, PM₁₀ and NO₂ were 1.10 (95% CI: 1.01, 1.19), 1.10 (95% CI: 1.01, 1.20) and 1.08 (95% CI: 1.00, 1.16). Lymphocyte count increased with a factor 1.05 (95% CI: 1.01, 1.10) for a (p75/p25)-factor increase in 1-year mean NO₂. Similar results were observed for 3-month mean exposures. Results were robust to adjustment for recent air pollution exposure. In girls, air pollutants were not associated with HCC or differential leucocyte count. Residential traffic noise level was not associated with HCC or leucocyte counts in boys nor girls.

CONCLUSIONS

Long-term residential exposure to air pollutants was positively associated with chronic endocrine stress and inflammation in adolescent boys, not in girls. This study may contribute to a better understanding of the early pathophysiological changes that may underlie adverse health effects of air pollution exposure in adolescents.

Transportation noise and risk for colorectal cancer: a nationwide study covering Denmark

PURPOSE

Few studies have suggested that traffic noise is a risk factor for cancer, but evidence is inconclusive. We aimed to investigate whether road traffic and railway noise are associated with risk of colorectal cancer.

METHODS

We obtained address history for all 3.5 million people above 40 years of age and living in Denmark for the period 1990-2017 and estimated road traffic and railway noise (L_den) at the most and least exposed facades of all addresses as well as air pollution (PM_2.5). During follow-up (2000-2017), 35,881 persons developed colon cancer and 19,755 developed rectal cancer. Information on individual and area-level demographic and socioeconomic variables was collected from Danish registries. We analyzed data using Cox proportional hazards models, including traffic noise as time-varying 10-year average exposure.

RESULTS

Exposure to road traffic noise at the most exposed façade was associated with an incidence rate ratio and 95% confidence interval for proximal colon cancer of 1.018 (0.999-1.038) per 10 dB higher noise. We observed no associations for road traffic noise at the least exposed façade or for railway noise in relation to proximal colon cancer. Also, we found no association between road traffic or railway noise and risk for distal colon cancer or rectal cancer.

CONCLUSION

Traffic noise did not seem associated with higher risk for colorectal cancer, although the suggestion of a slightly higher risk of proximal colon cancer following exposure to road traffic noise warrants further research.

Modelling traffic noise in a wide gradient interval using artificial neural networks

As classical traffic noise prediction models lack a deeper consideration of the impact of the gradient, the characteristics of longitudinal gradients from multiple roads were collected as data in the mountain city of Chongqing county, which was chosen as the entry point, to study the noise characteristics for a wide range of road gradients and to build a traffic noise prediction model based on artificial neural networks (ANNs). The field data consisted of traffic volumes, heavy-vehicle ratios, average vehicle speeds, road gradients, and corresponding equivalent sound pressure levels. An optimal ANN model was determined and compared with two classical models. The results demonstrated that a one-hidden-layer ANN model was suitable for traffic noise prediction in mountain cities and presented better predictive performance than the conventional models. The best-performing ANN model yielded a determination coefficient of 0.9447 and a mean-squared error of 0.2708 dBA. Moreover, this study confirmed that road gradients were significant for constructing traffic noise prediction models.

Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach

A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study's main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (Leq,1h), maximum noise level (L10,1h), and background noise level (L90,1h). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for Leq,1h , L90,1h , and L10,1h respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.

Does exposure to noise pollution influence the incidence and severity of COVID-19?

Research that analyzes the effect of different environmental factors on the impact of COVID-19 focus primarily on meteorological variables such as humidity and temperature or on air pollution variables. However, noise pollution is also a relevant environmental factor that contributes to the worsening of chronic cardiovascular diseases and even diabetes. This study analyzes the role of short-term noise pollution levels on the incidence and severity of cases of COVID-19 in Madrid from February 1 to May 31, 2020. The following variables were used in the study: daily noise levels averaged over 14 days; daily incidence rates, average cumulative incidence over 14 days; hospital admissions, Intensive Care Unit (ICU) admissions and mortality due to COVID-19. We controlled for the effect of the pollutants PM10 and NO2 as well as for variables related to seasonality and autoregressive nature. GLM models with Poisson regressions were carried out using significant variable selection (p < 0.05) to calculate attributable RR. The results of the modeling using a single variable show that the levels of noise (leq24 h) were related to the incidence rate, the rate of hospital admissions, the ICU admissions and the rate of average cumulative incidence over 14 days. These associations presented lags, and the first association was with incidence (lag 7 and lag 10), then with hospital admissions (lag 17) and finally ICU admissions (lag 22). There was no association with deaths due to COVID-19. In the results of the models that included PM10, NO2, Leq24 h and the control variables simultaneously, we observed that only Leq24 h went on to become a part of the models using COVID-19 variables, including the 14-day average cumulative incidence. These results show that noise pollution is an important environmental variable that is relevant in relation to the incidence and severity of COVID-19 in the Province of Madrid.

Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord2000 and TRANEX

Road traffic noise is the most pervasive source of ambient outdoor noise pollution in Europe. Traffic noise prediction models vary in parameterisation and therefore may produce different estimates of noise levels depending on the geographical setting in terms of emissions sources and propagation field. This paper compares three such models: the European standard, Common Noise Assessment Methods for the EU Member States (hereafter, CNOSSOS), Nord2000 and Traffic Noise Exposure (TRANEX) model based on the UK methodology, in terms of their source and propagation characteristics. The tools are also compared by analysing estimated noise (L_Aeq) from CNOSSOS, Nord2000 (2006 version), and TRANEX for more than one hundred test cases (N = 111) covering a variety of source and receiver configurations (e.g. varying source to receiver distance). The main aim of this approach was to investigate the potential pattern in differences between models' performance for certain types of configurations. Discrepancies in performance may thus be linked to the differences in parameterisations of the CNOSSOS, Nord2000, and TRANEX (e.g. handling of diffraction, refraction). In most cases, both CNOSSOS and TRANEX reproduced L_Aeq levels of Nord2000 (2006 version) within three to five dBA (CNOSSOS: 87%, TRANEX: 94%). The differences in L_Aeq levels of CNOSSOS, compared to Nord2000, can be related to several shortcomings of the existing CNOSSOS algorithms (e.g. ground attenuation, multiple diffractions, and mean ground plane). The analyses show that more research is required in order to improve CNOSSOS for its implementation in the EU. In this context, amendments for CNOSSOS proposed by an EU Working Group hold significant potential. Overall, both CNOSSOS and TRANEX produced similar results, with TRANEX reproducing Nord2000 L_Aeq values slightly better than the CNOSSOS. The lack of measured noise data highlights one of the significant limitations of this study and needs to be addressed in future work.

An observation of the impact of CoViD-19 recommendation measures monitored through urban noise levels in central Stockholm, Sweden

Sweden stands out among the other European countries by the degree of restrictive measures taken towards handling the 2019 coronavirus outbreak, associated with the CoViD-19 pandemic. While several governments have imposed a nationwide total or partial lockdown in order to slow down the spread of the virus, the Swedish government has opted for a recommendation-based approach together with a few imposed restrictions. In the present contribution, the impact of this strategy will be observed through the monitored variation of the city noise levels during the associated period. The data used are recorded during a campaign of over a full year of noise level measurements at a building façade situated in a busy urban intersection in central Stockholm, Sweden. The noise level reductions, observed during the period of restrictions, are shown to be comparable to those found for the two most popular public holidays in Sweden with a peak reduction occurring during the first half of April 2020. Contrary to what has been recently discussed in public media, the spread of the virus, the recommendations, and the restrictions imposed during the ongoing pandemic clearly have had a significant effect on the transport and other human-related activities in Stockholm. In this unique investigation, the use of distributed acoustic sensors has thus shown to be a viable solution not only to enforce regulations but also to monitor the effectiveness of their implementation.

Traffic noise prediction model of an Indian road: an increased scenario of vehicles and honking

Noise is considered as an underrated and underemphasized pollutant in contrast to other pollutants of the environment. Due to the non-acute response of health effects, people are not vigilant towards consequences regarding noise pollution. The expansion of the transportation industry is contributing towards the increment in the public and private vehicular volume which causes an increment in noise pollution. For evaluation of respective scenario, the research study has been conducted on one of the minor roads of Nagpur, India; for 2 years, viz., 2012 and 2019. The study concludes an increment of 5-6 dB(A) in noise level, 4-6 times in honking, and 1.7 times in traffic volume. The study confirms increment in sound pressure by 65.9% and 81.9% for the year 2012 and 2019 during morning and evening sessions, respectively. Noise prediction model has also been developed for the abovementioned years, using multiple regression analysis, considering traffic volume, honking, and speed against noise equivalent level. Honking has been further characterized into honk by light and medium category vehicles as acoustical properties of horns vary with respect to category of vehicle and introduced into the noise prediction model. Noise prediction model for 2019 has predicted the noise level in a range of - 1.7 to + 1.4 dB (Leq) with 84% of observations in the range of - 1 to + 1 dB (Leq), when compared with observed Leq on the field. For proper management of noise pollution, a noise prediction model is essentially needed so that the noise level can be anticipated, and accordingly, measures can be outlined and executed. This increased noise level has serious impacts on human hearing capacity and overall health. Accordingly, noise mitigation preventive measures are recommended to control traffic noise in the urban environment.

The association between road traffic noise and myocardial infarction: A systematic review and meta-analysis

This systematic review and meta-analysis study aimed to investigate the association between exposure to road traffic noise (RTN) and myocardial infarction (MI). Of 681 studies found by searching in databases, including Scopus, Web of Science, Embase, and PubMed on November 29, 2019, the number of 13 studies, including seven cohort, five case-control, and one cross-sectional studies with 1,626,910 participants and 45,713 cases of MI was included. The pooled relative risk (RR) and 95% confidence interval (CI) of MI were calculated using a random-effect model across studies. Heterogeneity measures by reporting the I-square index. Subgroup analysis according to the designs and sensitivity analysis based on the Jackknife approach was performed. We observed in the eight studies the association was investigated in different noise exposure groups and in the 10 studies (including two conference papers) the risk of MI was provided per specific unit increment of RTN. We ran two independent types of meta-analyses involving a categorical analysis (comparing the highest and the lowest category of noise exposure groups) and an exposure-response analysis (the risk of MI per 10-dB increment of RTN). The pooled RR (95% CI) of MI for the categorical and exposure-response meta-analyses was calculated 1.03 (0.93, 1.13) and 1.02 (1.00, 1.05), respectively. For both types of meta-analyses, subgroup analysis indicates a significant association in the studies with case-control and cross-sectional designs but not cohort studies. For the exposure-response meta-analysis, a significantly greater risk of MI was observed after excluding the two conference papers (RR = 1.03 and 95% CI = 1.00, 1.05) and by further excluding the studies provided originally the risk of MI only for the categorical analysis (RR = 1.02 and 95% CI = 1.01, 1.03). We did not show a significant publication bias across studies. In conclusion, our study suggests a significant odds of association between exposure to RTN and the risk of MI.

[Adverse health effects due to environmental noise exposure in urban areas]

Environmental noise is a great burden for the population in Germany, especially in urban areas. People are often exposed not only to one but several noise sources. Long-term exposure to environmental noise can have several and severe adverse effects on human health, such as noise annoyance, sleep disturbances, ischaemic heart disease and depression. Additional burdens arise from multiple exposure to particulate matter or air pollutants. The current article describes the relevant adverse health effects due to long-term environmental noise exposure, discusses the challenges of environmental noise in urban areas different and introduces measures and instruments to abate environmental noise.

The spatial relationship between traffic-related air pollution and noise in two Danish cities: Implications for health-related studies

Air pollution and noise originating from urban road traffic have been linked to the adverse health effects e.g. cardiovascular disease (CVD), although their generation and propagation mechanisms vary. We aimed to (i) develop a tool to model exposures to air pollution and noise using harmonized inputs based on similar geographical structure (ii) explore the relationship (using Spearman's rank correlation) of both pollutions at residential exposure level (iii) investigate the influence of traffic speed and Annual Average Daily Traffic (AADT) on air-noise relationship. The annual average (2005) air pollution (NO_x, NO₂, PM₁₀, PM_2.5) and noise levels (L_day, L_eve, L_night, L_den, L_Aeq,24h) are modelled at address locations in Copenhagen and Roskilde (N = 11,000 and 1500). The new AirGIS system together with the Operational Street Pollution Model (OSPM®) is used to produce air pollution estimates. Whereas, noise is estimated using Common Noise Assessment Methods in the EU (CNOSSOS-EU, hereafter CNOSSOS) with relatively coarser inputs (100 m CORINE land cover, simplified vehicle composition). In addition, noise estimates (L_day, L_eve, L_night) from CNOSSOS are also compared with noise estimates from Road Traffic Noise 1996 (RTN-96, one of the Nordic noise prediction standards). The overall air-noise correlation structure varied significantly in the range |r_S| = 0.01-0.42, which was mainly affected by the background concentrations of air pollution as well as non-traffic emission sources. Moreover, neither AADT nor traffic speed showed substantial influence on the air-noise relationship. The noise levels estimated by CNOSSOS were substantially lower, and showed much lower variation than levels obtained by RTN-96. CNOSSOS, therefore, needs to be further evaluated using more detailed inputs (e.g. 10 m land cover polygons) to assess its feasibility for epidemiological noise exposure studies in Denmark. Lower to moderate air-noise correlations point towards significant potential to determine the independent health effects of air pollution and noise.

Effects of adding natural sounds to urban noises on the perceived loudness of noise and soundscape quality

Introducing pleasant natural sounds to mask urban noises is an important soundscape design strategy to improve acoustic comfort. This study investigates the effects of signal-to-noise ratio (SNR) between natural sounds (signal) and the target noises (noise) and their temporal characteristics on the perceived loudness of noise (PLN) and overall soundscape quality (OSQ) through a laboratory experiment. Two types of urban noise sources (hydraulic breaker and traffic noises) were set to A-weighted equivalent sound pressure levels (SPL) of 55, 65, and 75 dB and then augmented with two types of natural sounds (birdsong and stream), across a range of SNRs. Each acoustic stimulus was a combination of noise and natural sound at SNRs from -6 to 6 dB. Averaged across all cases, the subjective assessment of PLN showed that augmenting urban noise separately with the two natural sounds reduced the PLN by 17.9%, with no significant differences found between the birdsong and stream sounds. Adding natural sounds increased the OSQ by on average 18.3% across the cases, but their effects gradually decreased as the noise level increased. The OSQ of the birdsong and stream sounds were similar for traffic noise, whereas the stream sound was rated higher than the birdsong for the breaker noise. The results suggest that increasing the dissimilarity in temporal structure between the target noise and natural sounds could enhance the soundscape quality. Appropriate SNRs were explored considering both PLN and OSQ. The results showed that the SNR of -6 dB was desirable when the A-weighted SPL of the noise rose to 75 dB.

Short-term effects of traffic noise on suicides and emergency hospital admissions due to anxiety and depression in Madrid (Spain)

Studies show a relationship between some mental illnesses and suicides and different environmental variables such as air pollution, characterized by stress at the neuropsychological level. Despite the fact that traffic noise is also a powerful neurological stressor, studies that relate traffic noise to these mental disorders are practically non-existent. The objective is to analyze the short-term impact that chemical air pollution, traffic noise and thermal extremes have on emergency hospital admissions due to anxiety, dementia and suicides in the city of Madrid. This ecological, longitudinal study uses generalized linear models with Poisson link to analyze the short-term impact of the average daily concentrations of chemical pollutants (NO₂, PM₁₀, PM_2.5, O₃), noise pollution indicators (Leq_day, Leq_night and Leq_24h) and temperatures during heat waves (T_heat) and cold waves (T_cold) on daily admissions to emergency services in the city of Madrid from 2010 to 2013 due to anxiety (ICD-10: F32), depression (ICD-10: F40-F42) and suicide (ICD-10: X60-X84). The results show no association between any of the chemical pollutants considered and the dependent variables studied. On the contrary, the values of Leq_day are associated with the three variables analyzed in lag 0 for the cases of anxiety and depression and in lag 1 for suicides, with RR: 1.20 (IC95% 1.14 1.26), RR: 1.11 (IC95% 1.06 1.16) and RR: 1.17 (IC95% 1.05 1.30), respectively, for increases of 1 dB(A) in the values of Leq_day. An association was also found between T_cold and admissions for anxiety in lag 9 with RR: 1.62 (IC95% 1.18 2.22) for increases of 1 °C in the values of T_cold. Traffic noise can be considered an important risk factor related to the illnesses and anxiety and depression and for suicides in the city of Madrid, although new studies are needed to support the findings shown here.

High-resolution assessment of road traffic noise exposure in Denmark

Recent studies show associations between transportation noise and various diseases. However, selection bias remains an inherent limitation in many cohort studies. In this study, we aimed to model road traffic noise exposure across the entire Danish population and investigate its distribution in relation to area-level socioeconomic indicators and green space. Based on the Nordic prediction method, we estimated road traffic noise for all Danish residential addresses, in total 2,761,739 addresses, for the years 1995, 2000, 2005, 2010, and 2015 at the most and least exposed façades. Area-level sociodemographic variables encompassing education, income, and unemployment were collected and residential green within a 150 m radius buffer at the address level was estimated using high-resolution national land use classification data. Median levels of noise at both the most and least exposed facades across Denmark increased slightly from 1995 to 2015. Correlations between most and least exposed façades varied based on population density and building type, with the highest correlations between the most and least exposed façades found for semidetached homes and lowest for multistory buildings. Increasing median noise levels were observed across increasing levels of higher education, lower income, and higher unemployment. A decreasing trend in median noise levels with increasing levels of green space was observed. In conclusion, we showed that it is feasible to estimate nationwide, address-specific exposure over a long time-period. Furthermore, the low correlations found between most and least exposed façade for multistory buildings, which characterize metropolitan centers, suggests that the most exposed façade estimation used in most previous studies and predicts exposure at the silent façade relatively poorly.

Transportation noise impairs cardiovascular function without altering sleep: The importance of autonomic arousals

AIMS

Chronic exposure to nocturnal transportation noise has been linked to cardiovascular disorders with sleep impairment as the main mediator. Here we examined whether nocturnal transportation noise affects the main stress pathways, and whether it relates to changes in the macro and micro structure of sleep.

METHODS AND RESULTS

Twenty-six young healthy participants (12 women, 24.6 ± 0.7 years, mean ± SE) spent five consecutive 24-h days and one last morning in the laboratory. The first (baseline) and last (recovery) nights comprised a quiet ambient scenario. In-between, four different noise scenarios (low/medium/high intermittent road or rail scenarios with an identical equivalent continuous sound level of 45 dB) were randomly presented during the 8-h nights. Participants felt more annoyed from the transportation noise scenarios compared to the quiet ambient scenario played back during the baseline and recovery nights (F_5,117 = 10.2, p < 0.001). Nocturnal transportation noise did not significantly impact polysomnographically assessed sleep macrostructure, blood pressure, nocturnal catecholamine levels and morning cytokine levels. Evening cortisol levels increased after sleeping with highly intermittent road noise compared to baseline (p = 0.002, noise effect: F_4,83 = 4.0, p = 0.005), a result related to increased cumulative duration of autonomic arousals during the noise nights (F_5,106 = 3.4, p < 0.001; correlation: r_pearson = 0.64, p = 0.006).

CONCLUSION

Under controlled laboratory conditions, highly intermittent nocturnal road noise exposure at 45 dB increased the cumulative duration of autonomic arousals during sleep and next-day evening cortisol levels. Our results indicate that, without impairing sleep macrostructure, nocturnal transportation noise of 45 dB is a physiological stressor that affects the hypothalamic-pituitary-adrenal axis during the following day in healthy young good sleepers.

Artificial intelligence based ensemble model for prediction of vehicular traffic noise

Vehicular traffic noise is the main source of noise pollution in major cities around the globe. A reliable and accurate method for the estimation of vehicular traffic noise is therefore essential for creating a healthy noise-free environment. In this study, 2 linear (simple average and weighted average) and 2-nonlinear (neural network and neuro-fuzzy) ensemble models were developed by combining the outputs of three Artificial Intelligence (AI) based non-linear models; Adaptive Neuro Fuzzy Inference System (ANFIS), Feed Forward Neural Network (FFNN), Support Vector Regression (SVR) and one Multilinear regression (MLR) model to enhance the performance of the single black box models in predicting vehicular traffic noise of Nicosia city, North Cyprus. In this way, first a nonlinear sensitivity analysis was applied to select the most relevant and dominant input parameters of the traffic data obtained from 12 observation points in the study area. The most dominant parameters in order of their importance were determined to be number of cars, number of van/pickups, number of trucks, average speed and number of buses. Classifying the number of vehicles into five categories before feeding the traffic data into the AI models was observed to improve performance of the single models up to 29% in the verification phase. Out of the four ensembles models developed, the nonlinear ANFIS ensemble was found to be the most robust by improving the performance of ANFIS, FFNN, SVR and MLR models in the verification stage by 11%, 19%, 21% and 31%, respectively.

Surrounding green, air pollution, traffic noise exposure and non-accidental and cause-specific mortality

BACKGROUND

Most previous studies that investigated associations of surrounding green, air pollution or traffic noise with mortality focused on single exposures.

OBJECTIVES

The aim of this study was to evaluate combined associations of long-term residential exposure to surrounding green, air pollution and traffic noise with total non-accidental and cause-specific mortality.

METHODS

We linked a national health survey (Public Health Monitor, PHM) conducted in 2012 to the Dutch longitudinal mortality database. Subjects of the survey who were 30 years or older on 1 January 2013 (n = 339,633) were followed from 1 January 2013 till 31 December 2017. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 m and 1000 m), annual average air pollutant concentrations (including particulate matter (PM₁₀, PM_2.5), nitrogen dioxide (NO₂)) and traffic noise with non-accidental, circulatory disease, respiratory disease, lung cancer and neurodegenerative disease mortality.

RESULTS

We observed 26,886 non-accidental deaths over 1.627.365 person-years of follow-up. Surrounding green, air pollution and traffic noise exposure were not significantly associated with non-accidental or cause-specific mortality. For non-accidental mortality, we found a hazard ratio (HR) of 0.99 (0.98, 1.01) per IQR increase in NDVI 300 m, a HR of 0.99 (95% CI: 0.97, 1.01) per IQR increase in NO₂, a HR of 0.98 (0.97, 1.00) per IQR increase in PM_2.5 and a HR of 0.99 (95% CI: 0.97, 1.01) per IQR increase in road-traffic noise. Analyses restricted to non-movers or excluding subjects aged 85+ years did not change the findings.

CONCLUSION

We found no evidence for associations of long-term residential exposures to surrounding green, air pollution and traffic noise with non-accidental or cause-specific mortality in a large population based survey in the Netherlands, possibly related to the relatively short follow-up period.

An innovative approach of urban noise monitoring using cycle in Nagpur, India

Noise is one of the most significant sources of environmental pollution in metro cities. Traffic noise plays an important role to contribute to ambient noise pollution. Noise pollution monitoring was carried out using sound level meter mounted on the cycle with the help of mould fixed on it. Cycle is a noiseless mode of transport and therefore, it was used for noise monitoring over 700 locations in Nagpur City. Noise levels were recorded at each location for 5 min during peak rush hours of traffic in the morning and in the evening at an interval of 1 s leading to around 300 readings each. Noise indices like L_eq, L₉₀, L₁₀, L_max and L_min were calculated for noise level data analysis. TNI was also determined for analysis of traffic noise on intersections of roads. Equivalent noise levels were transferred into the Geographic Information System (GIS) and spatial analysis tool was used to create spatial and strategic noise maps. Based on the maps, it is revealed that noise levels are exceeded all over the city with respect to ambient noise standards. It was experienced that intersections of roads during peak rush time are very noisy due to honking. The study recommends the noise monitoring through cycle-mounted sound level meter since it covers unapproachable sites, more coverage in the city that helps in identifying hotspot area of concern for noise abatement strategies and planning.

Residential surrounding green, air pollution, traffic noise and self-perceived general health

Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental exposures on SGH. Our aim was to evaluate associations of combined residential exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM₁₀, PM_2.5), and nitrogen dioxide (NO₂)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined exposures with poor SGH. In single-exposure models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO₂ was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-exposure models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-exposure models. Studies including only one of these correlated exposures may overestimate the risk of poor SGH attributed to the studied exposure, while underestimating the risk of combined exposures.

A nuanced approach to the Environmental Noise Guidelines for the European Region related to traffic noise

The recently published guide "Environmental Noise Guidelines for the European Region" provides evidence of the non-auditory effects of environmental noise on population health. This guide reflects the current state of knowledge about how traffic noise affects cardiovascular diseases among others. Despite the valuable contributions of this document in terms of current knowledge, there are different points of improvement in terms of implementation of the guide for all Europe. The studies related to Southern Europe have little presence, probably due to the lack of research in this issue, it means, there are few studies that evaluate short-term effects of noise on morbid-mortality. In this sense, it would be advisable to include the most recently published papers on the subject. Due to the aforementioned, the noise exposure assessment should be considerate at the short and long term to evaluate completely the noise effects on health, specially taking into account the characteristics of countries in which leisure-related noise is especially problematic.

Nocturnal noise and habitat homogeneity limit species richness of owls in an urban environment

Habitat loss and fragmentation are listed among the most significant effects of urbanization, which is regarded as an important threat to wildlife. Owls are the top predators in most terrestrial habitats, and their presence is a reliable indicator of ecosystem quality and complexity. However, influence of urbanization on owl communities, anthropogenic noise in particular, has not been investigated so far. The aim of this study was to identify the role of noise and landcover heterogeneity in the species richness of owl assemblage in the urban ecosystem. Owls were surveyed in the city of Kraków (southern Poland) on 65 randomly selected sample plots (1 km²). The area of main landcover types, landcover diversity index, mean size of landcover patch, and nocturnal noise level were defined within the sample plots and correlated with owl species richness. Five owl species were recorded in the study area with forests as the dominant landcover type for Tawny and Ural owls, grasslands for Long-eared and Barn owls, and gardens for Little owls. In total, 52% of sample plots were occupied by at least one species (1-3 species per plot). The number of owl species was positively correlated with landcover diversity index and negatively correlated with nocturnal noise emission. This study demonstrates that species richness of owls in urban areas may be shaped by landcover heterogeneity and limited by noise intensity. This indicates that noise changes top predator assemblage, which in consequence may disturb predator-prey interactions within human-transformed habitats.