Real noise from the urban environment: how ambient community noise affects health and what can be done about it

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Effects of postoperative environmental noise on surgery induced pain: Evidence based on a prospective observational study

CONTEXT

Many patients recovering from surgery in wards are disturbed by environmental noise. However, the effects of environmental noise on postoperative pain are unclear.

OBJECTIVES

This study aimed to assess the association between postoperative noise and pain.

METHODS

This prospective study included 182 women who underwent cesarean sections. Postoperative noise was continuously recorded, and pain intensity at rest was assessed using a numerical rating scale (NRS) for 0-6, 6-12, 12-18, and 18-24 h after the patients were returned to the ward. Cumulative pain scores were calculated by summing the NRS scores at each time point and comprised the primary outcome. The maximum pain NRS score and analgesic consumption during the 24 h after surgery were also recorded.

RESULTS

Mean environmental noise intensity during the daytime was an independent factor for cumulative pain scores, maximum pain scores, and analgesic use during the first postoperative 24 h (β, 0.37; 95% CI, 0.21-0.53 and β, 0.12; 95% CI, 0.07-0.17; P < 0.001 for both; β, 0.86; 95% CI, 0.25-1.46; P = 0.006). Cumulative and maximum NRS pain scores as well as the incidence of NRS ≥ 4 were significantly higher in patients under mean daytime environmental noise of ≥58, than <58 decibels (dB) (8.0 [6.0-11.3] vs. 6.0 (5.0-7.0); 3.0 [2.0-4.0] vs. 2.0 [2.0-2.0, and 25.6% vs. 11.0%; RR, 2.32; 95% CI, 1.19-4.54, respectively; P < 0.001 for all).

CONCLUSIONS

Higher-level postoperative noise exposure was associated with more severe postoperative pain and increased analgesic needs, as well as a higher incidence of moderate-to-severe pain in patients recovering from cesarean delivery. Our findings indicate that reducing environmental ward noise might benefit for postoperative pain management.

Feature selection for a continental-scale geospatial model of environmental sound levels

Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett. 1(12), 122401 (2021)]. In this paper, the downselection process, which is based on factors such as data quality and inter-feature correlations, is described in further detail. To investigate additional dimensionality reduction, four different feature selection methods are applied to the 51 layers. Leave-one-out median absolute deviation cross-validation errors suggest that the number of geospatial features can be reduced to 15 without significant degradation of the model's predictive error. However, ensemble predictions demonstrate that feature selection results are sensitive to variations in details of the problem formulation and, therefore, should elicit some skepticism. These results suggest that more sophisticated dimensionality reduction techniques are necessary for problems with limited training data and different training and testing distributions.

Deep Learning-Based Road Traffic Noise Annoyance Assessment

With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students' data has some limitations, it is still a useful attempt to apply deep learning to noise assessment.

Evolution of road infrastructure in large urban areas

Most cities in the United States and around the world were organized around car traffic. In particular, large structures such as urban freeways or ring roads were built for reducing car traffic congestion. With the evolution of public transportation and working conditions, the future of these structures and the organization of large urban areas is uncertain. Here we analyze empirical data for U.S. urban areas and show that they display two transitions at different thresholds. For the first threshold of order T_{c}^{FW}∼10^{4} commuters, we observe the emergence of a urban freeway. The second threshold is larger and on the order T_{c}^{RR}∼10^{5} commuters above which a ring road emerges. In order to understand these empirical results, we propose a simple model based on a cost-benefit analysis which relies on the balance between construction and maintenance costs of infrastructures and the trip duration decrease (including the effect of congestion). This model indeed predicts such transitions and allows us to compute explicitly the commuter thresholds in terms of critical parameters such as the average value of time, average capacity of roads, and typical construction cost. Furthermore, this analysis allows us to discuss possible scenarios for the future evolution of these structures. In particular, we show that because of the externalities associated with freeways (pollution, health costs, etc.), it might become economically justified to remove urban freeways. This type of information is particularly useful at a time when many cities are confronted with the dilemma of renovating these aging structures or converting them into other uses.

Environmental Noise Exposure and Mental Health: Evidence From a Population-Based Longitudinal Study

INTRODUCTION

Exposure to environmental noise from within homes has been associated with poor mental health. Existing evidence rests on cross-sectional studies prone to residual confounding, reverse causation, and small sample sizes, failing to adequately consider the causal nature of this relationship. Furthermore, few studies have examined the sociodemographic distribution of noise exposure at a country level.

METHODS

The study, conducted in 2021, examined the impact of environmental noise from road traffic, airplanes, trains, and industry on mental health and psychological distress as reported by 31,387 respondents using a 19-year longitudinal data set in Australia (2001‒2019). To improve the capacity to make causal inference and reduce bias from measurement error, reverse causation, and unobserved confounders, analyses used instrumental variables, fixed-effects models, and an aggregated area-level measure of noise exposure. Utilizing the large-scale national data set, sociospatial distributions of noise exposure were described.

RESULTS

Private and public rental tenants, lone parents, residents of socioeconomically disadvantaged areas, and those with long-term health conditions were more likely to report residential noise exposure. This exposure to noise was consistently associated with poorer mental health (self-reported noise: β= -0.58; 95% CI= -0.76, -0.39; area-level noise: β= -0.43; 95% CI= -0.61, -0.26), with the relationship strongest for traffic noise (β= -0.79; 95% CI= -1.07, -0.51). Notably, when noise exposure decreased over time, there was an increase in mental health (β= 0.43; 95% CI= 0.14, 0.72).

CONCLUSIONS

The study provides strong evidence of a negative mental health effect of perceived residential noise, and the results have implications for healthy home design and urban planning. These findings should be validated with further studies that measure noise intensity and housing quality.

A Study on Transformation of Housing Typology and Its Environmental and Social Effects on the Living Conditions of Residents in Planned Residential Neighborhoods of Kabul City

This study examines the transformation in housing typology from low-rise to apartment buildings in the formal neighborhood of Kabul city. These formal neighborhoods were developed according to plans from 1978. The majority of these neighborhoods were designed with detached houses that had courtyards. Literature reviews, field visits, opinions of residents, and a planning organization provided data for this study. In this study, the transformation of housing in planned neighborhoods is analyzed in relation to their social and environmental impacts. Researchers determined how varying housing typologies affected residents’ health and quality of life in these planned neighborhoods. Initially, we assessed the physical characteristics of the study area and evaluated how much transformation volume is present in the study area. Second, we examined residents’ views of residential development and its impacts, as well as their daily lives. In order to identify the relationship between these two aspects, the study examined the characteristics of the area (variables) from the perspectives of privacy, natural light, shading, sound pollution, air pollution, and energy use. We used several criteria to evaluate the accuracy of the physical characteristics and the respondents’ opinions. Lastly, we provided some recommendations and solutions to improve the current situation.

Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors

Many people living in urban environments nowadays are overexposed to noise, which results in adverse effects on their health. Thus, urban sound monitoring has emerged as a powerful tool that might enable public administrations to automatically identify and quantify noise pollution. Therefore, identifying multiple and simultaneous acoustic sources in these environments in a reliable and cost-effective way has emerged as a hot research topic. The purpose of this paper is to propose a two-stage classifier able to identify, in real time, a set of up to 21 urban acoustic events that may occur simultaneously (i.e., multilabel), taking advantage of physical redundancy in acoustic sensors from a wireless acoustic sensors network. The first stage of the proposed system consists of a multilabel deep neural network that makes a classification for each 4-s window. The second stage intelligently aggregates the classification results from the first stage of four neighboring nodes to determine the final classification result. Conducted experiments with real-world data and up to three different computing devices show that the system is able to provide classification results in less than 1 s and that it has good performance when classifying the most common events from the dataset. The results of this research may help civic organisations to obtain actionable noise monitoring information from automatic systems.

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.

Temporal and seasonal variations of noise pollution in urban zones: a case study in Pakistan

Noise pollution is widely recognized as an important problem and can negatively affect quality of life. This study aimed to examine the temporal and seasonal variations of noise pollution in urban zones of Peshawar, Pakistan. This city is increasingly becoming congested and traffic-related problems are common. Noise levels were assessed in four different seasons at 20 points around the city, including three different zones: commercial, residential, and silent. All the noise indices including equivalent noise level, day and night time noise level, noise climate, and noise pollution level were calculated for all zones. In winter, the Leq values ranged between 52.5 and 73.3 dBA; while in spring, summer, and autumn, it ranged between 56.2 and 88.3 dBA; 46.9 and 88.6 dBA; and 49.2 and 76.6 dBA, respectively. The level of the noise was observed highest in commercial followed by residential and the silent zones. The levels of the noise were beyond the permissible limits in some zones mentioned in the Pakistan National Environmental Quality Standards (Pak-NEQS' 2010). The seasonal variation in Leq revealed that the noise level in 70% of areas increased from winter to spring, 45% from spring to summer, 35% summer to autumn, 30% autumn to winter, 70% winter to summer, and 40% spring to autumn. Temperature, humidity, and wind speed were the main seasonal factors causing the seasonal variations and traffic was the main source of noise pollution identified in the area.

Modes of Transport to School and Their Associations with Weight Status: A Cross-Sectional Survey of Students in Shanghai, China

Background: Over the past two decades, both transport modes as well as overweight/obesity have changed dramatically among students in China, but their relationships are not clear. This study aimed to investigate modes of transport to school and their associations with the weight status of Chinese students. Methods: A cross-sectional study was conducted with non-resident students aged 6 to 17 years from all 16 districts across Shanghai, China in October and November 2019. Information about sociodemographic characteristics and the models of travel to school among students was investigated using an online, self-administered, structured questionnaire (or those assisted by their parents). Weight and height were measured by school health workers, and the Chinese standard age adjusted BMI (weight/height²) was used to classify students’ weight status. Cumulative logistic regression modelling was used to examine the relationships. Results: The main mode of transport to school was an active mode (46.5%, defined as walking, bicycling, or public transport), followed by an inactive mode of transport (30.5%, defined as a car or bicycle as a passenger), and a combination of both modes (23%). About one-third of the students were overweight or obese and 5% were underweight. No statistically significant association between transport modes and weight status was found in this study. Conclusions: In Shanghai, close to one-third of children travel to school by an inactive mode of transport. The findings of this study did not support the notion that an active mode to school could be beneficial for preventing overweight/obesity in students in China.

Residential exposure to air pollution and access to neighborhood greenspace in relation to hair cortisol concentrations during the second and third trimester of pregnancy

BACKGROUND

Exposure to air pollution during pregnancy has been associated with adverse pregnancy outcomes in studies worldwide, other studies have described beneficial effects of residential greenspace on pregnancy outcomes. The biological mechanisms that underlie these associations are incompletely understood. A biological stress response, which implies release of cortisol, may underlie associations of air pollution exposure and access to neighborhood greenspaces with health.

METHODS

We explored residential exposure to air pollution and residential access to neighborhood greenspaces in relation to hair cortisol concentrations of participants in a prospective pregnancy cohort study in Flanders, Belgium. Hair samples were collected at the end of the second pregnancy trimester (n = 133) and shortly after delivery (n = 81). Cortisol concentrations were measured in 3-cm scalp-near hair sections, to reflect second and third pregnancy trimester cortisol secretion. We estimated long-term (3 months before sampling) residential exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and black carbon (BC), assessed residential distance to major roads and residential access to neighborhood greenspaces (NHGS). Associations between residential exposures and hair cortisol concentrations were studied using linear regression models while adjusting for season of sampling.

RESULTS

Three-month mean residential NO₂ and BC concentrations were positively associated with third pregnancy trimester hair cortisol concentrations (p = 0.008 and p = 0.017). Access to a large NHGS (10 ha or more within 800 m from residence) was negatively associated with third trimester hair cortisol concentrations (p = 0.019). Access to a large NHGS significantly moderated the association between residential proximity to major roads and second trimester hair cortisol concentrations (p = 0.021). Residential distance to major roads was negatively associated with second trimester hair cortisol concentrations of participants without access to a large NHGS (p = 0.003). The association was not significant for participants with access to a large NHGS. The moderation tended towards significance in the third pregnancy trimester (p < 0.10).

CONCLUSIONS

Our findings suggest a positive association between long-term residential exposure to air pollution and biological stress during pregnancy, residential access to neighborhood greenspaces may moderate the association. Further research is needed to confirm our results.

TRIAL REGISTRATION

The IPANEMA study is registered under number  NCT02592005 at clinicaltrials.gov .

Environmental noise degrades hippocampus-related learning and memory

The neural mechanisms underlying the impacts of noise on nonauditory function, particularly learning and memory, remain largely unknown. Here, we demonstrate that rats exposed postnatally (between postnatal days 9 and 56) to structured noise delivered at a sound pressure level of ∼65 dB displayed significantly degraded hippocampus-related learning and memory abilities. Noise exposure also suppressed the induction of hippocampal long-term potentiation (LTP). In parallel, the total or phosphorylated levels of certain LTP-related key signaling molecules in the synapses of the hippocampus were down-regulated. However, no significant changes in stress-related processes were found for the noise-exposed rats. These results in a rodent model indicate that even moderate-level noise with little effect on stress status can substantially impair hippocampus-related learning and memory by altering the plasticity of synaptic transmission. They support the importance of more thoroughly defining the unappreciated hazards of moderately loud noise in modern human environments.

In the Subtropical Monsoon Climate High-Density City, What Features of the Neighborhood Environment Matter Most for Public Health?

Urbanization and climate change have been rapidly occurring globally. Evidence-based healthy city development is required to improve living quality and mitigate the adverse impact of the outdoor neighborhood environment on public health. Taking Guangzhou as an example to explore the association of neighborhood environment and public health and preferably to offer some implications for better future city development, we measured ten environmental factors (temperature (T), wind-chill index (WCI), thermal stress index (HSI), relative humidity (RH), average wind speed (AWS), negative oxygen ions (NOI), PM2.5, luminous flux (LF), and illuminance (I)) in four seasons in four typical neighborhoods, and the SF-36 health scale was employed to assess the physical and mental health of neighborhood residents in nine subscales (health transition(HT), physiological functions (PF), general health status (GH), physical pain (BP), physiological functions (RP), energy vitality (VT), mental health (MH), social function (SF), and emotional functions (RE)). The linear mixed model was used in an analysis of variance. We ranked the different environmental factors in relation to aspects of health and weighted them accordingly. Generally, the thermal environment had the greatest impact on both physical and mental health and the atmospheric environment and wind environment had the least impact on physical health and mental health, respectively. In addition, the physical health of the resident was more greatly affected by the environment than mental health. According to the results, we make a number of strategic suggestions for the renewal of the outdoor neighborhood environment in subtropical monsoon climate high-density cities and provide a theoretical basis for improving public health through landscape architecture at the neighborhood scale.

Low-Cost Distributed Acoustic Sensor Network for Real-Time Urban Sound Monitoring

Continuous exposure to urban noise has been found to be one of the major threats to citizens’ health. In this regard, several organizations are devoting huge efforts to designing new in-field systems to identify the acoustic sources of these threats to protect those citizens at risk. Typically, these prototype systems are composed of expensive components that limit their large-scale deployment and thus reduce the scope of their measurements. This paper aims to present a highly scalable low-cost distributed infrastructure that features a ubiquitous acoustic sensor network to monitor urban sounds. It takes advantage of (1) low-cost microphones deployed in a redundant topology to improve their individual performance when identifying the sound source, (2) a deep-learning algorithm for sound recognition, (3) a distributed data-processing middleware to reach consensus on the sound identification, and (4) a custom planar antenna with an almost isotropic radiation pattern for the proper node communication. This enables practitioners to acoustically populate urban spaces and provide a reliable view of noises occurring in real time. The city of Barcelona (Spain) and the UrbanSound8K dataset have been selected to analytically validate the proposed approach. Results obtained in laboratory tests endorse the feasibility of this proposal.

Hotels' Eco-Friendly Physical Environment as Nature-Based Solutions for Decreasing Burnout and Increasing Job Satisfaction and Performance

This study investigates the effect of the hotel’s nature-friendly environment on burnout, job satisfaction and job performance of hotel employees. A total of 11 hypotheses were set up to achieve the purpose of this study, and an empirical analysis was conducted based on 309 surveys collected from hotel employees. A total of 11 hypotheses were set to achieve the research goals, and an empirical analysis was conducted based on a total of 309 pieces of data collected from 320 hotel employees who are currently working in 11 hotels in South Korea. As a result, eight hypotheses were accepted and three were rejected. Specifically, it was found that the hotel’s nature-friendly environment reduced burnout in employees, and indirectly had a significant effect on job satisfaction and job performance. Therefore, the relationship between the variables presented was clearly demonstrated through the research results, and the purpose of this study was satisfactorily explained. The results are expected to be of great help to hotel employees and researchers in developing strategies to efficiently manage hotel employees through nature-based solutions (NBSs). Based on the results, the proposed theoretical and practical implications are discussed in detail in the discussion section.

Burden of disease from transportation noise and motor vehicle crashes: Analysis of data from Houston, Texas

BACKGROUND

Transportation systems have an essential role in satisfying individuals' needs for mobility and accessibility. Yet, they have been linked to several adverse health impacts, with a large, but modifiable, burden of disease. Among the several transportation-related health risk factors, this study focused on transportation-related noise as an emerging exposure whose burden of disease remains partially recognized. We compared premature deaths potentially attributable to transportation-related noise with deaths from motor vehicle crashes, a well-researched and widely recognized transportation risk factor.

METHOD

We employed a standard burden of disease assessment framework to quantify premature cardiovascular diseases mortality attributable to transportation-related (road and aviation) noise at the census tract level (n = 592) in Houston, Texas. The results were compared to motor vehicle crash fatalities, which are routinely observed and collected in the study area. We also investigated the distribution of premature deaths across the city and explored the relationship between household median income and premature deaths attributable to transportation-related noise.

RESULTS

We estimated 302 (95% CI: 185-427) premature deaths (adults 30-75 years old) attributable to transportation-related noise in Houston, compared to 330 fatalities from motor vehicle crashes (adults younger than 75 years old). Transportation-related noise and motor vehicle crashes were responsible for 1.7% and 1.9% of all-cause premature deaths in Houston, respectively. Households with lower median income had a higher risk of adverse exposure and premature deaths potentially attributable to transportation-related noise. A larger number of premature deaths was associated with living in the central business district and the vicinity of highways and airports.

CONCLUSION

This study highlighted the significant contribution of transportation-related noise and motor vehicle crashes to premature deaths in the city of Houston. The analogy between the estimated premature deaths attributable to transportation-related noise and motor vehicle crashes showed that the health impacts of transportation-related noise were as significant as motor vehicle crashes. The estimated premature death rate attributable to transportation-related noise was also comparable to the death rate caused by suicide, influenza, or pneumonia in the US. There is an urgent need for imposing policies to reduce transportation noise emissions and human exposures and to equip health impact assessment tools with a noise burden of disease analysis function.

Population-Based Approaches to Mental Health: History, Strategies, and Evidence

There is growing recognition in the fields of public health and mental health services research that the provision of clinical services to individuals is not a viable approach to meeting the mental health needs of a population. Despite enthusiasm for the notion of population-based approaches to mental health, concrete guidance about what such approaches entail is lacking, and evidence of their effectiveness has not been integrated. Drawing from research and scholarship across multiple disciplines, this review provides a concrete definition of population-based approaches to mental health, situates these approaches within their historical context in the United States, and summarizes the nature of these approaches and their evidence. These approaches span three domains: (a) social, economic, and environmental policy interventions that can be implemented by legislators and public agency directors, (b) public health practice interventions that can be implemented by public health department officials, and (c) health care system interventions that can be implemented by hospital and health care system leaders.

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

Sustainable transportation is an essential part of a sustainable city; however, modern transportation systems with internal-combustion engines emits unacceptably high level of air-pollutants and noise. It is recognized widely that road-traffic noise has negative health impacts (such as annoyance and sleep disturbance) on exposed population in highly-populated cities. These harmful effects should be removed or at least reduced to guarantee the sustainability of modern cities. The estimation of pollutant levels at a specific location and the extent of the damage is therefore important for policy makers. This study presents a procedure to determine the levels of road-traffic noise at both day and night, and an assessment of the adverse health effects across Gwangju Metropolitan City (GMC), Republic of Korea (ROK). Road-traffic noise maps in 2-D and 3-D were generated, in order to find spatial distribution of noise levels across the city and noise level at the façade of a building-floor, respectively. The adoption of existing assessment models for the highly-annoyed (%HA) and highly-sleep-disturbed (%HSD) leads to building-based estimation of the affected population and spatial distribution of the road networks of the city. Very high noise levels were found to exist along major roads in the day and at night, with little difference between them. As a result, approximately 10% and 5% of the total population (n = 1,471,944) were estimated to experience high-level annoyance and sleep disturbance, respectively.

Environmental noise pollution and risk of preeclampsia

BACKGROUND

Environmental noise exposure is associated with a greater risk of hypertension, but the link with preeclampsia, a hypertensive disorder of pregnancy, is unclear.

OBJECTIVES

We sought to determine the relationship between environmental noise pollution and risk of preeclampsia during pregnancy.

METHODS

We analyzed a population-based cohort comprising 269,263 deliveries on the island of Montreal, Canada between 2000 and 2013. We obtained total environmental noise pollution measurements (LA_eq24, L_den, L_night) from land use regression models, and assigned noise levels to each woman based on the residential postal code. We computed odds ratios (OR) and 95% confidence intervals (CI) for the association of noise with preeclampsia in mixed logistic regression models with participants as a random effect, and adjusted for air pollution, neighbourhood walkability, maternal age, parity, multiple pregnancy, comorbidity, socioeconomic deprivation, and year of delivery. We assessed whether noise exposure was more strongly associated with severe or early onset preeclampsia than mild or late onset preeclampsia.

RESULTS

Prevalence of preeclampsia was higher for women exposed to elevated environmental noise pollution levels (LA_eq24h ≥ 65 dB(A) = 37.9 per 1000 vs. <50 dB(A) = 27.9 per 1000). Compared with 50 dB(A), an LA_eq24h of 65.0 dB(A) was not significantly associated the risk of preeclampsia (OR 1.09, 95% CI 0.99-1.20). Associations were however present with severe (OR 1.29, 95% CI 1.09-1.54) and early onset (OR 1.71, 95% CI 1.20-2.43) preeclampsia, with results consistent across all noise indicators. The associations were much weaker or absent for mild and late preeclampsia.

CONCLUSIONS

Environmental noise pollution may be a novel risk factor for pregnancy-related hypertension, particularly more severe variants of preeclampsia.

Physical neighborhood and social environment, beliefs about sleep, sleep hygiene behaviors, and sleep quality among African Americans

OBJECTIVES

African Americans (AAs) have a higher prevalence of sleep disorders than other racial/ethnic groups. However, little is known about the relationships among individual and neighborhood factors related to sleep quality in AAs. The purposes of this study were to (1) describe beliefs about sleep, sleep hygiene behaviors, and sleep quality among AAs; and (2) examine the relationships among sociodemographic characteristics, neighborhood environment, beliefs about sleep, sleep hygiene behaviors, and sleep quality.

METHODS

We conducted a cross-sectional study of 252 AA men and women in the Greater New Haven, CT, USA community. We assessed their sociodemographic characteristics, neighborhood environment, beliefs about sleep, sleep hygiene, and sleep quality with the following measures, respectively: the Neighborhood Environment Scale, the brief version of Dysfunctional Beliefs and Attitudes about Sleep, the Sleep Hygiene Practice Scale, the Pittsburgh Sleep Quality Index. We performed descriptive statistics, correlations and multiple hierarchical regression.

RESULTS

About 72% of the participants (mean age: 53.88 ± 14.17 years, 77.8% women) reported experiencing sleep disturbance. People with poor sleep quality were more likely to report poorer neighborhood social environment (social cohesion), poorer overall neighborhood environment, more dysfunctional beliefs toward sleep, and poorer sleep hygiene than those who had good sleep quality. In the final multivariate model that controlled for a number of chronic comorbid conditions, neighborhood environment, beliefs about sleep, and sleep hygiene behaviors were significantly associated with sleep quality.

CONCLUSIONS

Future efforts are needed to improve sleep among AAs by considering both the individual's belief about sleep, sleep hygiene behaviors and neighborhood factors.

Escaping the Ashby limit for mechanical damping/stiffness trade-off using a constrained high internal friction interfacial layer

The development of new materials with reduced noise and vibration levels is an active area of research due to concerns in various aspects of environmental noise pollution and its effects on health. Excessive vibrations also reduce the service live of the structures and limit the fields of their utilization. In oscillations, the viscoelastic moduli of a material are complex and it is their loss part - the product of the stiffness part and loss tangent - that is commonly viewed as a figure of merit in noise and vibration damping applications. The stiffness modulus and loss tangent are usually mutually exclusive properties so it is a technological challenge to develop materials that simultaneously combine high stiffness and high loss. Here we achieve this rare balance of properties by filling a solid polymer matrix with rigid inorganic spheres coated by a sub-micron layer of a viscoelastic material with a high level of internal friction. We demonstrate that this combination can be experimentally realised and that the analytically predicted behaviour is closely reproduced, thereby escaping the often termed 'Ashby' limit for mechanical stiffness/damping trade-off and offering a new route for manufacturing advanced composite structures with markedly reduced noise and vibration levels.

The Neighborhood Social Environment and Objective Measures of Sleep in the Multi-Ethnic Study of Atherosclerosis

Study objectives

To investigate cross-sectional associations of neighborhood social environment (social cohesion, safety) with objective measures of sleep duration, timing, and disturbances.

Methods

A racially/ethnically diverse population of men and women (N = 1949) aged 54 to 93 years participating in the Multi-Ethnic Study of Atherosclerosis Sleep and Neighborhood Ancillary studies. Participants underwent 1-week actigraphy between 2010 and 2013. Measures of sleep duration, timing, and disruption were averaged over all days. Neighborhood characteristics were assessed via questionnaires administered to participants and an independent sample within the same neighborhood and aggregated at the neighborhood (census tract, N = 783) level using empirical Bayes estimation. Multilevel linear regression models were used to assess the association between the neighborhood social environment and each sleep outcome.

Results

Neighborhood social environment characterized by higher levels of social cohesion and safety were associated with longer sleep duration and earlier sleep midpoint. Each 1 standard deviation higher neighborhood social environment score was associated with 6.1 minutes longer [95% confidence interval (CI): 2.0, 10.2] sleep duration and 6.4 minutes earlier (CI: 2.2, 10.6) sleep midpoint after adjustment for age, sex, race, socioeconomic status, and marital status. These associations persisted after adjustment for other risk factors. Neighborhood social factors were not associated with sleep efficiency or sleep fragmentation index.

Conclusions

A more favorable neighborhood social environment is associated with longer objectively measured sleep duration and earlier sleep timing. Intervening on the neighborhood environment may improve sleep and subsequent health outcomes.

City planning and population health: a global challenge

Significant global health challenges are being confronted in the 21st century, prompting calls to rethink approaches to disease prevention. A key part of the solution is city planning that reduces non-communicable diseases and road trauma while also managing rapid urbanisation. This Series of papers considers the health impacts of city planning through transport mode choices. In this, the first paper, we identify eight integrated regional and local interventions that, when combined, encourage walking, cycling, and public transport use, while reducing private motor vehicle use. These interventions are destination accessibility, equitable distribution of employment across cities, managing demand by reducing the availability and increasing the cost of parking, designing pedestrian-friendly and cycling-friendly movement networks, achieving optimum levels of residential density, reducing distance to public transport, and enhancing the desirability of active travel modes (eg, creating safe attractive neighbourhoods and safe, affordable, and convenient public transport). Together, these interventions will create healthier and more sustainable compact cities that reduce the environmental, social, and behavioural risk factors that affect lifestyle choices, levels of traffic, environmental pollution, noise, and crime. The health sector, including health ministers, must lead in advocating for integrated multisector city planning that prioritises health, sustainability, and liveability outcomes, particularly in rapidly changing low-income and middle-income countries. We recommend establishing a set of indicators to benchmark and monitor progress towards achievement of more compact cities that promote health and reduce health inequities.

Neuroprotective effects of sildenafil against oxidative stress and memory dysfunction in mice exposed to noise stress

Noise exposure has been well characterized as an environmental stressor, and is known to have auditory and non-auditory effects. Phosphodiesterase 5 (PDE5) inhibitors affect memory and hippocampus plasticity through various signaling cascades which are regulated by cGMP. In this study, we investigated the effects of sildenafil on memory deficiency, neuroprotection and oxidative stress in mice caused by chronic noise exposure. Mice were exposed to noise for 4h every day up to 14days at 110dB SPL of noise level. Sildenafil (15mg/kg) was orally administered 30min before noise exposure for 14days. Behavioral assessments were performed using novel object recognition (NOR) test and radial arm maze (RAM) test. Higher levels of memory dysfunction and oxidative stress were observed in noise alone-induced mice compared to control group. Interestingly, sildenafil administration increased memory performance, decreased oxidative stress, and increased neuroprotection in the hippocampus region of noise alone-induced mice likely through affecting memory related pathways such as cGMP/PKG/CREB and p25/CDK5, and induction of free radical scavengers such as SOD1, SOD2, SOD3, Prdx5, and catalase in the brain of stressed mice.

Influencing Genomic Change and Cancer Disparities through Neighborhood Chronic Toxic Stress Exposure: A Research Framework

Black Americans have disproportionately higher incidence and mortality rates for many cancers. These disparities may be related to genomic changes that occur from exposure to chronic toxic stress and may result from conditions associated with living in racially segregated neighborhoods with high rates of concentrated poverty. The purpose of this article is to present a nursing research framework for developing and testing neighborhood-level interventions that have the potential to mitigate exposure to neighborhood-associated chronic toxic stress, improve individual-level genomic sequelae and cancer outcomes, and reduce cancer health disparities of Black Americans. Public health nursing researchers should collaborate with local officials to determine ways to reduce neighborhood-level stress. Intermediate outcomes can be measured using genomic or other stress biomarkers, and long-term outcomes can be measured by evaluating population-level cancer incidence and mortality.

Moving to a Highly Walkable Neighborhood and Incidence of Hypertension: A Propensity-Score Matched Cohort Study

BACKGROUND

The impact of moving to a neighborhood more conducive to utilitarian walking on the risk of incident hypertension is uncertain.

OBJECTIVE

Our study aimed to examine the effect of moving to a highly walkable neighborhood on the risk of incident hypertension.

METHODS

A population-based propensity-score matched cohort study design was used based on the Ontario population from the Canadian Community Health Survey (2001-2010). Participants were adults ≥ 20 years of age who moved from a low-walkability neighborhood (defined as any neighborhood with a Walk Score < 90) to either a high- (Walk Score ≥ 90) or another low-walkability neighborhood. The incidence of hypertension was assessed by linking the cohort to administrative health databases using a validated algorithm. Propensity-score matched Cox proportional hazard models were used. Annual health examination was used as a control event.

RESULTS

Among the 1,057 propensity-score matched pairs there was a significantly lower risk of incident hypertension in the low to high vs. the low to low-walkability groups [hazard ratio = 0.46; 95% CI, 0.26, 0.81, p < 0.01]. The crude hypertension incidence rates were 18.0 per 1,000 person-years (95% CI: 11.6, 24.8) among the low- to low-walkability movers compared with 8.6 per 1,000 person-years (95% CI: 5.3, 12.7) among the low- to high-walkability movers (p < 0.001). There were no significant differences in the hazard of annual health examination between the two mover groups.

CONCLUSIONS

Moving to a highly walkable neighborhood was associated with a significantly lower risk of incident hypertension. Future research should assess whether specific attributes of walkable neighborhoods (e.g., amenities, density, land-use mix) may be driving this relationship.

CITATION

Chiu M, Rezai MR, Maclagan LC, Austin PC, Shah BR, Redelmeier DA, Tu JV. 2016. Moving to a highly walkable neighborhood and incidence of hypertension: a propensity-score matched cohort study. Environ Health Perspect 124:754-760; http://dx.doi.org/10.1289/ehp.1510425.

Analysis of Sampling Methodologies for Noise Pollution Assessment and the Impact on the Population

Today, noise pollution is an increasing environmental stressor. Noise maps are recognised as the main tool for assessing and managing environmental noise, but their accuracy largely depends on the sampling method used. The sampling methods most commonly used by different researchers (grid, legislative road types and categorisation methods) were analysed and compared using the city of Talca (Chile) as a test case. The results show that the stratification of sound values in road categories has a significantly lower prediction error and a higher capacity for discrimination and prediction than in the legislative road types used by the Ministry of Transport and Telecommunications in Chile. Also, the use of one or another method implies significant differences in the assessment of population exposure to noise pollution. Thus, the selection of a suitable method for performing noise maps through measurements is essential to achieve an accurate assessment of the impact of noise pollution on the population.

Traffic-Related Air Pollution, Noise at School, and Behavioral Problems in Barcelona Schoolchildren: A Cross-Sectional Study

BACKGROUND

The available evidence of the effects of air pollution and noise on behavioral development is limited, and it overlooks exposure at schools, where children spend a considerable amount of time.

OBJECTIVE

We aimed to investigate the associations of exposure to traffic-related air pollutants (TRAPs) and noise at school on behavioral development of schoolchildren.

METHODS

We evaluated children 7-11 years of age in Barcelona (Catalonia, Spain) during 2012-2013 within the BREATHE project. Indoor and outdoor concentrations of elemental carbon (EC), black carbon (BC), and nitrogen dioxide (NO2) were measured at schools in two separate 1-week campaigns. In one campaign we also measured noise levels inside classrooms. Parents filled out the strengths and difficulties questionnaire (SDQ) to assess child behavioral development, while teachers completed the attention deficit/hyperactivity disorder criteria of the DSM-IV (ADHD-DSM-IV) list to assess specific ADHD symptomatology. Negative binomial mixed-effects models were used to estimate associations between the exposures and behavioral development scores.

RESULTS

Interquartile range (IQR) increases in indoor and outdoor EC, BC, and NO2 concentrations were positively associated with SDQ total difficulties scores (suggesting more frequent behavioral problems) in adjusted multivariate models, whereas noise was significantly associated with ADHD-DSM-IV scores.

CONCLUSION

In our study population of 7- to 11-year-old children residing in Barcelona, exposure to TRAPs at school was associated with increased behavioral problems in schoolchildren. Noise exposure at school was associated with more ADHD symptoms.

CITATION

Forns J, Dadvand P, Foraster M, Alvarez-Pedrerol M, Rivas I, López-Vicente M, Suades-Gonzalez E, Garcia-Esteban R, Esnaola M, Cirach M, Grellier J, Basagaña X, Querol X, Guxens M, Nieuwenhuijsen MJ, Sunyer J. 2016. Traffic-related air pollution, noise at school, and behavioral problems in Barcelona schoolchildren: a cross-sectional study. Environ Health Perspect 124:529-535; http://dx.doi.org/10.1289/ehp.1409449.

A stochastic simulation framework for the prediction of strategic noise mapping and occupational noise exposure using the random walk approach

Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces.

Residential proximity to major roads and term low birth weight: the roles of air pollution, heat, noise, and road-adjacent trees

BACKGROUND

Maternal residential proximity to roads has been associated with adverse pregnancy outcomes. However, there is no study investigating mediators or buffering effects of road-adjacent trees on this association. We investigated the association between mothers' residential proximity to major roads and term low birth weight (LBW), while exploring possible mediating roles of air pollution (PM(2.5), PM(2.5-10), PM(10), PM(2.5) absorbance, nitrogen dioxide, and nitrogen oxides), heat, and noise and buffering effect of road-adjacent trees on this association.

METHODS

This cohort study was based on 6438 singleton term births in Barcelona, Spain (2001-2005). Road proximity was measured as both continuous distance to and living within 200 m from a major road. We assessed individual exposures to air pollution, noise, and heat using, respectively, temporally adjusted land-use regression models, annual averages of 24-hour noise levels across 50 m and 250 m, and average of satellite-derived land-surface temperature in a 50-m buffer around each residential address. We used vegetation continuous fields to abstract tree coverage in a 200-m buffer around major roads.

RESULTS

Living within 200 m of major roads was associated with a 46% increase in term LBW risk; an interquartile range increase in heat exposure with an 18% increase; and third-trimester exposure to PM(2.5), PM(2.5-10), and PM10 with 24%, 25%, and 26% increases, respectively. Air pollution and heat exposures together explained about one-third of the association between residential proximity to major roads and term LBW. Our observations on the buffering of this association by road-adjacent trees were not consistent between our 2 measures of proximity to major roads.

CONCLUSION

An increased risk of term LBW associated with proximity to major roads was partly mediated by air pollution and heat exposures.

A disadvantaged advantage in walkability: findings from socioeconomic and geographical analysis of national built environment data in the United States

Urban form-the structure of the built environment-can influence physical activity, yet little is known about how walkable design differs according to neighborhood sociodemographic composition. We studied how walkable urban form varies by neighborhood sociodemographic composition, region, and urbanicity across the United States. Using linear regression models and 2000-2001 US Census data, we investigated the relationship between 5 neighborhood census characteristics (income, education, racial/ethnic composition, age distribution, and sex) and 5 walkability indicators in almost 65,000 census tracts in 48 states and the District of Columbia. Data on the built environment were obtained from the RAND Corporation's (Santa Monica, California) Center for Population Health and Health Disparities (median block length, street segment, and node density) and the US Geological Survey's National Land Cover Database (proportion open space and proportion highly developed). Disadvantaged neighborhoods and those with more educated residents were more walkable (i.e., shorter block length, greater street node density, more developed land use, and higher density of street segments). However, tracts with a higher proportion of children and older adults were less walkable (fewer street nodes and lower density of street segments), after adjustment for region and level of urbanicity. Research and policy on the walkability-health link should give nuanced attention to the gap between persons living in walkable areas and those for whom walkability has the most to offer.

Modeling of noise pollution and estimated human exposure around İstanbul Atatürk Airport in Turkey

The level of aircraft noise exposure around İstanbul Atatürk Airport was calculated according to the European Noise Directive. These calculations were based on the actual flight data for each flight in the year 2011. The study area was selected to cover of 25km radius centered on the Aerodrome Reference Point of the airport. The geographical data around İstanbul Atatürk Airport was used to prepare elevation, residential building, auxiliary building, hospital and school layers in SoundPlan software. It was found that 1.2% of the land area of İstanbul City exceeds the threshold of 55dB(A) during daytime. However, when the exceedance of threshold of 65dB(A)is investigated, the affected area is found quite small (0.2% of land area of city). About 0.3% of the land area of İstanbul City has noise levels exceeding 55dB(A) during night-time. Our results show that about 4% of the resident population was exposed to 55dB(A) or higher noises during daytime in İstanbul. When applying the second threshhold criteria, nearly 1% of the population is exposed to noise levels greater than 65dB(A). At night-time, 1.3% of the population is exposed to 55dB(A) or higher noise levels.

The relationship between social fragmentation and sleep among adolescents living in Boston, Massachusetts

BACKGROUND

Sufficient sleep is needed for the healthy development of youth. However, only a small minority of adolescents obtain adequate amounts of sleep. Although individual-level correlates of sleep have been identified, studies investigating the influence of the environment on sleep are warranted.

METHODS

By using cross-sectional data collected from 1878 urban adolescents living in 38 neighborhoods participating in the 2008 Boston Youth Survey (BYS), we determined the association between neighborhood social fragmentation and sleep. Social fragmentation of each participant's residential neighborhood was composed using 2010 US Census data. Multilevel regression models were used to determine the association between social fragmentation and meeting the recommended hours of sleep (>8.5 h) and sleep duration while controlling for individual-level sex, race, age and nativity.

RESULTS

Moderate (OR = 0.51, 95% CI = 0.27, 0.97) and high (OR = 0.33, 95% CI = 0.18, 0.61) social fragmentation within the residential neighborhood was associated with a decreased likelihood of obtaining adequate sleep. Those in moderate (β = -23.9, 95% CI = -43.1, -4.8) and high (β = -22.1, 95% CI = -43.3, -0.9) socially fragmented neighborhoods obtained fewer minutes of sleep per night.

CONCLUSIONS

Social fragmentation may be an important determinant of sleep among youth living in urban settings.

SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

Perception of environmental problems and common mental disorders (CMD)

BACKGROUND

In the past few years, there has been increasing interest in studying the association between problems in the neighbourhood environment and health indicators. The objective of this study is to examine the relationship between the perception of environmental problems by individuals and the prevalence of common mental disorders (CMD) in Spain.

METHODS

A cross-sectional study using data from a large scale national representative survey of households (the 2006 Spanish National Health Survey). Participants included in the study were aged between 16 and 64 years (n = 23,760). The dependent variable was common mental disorders assessed with the General Health Questionnaire (GHQ-12). The independent variable was the individual's perception of environmental problems. Raw and adjusted Odds Ratios and their confidence intervals (95%) were calculated by fitting logistic regression models adjusting for age, marital status, work situation, social class, rural or urban area, country of origin, restrictions in carrying out activities of daily life due to a health problem and social support.

RESULTS

The individuals who reported environmental problems had a higher prevalence of CMD. There was a clear increasing gradient in CMD prevalence with the increase in the number of environmental problems mentioned. Among the subjects who reported to have 1 or no environmental problem the prevalence of CMD was 11.8% (men) and 18.7% (women), and among those who mentioned 6 or more problems, the prevalence increased to 20.8% (men) and 35.4% (women). After adjusting for all the co-variables, there is an association between environmental problems and CMD (men OR 1.44, 95% CI 1.08-1.66; women OR 1.46, 95% CI 1.27-1.67). The environmental problems most strongly associated with the prevalence of CMD are noise, bad smell, air pollution, and lack of green areas.

CONCLUSIONS

Our findings show that individuals who perceive environmental problems in their neighbourhood have a higher prevalence of CMD, even after adjusting for all co-variables. In addition, there is a clear increasing gradient in the prevalence of CMD with the increase in the number of environmental problems. Efforts to reduce the prevalence of CMD must be directed to improve individual and contextual risks.

Road traffic noise: annoyance, sleep disturbance, and public health implications

BACKGROUND

The WHO has recognized environmental noise as harmful pollution that causes adverse psychosocial and physiologic effects (i.e., annoyance and sleep disturbance) on human health. In Europe, noise-related health studies have been actively conducted, but the U.S. has lagged behind in this research field.

PURPOSE

This research predicted ambient levels of road traffic noise for a highly urbanized area: Fulton County GA. Assessment was made of noise impacts on the population, focusing on annoyance and sleep disturbance.

METHODS

All the data sets were collected during 2009-2011, and data analysis was performed in 2010-2011. The study used a sound-propagation model for noise-level prediction and derived noise-impact indicators for annoyance and sleep disturbance from exposure-response models. Then, annoyed and sleep-disturbed populations were predicted with the use of each noise-impact indicator.

RESULTS

It was predicted that 109,967 people would be at risk of being highly annoyed, with 19,621 people at risk for high sleep disturbance for Fulton County GA. Noise-impact indicators such as the percentage of those who were highly annoyed and who had high levels of sleep disturbance were expected to be valuable metrics to compare noise equity among urban communities.

CONCLUSIONS

Many residents of the greater Atlanta area may be exposed to noise levels that put them at risk of being highly annoyed or having high levels of sleep disturbance. These results, if generalized to other urban areas with high levels of road traffic, indicate that it may be important for the public's health to update existing noise-related policies or develop new ones to control and abate noise concerns in urban communities.

Associations between nighttime traffic noise and sleep: the Finnish public sector study

BACKGROUND

Associations between traffic noise and sleep problems have been detected in experimental studies, but population-level evidence is scarce.

OBJECTIVES

We studied the relationship between the levels of nighttime traffic noise and sleep disturbances and identified vulnerable population groups.

METHODS

Noise levels of nighttime-outdoor traffic were modeled based on the traffic intensities in the cities of Helsinki and Vantaa, Finland. In these cities, 7,019 public sector employees (81% women) responded to postal surveys on sleep and health. We linked modeled outdoor noise levels to the residences of the employees who responded to the postal survey. We used logistic regression models to estimate associations of noise levels with subjectively assessed duration of sleep and symptoms of insomnia (i.e., difficulties falling asleep, waking up frequently during the night, waking up too early in the morning, nonrestorative sleep). We also used stratified models to investigate the possibility of vulnerable subgroups.

RESULTS

For the total study population, exposure to levels of nighttime-outside (L(night, outside)) traffic noise > 55 dB was associated with any insomnia symptom ≥ 2 nights per week [odds ratio (OR) = 1.32; 95% confidence interval (CI): 1.05, 1.65]. Among participants with higher trait anxiety scores, which we hypothesized were a proxy for noise sensitivity, the ORs for any insomnia symptom at exposures to L(night, outside) traffic noises 50.1-55 dB and > 55 dB versus ≤ 45 dB were 1.34 (95% CI: 1.00, 1.80) and 1.61 (95% CI: 1.07, 2.42), respectively.

CONCLUSIONS

Nighttime traffic noise levels > 50 dB L(night, outside) was associated with insomnia symptoms among persons with higher scores for trait anxiety. For the total study population, L(night, outside) > 55 dB was positively associated with any symptoms.

Aggravating conditions: cynical hostility and neighborhood ambient stressors

This study is the first to investigate neighborhood clustering of a personality trait--cynical hostility (a sense of mistrust of others amplified by suspicious antagonism.) Cynical hostility increases physiological reactivity by influencing appraisal and coping when stressful events occur and that has been well established as a predictor of cardiovascular disease, inflammation, and all-cause mortality. The analysis examines the associations of a variety of neighborhood physical and social conditions (especially ambient stressors) with individual cynical hostility, controlling for individual sociodemographics. Data are from the Chicago Community Adult Health Survey, a clustered population-based study of 3105 adults. Variation by neighborhood in cynical hostility is larger than variation of other selected health outcomes, which are commonly studied using ecological methods or for other personality measures. Controlling for neighborhood context reduces the black/white cynical hostility disparity by one-third. A measure of neighborhood ambient stressors (notably noise) significantly predicts cynical hostility, even after individual characteristics are controlled, and the effect size is larger than for other contextual predictors. Health-related psychosocial and personality traits may both cluster in and be influenced by contemporaneous neighborhoods rather than mere exogenous results of genes or early life conditions. Health-relevant psychosocial characteristics may also mediate effects of neighborhood deleterious physical conditions, thereby influencing downstream health outcomes and social disparities therein. Because residential location and neighborhood physical conditions are both modifiable, research on how ambient stressors influence health psychology may be particularly fruitful for health policy and practice.

Modeling of road traffic noise and estimated human exposure in Fulton County, Georgia, USA

Environmental noise is a major source of public complaints. Noise in the community causes physical and socio-economic effects and has been shown to be related to adverse health impacts. Noise, however, has not been actively researched in the United States compared with the European Union countries in recent years. In this research, we aimed at modeling road traffic noise and analyzing human exposure in Fulton County, Georgia, United States. We modeled road traffic noise levels using the United States Department of Transportation Federal Highway Administration Traffic Noise Model implemented in SoundPLAN®. After analyzing noise levels with raster, vector and façade maps, we estimated human exposure to high noise levels. Accurate digital elevation models and building heights were derived from Light Detection And Ranging survey datasets and building footprint boundaries. Traffic datasets were collected from the Georgia Department of Transportation and the Atlanta Regional Commission. Noise level simulation was performed with 62 computers in a distributed computing environment. Finally, the noise-exposed population was calculated using geographic information system techniques. Results show that 48% of the total county population [N=870,166 residents] is potentially exposed to 55 dB(A) or higher noise levels during daytime. About 9% of the population is potentially exposed to 67 dB(A) or higher noises. At nighttime, 32% of the population is expected to be exposed to noise levels higher than 50 dB(A). This research shows that large-scale traffic noise estimation is possible with the help of various organizations. We believe that this research is a significant stepping stone for analyzing community health associated with noise exposures in the United States.

A study of riders' noise exposure on Bay Area Rapid Transit trains

Excessive noise exposure may present a hazard to hearing, cardiovascular, and psychosomatic health. Mass transit systems, such as the Bay Area Rapid Transit (BART) system, are potential sources of excessive noise. The purpose of this study was to characterize transit noise and riders' exposure to noise on the BART system using three dosimetry metrics. We made 268 dosimetry measurements on a convenience sample of 51 line segments. Dosimetry measures were modeled using linear and nonlinear multiple regression as functions of average velocity, tunnel enclosure, flooring, and wet weather conditions and presented visually on a map of the BART system. This study provides evidence of levels of hazardous levels of noise exposure in all three dosimetry metrics. L(eq) and L(max) measures indicate exposures well above ranges associated with increased cardiovascular and psychosomatic health risks in the published literature. L(peak) indicate acute exposures hazardous to adult hearing on about 1% of line segment rides and acute exposures hazardous to child hearing on about 2% of such rides. The noise to which passengers are exposed may be due to train-specific conditions (velocity and flooring), but also to rail conditions (velocity and tunnels). These findings may point at possible remediation (revised speed limits on longer segments and those segments enclosed by tunnels). The findings also suggest that specific rail segments could be improved for noise.

Transportation noise and exposed population of an urban area in the Republic of Korea

Using noise prediction models, we explored the transportation noise levels of Youngdeungpo-gu, an urbanized area of Seoul Metropolitan City in the Republic of Korea. In addition, we estimated the population exposed to transportation noise levels and determined how many people are vulnerable to noise levels that would cause serious annoyance and sleep disturbance. Compared with the World Health Organization [WHO] recommended levels, the daytime and nighttime transportation noise levels were still high enough to have the two psychosocial effects on people when considering the recommended levels of the World Health Organization (WHO; 55 decibels [dB[A]] and 40 dB[A] for daytime and nighttime, respectively). Particularly, nighttime transportation noise was discovered to be harmful to a wider area and more people than daytime noise. Approximately 91% of the Youngdeungpo-gu area experienced nighttime transportation noise levels exceeding those recommended by WHO. It was estimated that as much as 80% of the people in the study area were exposed to transportation noise levels >40 dB[A] during nighttime. Taking this into account, there is an urgent need to control and reduce transportation noise levels in Seoul, to protect residents against the potential ill health effects caused by urban transportation.

Spatio-temporal patterns of road traffic noise pollution in Karachi, Pakistan

We studied the spatial and temporal patterns of noise exposure due to road traffic in Karachi City, Pakistan, and found that levels of noise were generally higher during mornings and evenings because of the commuting pattern of Karachi residents. This study found the average value of noise levels to be over 66 dB, which could cause serious annoyance according to the World Health Organization (WHO) outdoor noise guidelines. Maximum peak noise was over 101 dB, which is close to 110 dB, the level that can cause possible hearing impairment according to the WHO guidelines. We found that noise pollution is not an environmental problem reserved for developed countries, but occurs in developing countries as well. For this reason, steps might be required to reduce noise levels caused by road traffic.