Temporal and spatial variability of traffic-related noise in the City of Toronto, Canada

Random effect generalized linear model-based predictive modelling of traffic noise

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

Traffic noise monitoring and modelling - an overview

Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.

Ship Traffic Flow Prediction in Wind Farms Water Area Based on Spatiotemporal Dependence

To analyze the changing characteristics of ship traffic flow in wind farms water area, and to improve the accuracy of ship traffic flow prediction, a Gated Recurrent Unit (GRU) of a Recurrent Neural Network (RNN) was established to analyze multiple traffic flow sections in complex waters based on their traffic flow structure. Herein, we construct a spatiotemporal dependence feature matrix to predict ship traffic flow instead of the traditional ship traffic flow time series as the input of the neural network. The model was used to predict the ship traffic flow in the water area of wind farms in Yancheng city, Jiangsu Province. Autoregressive Integrated Moving Average (ARIMA), Support-Vector Machine (SVM) and Long Short-Term Memory (LSTM) were chosen as the control tests. The GRU method based on the spatiotemporal dependence is more accurate than the current mainstream ship traffic flow prediction methods. The results verify the reliability and validity of the GRU method.

Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone

We review a Dynamap European Life project whose main scope was the design, commissioning, and actual implementation of “real-time” acoustic maps in a district of the city of Milan (District 9, or Z9, composed of about 2000 road stretches), by employing a small number of noise monitoring stations within the urban zone. Dynamap is based on the idea of finding suitable sets of roads displaying similar daily traffic noise behavior, so that one can group them together into single dynamical noise maps. The Dynamap sensor network has been built upon twenty-four monitoring stations, which have been permanently installed in appropriate locations within the pilot zone Z9, by associating four sensors to each one of the six group of roads considered. In order to decide which road stretches belong to a group, a non-acoustic parameter is used, which is obtained from a traffic flow model of the city, developed and tested over the years by the “Enviroment, Mobility and Territory Agency” of Milan (EMTA). The fundamental predictive equation of Dynamap, for the local equivalent noise level at a given site, can be built by using real-time data provided by the monitoring sensors. In addition, the corresponding contributions of six static traffic noise maps, associated with the six group of roads, are required. The static noise maps can be calculated from the Cadna noise model, based on EMTA road traffic data referred to the ‘rush-hour’ (8:00–9:00 a.m.), when the road traffic flow is maximum and the model most accurate. A further analysis of road traffic noise measurements, performed over the whole city of Milan, has provided a more accurate description of road traffic noise behavior by using a clustering approach. It is found that essentially just two mean cluster hourly noise profiles are sufficient to represent the noise profile at any site location within the zone. In order words, one can use the 24 monitoring stations data to estimate the local noise variations at a single site in real time. The different steps in the construction of the network are described in detail, and several validation tests are presented in support of the Dynamap performance, leading to an overall error of about 3 dB. The present work ends with a discussion of how to improve the design of the network further, based on the calculation of the cross-correlations between monitoring stations’ noise data.

Urban noise distributions and the influence of geometric spreading on skewness

Statistical distributions of urban noise levels are influenced by many complex phenomena, including spatial and temporal variations in the source level, multisource mixtures, propagation losses, and random fading from multipath reflections. This article provides a broad perspective on the varying impacts of these phenomena. Distributions incorporating random fading and averaging (e.g., gamma and noncentral Erlang) tend to be negatively skewed on logarithmic (decibel) axes but can be positively skewed if the fading process is strongly modulated by source power variations (e.g., compound gamma). In contrast, distributions incorporating randomly positioned sources and explicit geometric spreading [e.g., exponentially modified Gaussian (EMG)] tend to be positively skewed with exponential tails on logarithmic axes. To evaluate the suitability of the various distributions, one-third octave band sound-level data were measured at 37 locations in the North End of Boston, MA. Based on the Kullback-Leibler divergence as calculated across all of the locations and frequencies, the EMG provides the most consistently good agreement with the data, which were generally positively skewed. The compound gamma also fits the data well and even outperforms the EMG for the small minority of cases exhibiting negative skew. The lognormal provides a suitable fit in cases in which particular non-traffic noise sources dominate.

Mapping Urban Environmental Performance with Emerging Data Sources: A Case of Urban Greenery and Traffic Noise in Sydney, Australia

Measuring urban environmental performance supports understanding and improving the livability and sustainability of a city. Creating a more livable and attractive environment facilitates a greater shift to active and greener transport modes. Two key aspects, among many others, that determine the environmental performance of an urban area are greenery and noise. This study aims to map street-level greenery and traffic noise using emerging data sources including crowd-sourced mobile phone-based data and street-level imagery data in Sydney, Australia. Results demonstrate the applicability of emerging data sources and the presented advanced techniques in capturing the seasonal variations in urban greenery and time-dependent nature of traffic noise. Results also confirm the presence of a negative correlation between urban greenery and traffic noise.

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time–space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

Assessing personal noise exposure and its relationship with mental health in Beijing based on individuals' space-time behavior

BACKGROUND

Most prior studies adopted a static residence-based approach in the assessment of noise exposure, which may lead to biased exposure estimates and misleading findings in noise-health relationships. Relatively little is known about personal noise exposure based on individuals' space-time behavior and its effect on mental health.

OBJECTIVES

This study aims to analyze and geo-visualize personal exposure to noise in various microenvironments based on individuals' space-time trajectories at a very fine resolution and to further investigate the relationships between mental health and personal noise exposure at both the activity/travel episode level and the entire day level.

METHODS

Individual-level real-time data were collected with portable noise sensors and GPS trackers from a sample of 117 residents aged 18-60 years old from December 2017 to February 2018 in Beijing, China. Descriptive statistics and geo-visualization methods were used to examine how personal noise exposure varied across different activity types, travel modes, and among residents living in the same residential neighborhood on workdays and weekends based on individuals' space-time behaviors. Logistic regression models were applied to examine the relationships between personal noise exposure and self-reported mental health.

RESULTS

We observed substantial differences in personal noise exposure across different activity types. The equivalent sound levels (L_eq, dB(A)) for sleeping were the lowest, while the average L_eq for work-related activities was the highest in indoor environments. The noise exposure levels for activities in outdoor environments were higher than indoor noise levels but differed between workdays and weekends. Variations in noise exposure associated with different travel modes were also evident, with the average L_eq for public transport being much higher than that of other travel modes. A-weighted equivalent sound pressure level measured over 24 h for each individual (L_eq,24h, dB(A)) varied significantly for residents living in the same residential neighborhood, ranging from 36 to 97 dB(A), with the majority of respondents being exposed to noise levels above 55 dB(A) on both workdays and weekends. Regarding the noise-health relationships, the modeling results showed that individual-level objective noise exposure based on space-time behaviors measured over a 24-h period (L_eq,24h) was strongly associated with residents' self-reported mental health. Higher exposure to noise was significantly associated with worse mental health. However, personal noise exposure at the activity/travel episode level (L_eq) was not significantly associated with mental health on weekdays, but this link turned out to be significant in the weekend model.

CONCLUSIONS

There were large variations in personal noise exposure associated with different activity types and travel modes, and the individual-level noise exposure varied significantly across time of day and between residents living in the same residential neighborhood. Variations in personal exposure strongly depend on different space-time behaviors and individual-specific microenvironments experienced in daily life, and they were significantly correlated with mental health.

Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review

Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities

Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R²: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.

Exposure to Ambient Ultrafine Particles and Nitrogen Dioxide and Incident Hypertension and Diabetes

BACKGROUND

Previous studies reported that long-term exposure to traffic-related air pollution may increase the incidence of hypertension and diabetes. However, little is known about the associations of ultrafine particles (≤0.1 μm in diameter) with these two conditions.

METHODS

We conducted a population-based cohort study to investigate the associations between exposures to ultrafine particles and nitrogen dioxide (NO2) and the incidence of diabetes and hypertension. Our study population included all Canadian-born residents aged 30 to 100 years who lived in the City of Toronto, Canada, from 1996 to 2012. Outcomes were ascertained using validated province-wide databases. We estimated annual concentrations of ultrafine particles and NO2 using land-use regression models and assigned these estimates to participants' annual postal code addresses during the follow-up period. Using random-effects Cox proportional hazards models, we calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for ultrafine particles and NO2, adjusted for individual- and neighborhood-level covariates. We considered both single- and multipollutant models.

RESULTS

Each interquartile change in exposure to ultrafine particles was associated with increased risk of incident hypertension (HR = 1.03; 95% CI = 1.02, 1.04) and diabetes (HR = 1.06; 95% CI = 1.05, 1.08) after adjusting for all covariates. These results remained unaltered with further control for fine particulate matter (≤2.5 μm; PM2.5) and NO2. Similarly, NO2 was positively associated with incident diabetes (HR = 1.06; 95% CI = 1.05, 1.07) after controlling for ultrafine particles and PM2.5.

CONCLUSIONS

Exposure to traffic-related air pollution including ultrafine particles and NO2 may increase the risk for incident hypertension and diabetes. See video abstract at, http://links.lww.com/EDE/B337.

Associations of Long-Term Exposure to Ultrafine Particles and Nitrogen Dioxide With Increased Incidence of Congestive Heart Failure and Acute Myocardial Infarction

Although long-term exposure to traffic-related air pollutants such as nitrogen dioxide has been linked to cardiovascular disease (CVD) mortality, little is known about the association between ultrafine particles (UFPs), defined as particles less than or equal to 0.1 μm in diameter, and incidence of major CVD events. We conducted a population-based cohort study to assess the associations of chronic exposure to UFPs and nitrogen dioxide with incident congestive heart failure (CHF) and acute myocardial infarction. Our study population comprised all long-term Canadian residents aged 30-100 years who lived in Toronto, Ontario, Canada, during the years 1996-2012. We estimated annual concentrations of UFPs and nitrogen dioxide by means of land-use regression models and assigned these estimates to participants' postal-code addresses in each year during the follow-up period. We estimated hazard ratios for the associations of UFPs and nitrogen dioxide with incident CVD using random-effects Cox proportional hazards models. We controlled for smoking and obesity using an indirect adjustment method. Our cohorts comprised approximately 1.1 million individuals at baseline. In single-pollutant models, each interquartile-range increase in UFP exposure was associated with increased incidence of CHF (hazard ratio for an interquartile-range increase (HRIQR) = 1.03, 95% confidence interval (CI): 1.02, 1.05) and acute myocardial infarction (HRIQR = 1.05, 95% CI: 1.02, 1.07). Adjustment for fine particles and nitrogen dioxide did not materially change these estimated associations. Exposure to nitrogen dioxide was also independently associated with higher CHF incidence (HRIQR = 1.04, 95% CI: 1.03, 1.06).

Residential green space and pathways to term birth weight in the Canadian Healthy Infant Longitudinal Development (CHILD) Study

Background

A growing number of studies observe associations between the amount of green space around a mother’s home and positive birth outcomes; however, the robustness of this association and potential pathways of action remain unclear.

Objectives

To examine associations between mother’s residential green space and term birth weight within the Canadian Healthy Infant Longitudinal Development (CHILD) study and examine specific hypothesized pathways.

Methods

We examined 2510 births located in Vancouver, Edmonton, Winnipeg, and Toronto Canada. Green space was estimated around mother’s residences during pregnancy using Landsat 30 m normalized difference vegetation index (NDVI). We examined hypothesized pathways of: (1) reduction of environmental exposure; (2) built environment features promoting physical activity; (3) psychosocial conditions; and (4) psychological influences. Linear regression was used to assess associations between green space and term birth weight adjusting first for a comprehensive set of confounding factors and then incrementally for pathway variables.

Results

Fully adjusted models showed non-statistically significant increases in term birth weight with increasing green space. For example, a 0.1 increase in NDVI within 500 m was associated with a 21.5 g (95% CI − 4.6, 47.7) increase in term birth weight. Associations varied by city and were most robust for high-density locations. For the two largest cities (Vancouver and Toronto), we observed an increase in birth weight of 41.2 g (95% CI 7.8, 74.6) for a 0.1 increase in NDVI within 500 m. We did not observe substantial reductions in the green space effect on birth weight when adjusting for pathway variables.

Conclusion

Our results highlight the need to further characterize the interactions between green space, urban density and climate related factors as well as the pathways linking residential green space to birth outcomes.

A combined emission and receptor-based approach to modelling environmental noise in urban environments

The state of practice for noise assessment utilizes established standards for emission and propagation modelling of linear and point sources. Recently, land use regression (LUR) modelling has emerged as an alternative method due to relatively low data and computing resource demands. However, a limitation of LUR modelling is that is does not account for noise attenuation and reflections by features of the built environment. This study demonstrates and validates a method that combines the two modelling frameworks to exploit their respective strengths: Emission and propagation based prediction of traffic noise, the predominant source of noise at the level of streetscapes, and a LUR-based correction for noise sources that vary on spatial scales beyond the streetscape. Multi-criteria analysis, location-allocation modelling and stakeholder consultation identified 220 monitoring sites with optimal coverage for a 1-week sampling period. A subset of sites was used to validate a road traffic noise emission and propagation model and to specify a LUR model that predicted the contribution of other sources. The equivalent 24-h sound pressure level (LAeq) for all sites was 62.9 dBA (SD 6.4). This varied by time of day, weekday, types of roads and land uses. The traffic noise emission model demonstrated a high level of covariance with observed noise levels, with R² values of 0.58, 0.60 and 0.59 for daytime, nighttime and 24-h periods, respectively. Combined with LUR models to correct for other noise sources, the hybrid models R² values were 0.64, 0.71 and 0.67 for the respective time periods. The study showed that road traffic noise emissions account for most of the variability of total environmental noise in Toronto. The combined approach to predict fine resolution noise exposures with emission and receptor-based models presents an effective alternative to noise modelling approaches based on emission and propagation or LUR modelling.

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.

Kriging-based spatial interpolation from measurements for sound level mapping in urban areas

Network-based sound monitoring systems are deployed in various cities over the world and mobile applications allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either in space or in time, complicates the production of sound maps. This paper describes the results of a measurement campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. Mobile measurements have been performed while walking multiple times in every street of the XIIIth district of Paris. By adaptively constructing a noise map on the basis of these measurements, the role of the density of observations and the performance of four different interpolation strategies is investigated. Ordinary and universal Kriging methods are assessed, as well as the effect of using an alternative definition of the distance between observation locations, which takes the topology of the road network into account. The results show that a high density of observation points is necessary to obtain an interpolated sound map close to the reference map.

Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada

Road traffic noise can adversely impact the health of city residents, particularly when it occurs at night. The objective of this study was to evaluate nighttime traffic ambient noise in Toronto, Canada using measured and model-estimated noise levels. Road traffic noise was measured at 767 locations over 3 seasonal sampling campaigns between June 2012 and October 2013 to fully capture noise variability in Toronto. Temporal and campaign-specific spatial models, developed using the noise measurements, were used to build a final predictive surface. The surface was capable of estimating noise across the city over a 24-hr time frame. Measured and surface-estimated noise levels were compared with guidelines from the World Health Organization and the Province of Ontario to identify areas where noise may pose a health risk. Measured mean nighttime noise in Toronto exceeded World Health Organization (40 dBA) guidelines and mean daytime noise exceeded provincial (55 dBA) guidelines. The final predictive surface, incorporating spatial variables and daily cycles in noise levels, provides noise estimates geocoded for the entire study area. This tool could be used for epidemiological studies and to inform noise mitigation efforts. Based on surface-estimated noise levels during the quietest time of night (2 a.m.-2:30 a.m.), 100% of Toronto has nighttime noise exceeding 40 dBA (mean = 57 dBA, range = 49-110 dBA). A predictive surface was developed to estimate geocoded noise levels and facilitate further study of noise in Toronto. This tool can be used to assess road traffic noise, particularly at night, as an environmental health hazard.

Annual traffic noise levels estimation based on temporal stratification

This paper proposes a temporal sampling strategy that increases the accuracy of long-term noise level estimation and allows to establish the estimation error according to the number of sampled days. Days of the week are stratified into working days and weekend days. This research shows how to use measurements of L_eq on working days to estimate the corresponding values for weekend days. This is possible because working days have higher noise levels and less variability than weekend days. The improvement in accuracy allows for a reduction in the number of required sampled days compared to taking samples randomly, which would help to reduce the uncertainty in environmental noise assessment. As a reference, to obtain a 90% confidence interval of ±1 dB for L_day, the proposed sampling strategy reduces the required measurement days by more than 38%. For L_DEN, the reduction is close to 18% of the total number of days. The proposed strategy could be adapted to different environments by simply changing a few parameters.

The Canadian Urban Environmental Health Research Consortium - a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health

BACKGROUND

Multiple external environmental exposures related to residential location and urban form including, air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this linkage will enable investigators to test a variety of their own hypotheses related to the interdependent associations of built environment features with diverse health outcomes encompassed by the cohorts and administrative data.

METHODS

We developed a protocol for compiling measures of built environment features that quantify exposure; vary spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases. Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a new national model being developed, and for transportation-related exposures, with detailed estimates of truck volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green space access and function and life-long climate-related exposures based on local climate zones. Critical challenges exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time, making it difficult to develop and validate historical exposures.

DISCUSSION

CANUE represents a unique effort to coordinate and leverage substantial research investments and will enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified, their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing policies that enhance healthy urban living.

Decomposing an urban soundscape to reveal patterns and drivers of variation in anthropogenic noise

Continuous and intermittent noise may have different effects on humans and wildlife, therefore distinguishing temporal patterns of noise and their drivers is important for policy regarding both public health and wildlife management. We visualized patterns and explored land-use drivers of continuous and high-amplitude intermittent sound pressure levels (SPLs) on an urban campus in Michigan, U.S.A. To visualize patterns of SPLs, we introduce decibel duration curves (DDCs), which show the cumulative frequency distribution of SPLs and aid in the interpretation of statistical SPLs (L_n values) that reflect continuous versus intermittent sounds. DDCs and L_n values reveal that our 24 recording locations varied in the intensity of both continuous and intermittent noise, with intermittent high-amplitude sound events in particular contributing to variability in SPLs over the study site. Time of day influenced both continuous and intermittent SPLs, as locations relatively close to manmade structures (buildings, roads and parking lots) experienced higher SPLs as the day progressed. Continuous SPLs increased with decreasing distance to manmade structures, whereas intermittent SPLs increased with decreasing distance to roads and increasing distance to buildings. Thus, different land-use factors influenced patterns of continuous and intermittent noise, which suggests that different policy and strategies may be needed to ameliorate their effects on the public and wildlife.

Spatial and temporal determinants of A-weighted and frequency specific sound levels-An elastic net approach

BACKGROUND

Urban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies.

OBJECTIVE

To develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts.

METHODS

Short-term sound levels were gathered at n = 400 sites from February 2015 - February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound.

RESULTS

The final models for low, mid, high, and A-weighted sound levels explained 59 - 69% of the variability in each measure. Similar to other A-weighted models, our sound models included transportation related variables such as length of roads and bus lines in the surrounding area; distance to road and rail lines; traffic volume, vehicle mix, residential and commercial land use. However, frequency specific models highlighted additional predictors not included in the A-weighted model including temperature, vegetation, impervious surfaces, vehicle mix, and density of entertainment establishments and restaurants.

CONCLUSIONS

Building spatial temporal models to characterize sound levels across the frequency spectrum using an elastic net approach can be a promising tool for noise exposure assessments within the urban soundscape. Models of sound's character may give us additional important sound exposure metrics to be utilized in epidemiological studies.

Participatory quantitative health impact assessment of urban and transport planning in cities: A review and research needs

INTRODUCTION

Urban and transport planning have large impacts on public health, but these are generally not explicitly considered and/or quantified, partly because there are no comprehensive models, methods and tools readily available. Air pollution, noise, temperature, green space, motor vehicle crashes and physical activity are important pathways linking urban and transport planning and public health. For policy decision-making, it is important to understand and be able to quantify the full-chain from source through pathways to health effects and impacts to substantiate and effectively target actions. In this paper, we aim to provide an overview of recent studies on the health impacts related to urban and transport planning in cities, describe the need for novel participatory quantitative health impact assessments (HIA) and provide recommendations.

METHOD

To devise our searches and narrative, we were guided by a recent conceptual framework linking urban and transport planning, environmental exposures, behaviour and health. We searched PubMed, Web of Science, Science Direct, and references from relevant articles in English language from January 1, 1980, to November 1, 2016, using pre-defined search terms.

RESULTS

The number of HIA studies is increasing rapidly, but there is lack of participatory integrated and full-chain HIA models, methods and tools. These should be based on the use of a systemic multidisciplinary/multisectorial approach and state-of-the-art methods to address questions such as what are the best, most feasible and needed urban and transport planning policy measures to improve public health in cities? Active citizen support and new forms of communication between experts and citizens and the involvement of all major stakeholders are crucial to find and successfully implement health promoting policy measures.

CONCLUSION

We provided an overview of the current state-of-the art of HIA in cities and made recommendations for further work. The process on how to get there is as important and will provide answers to many crucial questions on e.g. how different disciplines can effectively work together, how to incorporate citizen and stakeholder opinion into quantitative HIA modelling for urban and transport planning, how different modelling and measurement methods can be effectively integrated, and whether a public health approach can bring about positive changes in urban and transport planning.

Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung, Taiwan

Road traffic noise exposure has been associated with auditory and non-auditory health effects, but few studies report noise characteristics. This study determines 24-h noise levels and analyzes their frequency components to investigate associations between seasons, meteorology, land-use types, and traffic. We set up 50 monitoring stations covering ten different land-use types and conducted measurements at three times of the year to obtain 24-h-average A-weighted equivalent noise levels (L_Aeq,_24h) and frequency analyses from 2013 to 2014 in Taichung, Taiwan. Information on land-use types, road parameters, traffic flow rates, and meteorological variables was also collected for analysis with the annual averages of road traffic noise and its frequency components. The annual average L_Aeq,_24h in Taichung was 66.4 ± 4.7 A-weighed decibels (dBA). Significant differences in L_Aeq,_24h and frequency components were observed between land-use types (all p-values < 0.001), but not between seasons, with the highest two noise levels of 71.2 ± 1.0 dBA and 70.0 ± 2.6 dBA measured in stream-channel and commercial areas, with the highest component being 61.4 ± 5.3 dBA at 1000 Hz. Road width, traffic flow rates, and land-use types were significantly associated with annual average L_Aeq,_24h (all p-values < 0.050). Noise levels at 125 Hz had the highest correlation with total traffic (Spearman's coefficient = 0.795) and the highest prediction in the multiple linear regression (R² = 0.803; adjusted R² = 0.765). These findings reveal the spatial variation in road traffic noise exposure in Taichung. The highest correlation and predictive capacity was observed between this variation and noise levels at 125 Hz. We recommend that governmental agencies should take actions to reduce noise levels from traffic vehicles.

Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics

The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LA_eq24h, L_night, and L_den to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LA_eq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R²) was 0.68, 0.59, and 0.69 for LA_eq24h, L_night, and L_den, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LA_eq24h levels computed over 5 days at road-traffic sites (bias: -0.7 dB(A)), but not at rail (-2.1 dB(A)) nor at air (-2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.

Car free cities: Pathway to healthy urban living

BACKGROUND

Many cities across the world are beginning to shift their mobility solution away from the private cars and towards more environmentally friendly and citizen-focused means. Hamburg, Oslo, Helsinki, and Madrid have recently announced their plans to become (partly) private car free cities. Other cities like Paris, Milan, Chengdu, Masdar, Dublin, Brussels, Copenhagen, Bogota, and Hyderabad have measures that aim at reducing motorized traffic including implementing car free days, investing in cycling infrastructure and pedestrianization, restricting parking spaces and considerable increases in public transport provision. Such plans and measures are particularly implemented with the declared aim of reducing greenhouse gas emissions. These reductions are also likely to benefit public health.

AIMS

We aimed to describe the plans for private car free cities and its likely effects on public health.

METHODS

We reviewed the grey and scientific literature on plans for private car free cities, restricted car use, related exposures and health.

RESULTS

An increasing number of cities are planning to become (partly) private car free. They mainly focus on the reduction of private car use in city centers. The likely effects of such policies are significant reductions in traffic-related air pollution, noise, and temperature in city centers. For example, up to a 40% reduction in NO2 levels has been reported on car free days. These reductions are likely to lead to a reduction in premature mortality and morbidity. Furthermore the reduction in the number of cars, and therefore a reduction in the need for parking places and road space, provides opportunities to increase green space and green networks in cities, which in turn can lead to many beneficial health effects. All these measures are likely to lead to higher levels of active mobility and physical activity which may improve public health the most and also provide more opportunities for people to interact with each other in public space. Furthermore, such initiatives, if undertaken at a sufficiently large scale can result in positive distal effects and climate change mitigation through CO2 reductions. The potential negative effects which may arise due to motorized traffic detouring around car free zone into their destinations also need further evaluation and the areas in which car free zones are introduced need to be given sufficient attention so as not to become an additional way to exacerbate socioeconomic divides. The extent and magnitude of all the above effects is still unclear and needs further research, including full chain health impact assessment modeling to quantify the potential health benefits of such schemes, and exposure and epidemiological studies to measure any changes when such interventions take place.

CONCLUSIONS

The introduction of private car free cities is likely to have direct and indirect health benefits, but the exact magnitude and potential conflicting effects are as yet unclear. This paper has overviewed the expected health impacts, which can be useful to underpin policies to reduce car use in cities.

Sleep Disturbance from Road Traffic, Railways, Airplanes and from Total Environmental Noise Levels in Montreal

The objective of our study was to measure the impact of transportation-related noise and total environmental noise on sleep disturbance for the residents of Montreal, Canada. A telephone-based survey on noise-related sleep disturbance among 4336 persons aged 18 years and over was conducted. L_Night for each study participant was estimated using a land use regression (LUR) model. Distance of the respondent’s residence to the nearest transportation noise source was also used as an indicator of noise exposure. The proportion of the population whose sleep was disturbed by outdoor environmental noise in the past 4 weeks was 12.4%. The proportion of those affected by road traffic, airplane and railway noise was 4.2%, 1.5% and 1.1%, respectively. We observed an increased prevalence in sleep disturbance for those exposed to both rail and road noise when compared for those exposed to road only. We did not observe an increased prevalence in sleep disturbance for those that were both exposed to road and planes when compared to those exposed to road or planes only. We developed regression models to assess the marginal proportion of sleep disturbance as a function of estimated L_Night and distance to transportation noise sources. In our models, sleep disturbance increased with proximity to transportation noise sources (railway, airplane and road traffic) and with increasing L_Night values. Our study provides a quantitative estimate of the association between total environmental noise levels estimated using an LUR model and sleep disturbance from transportation noise.

Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities

BACKGROUND

The majority of people live in cities and urbanization is continuing worldwide. Cities have long been known to be society's predominant engine of innovation and wealth creation, yet they are also a main source of pollution and disease.

METHODS

We conducted a review around the topic urban and transport planning, environmental exposures and health and describe the findings.

RESULTS

Within cities there is considerable variation in the levels of environmental exposures such as air pollution, noise, temperature and green space. Emerging evidence suggests that urban and transport planning indicators such as road network, distance to major roads, and traffic density, household density, industry and natural and green space explain a large proportion of the variability. Personal behavior including mobility adds further variability to personal exposures, determines variability in green space and UV exposure, and can provide increased levels of physical activity. Air pollution, noise and temperature have been associated with adverse health effects including increased morbidity and premature mortality, UV and green space with both positive and negative health effects and physical activity with many health benefits. In many cities there is still scope for further improvement in environmental quality through targeted policies. Making cities 'green and healthy' goes far beyond simply reducing CO2 emissions. Environmental factors are highly modifiable, and environmental interventions at the community level, such as urban and transport planning, have been shown to be promising and more cost effective than interventions at the individual level. However, the urban environment is a complex interlinked system. Decision-makers need not only better data on the complexity of factors in environmental and developmental processes affecting human health, but also enhanced understanding of the linkages to be able to know at which level to target their actions. New research tools, methods and paradigms such as geographical information systems, smartphones, and other GPS devices, small sensors to measure environmental exposures, remote sensing and the exposome paradigm together with citizens observatories and science and health impact assessment can now provide this information.

CONCLUSION

While in cities there are often silos of urban planning, mobility and transport, parks and green space, environmental department, (public) health department that do not work together well enough, multi-sectorial approaches are needed to tackle the environmental problems. The city of the future needs to be a green city, a social city, an active city, a healthy city.

Annoyance from Road Traffic, Trains, Airplanes and from Total Environmental Noise Levels

There is a lack of studies assessing the exposure-response relationship between transportation noise and annoyance in North America. Our aims were to investigate the prevalence of noise annoyance induced by road traffic, trains and airplanes in relation to distance to transportation noise sources, and to total environmental noise levels in Montreal, Canada; annoyance was assessed as noise-induced disturbance. A telephone-based survey among 4336 persons aged >18 years was conducted. Exposure to total environmental noise (A-weighted outdoor noise levels—LA_eq24h and day-evening-night equivalent noise levels—L_den) for each study participant was determined using a statistical noise model (land use regression—LUR) that is based on actual outdoor noise measurements. The proportion of the population annoyed by road traffic, airplane and train noise was 20.1%, 13.0% and 6.1%, respectively. As the distance to major roads, railways and the Montreal International Airport increased, the percentage of people disturbed and highly disturbed due to the corresponding traffic noise significantly decreased. When applying the statistical noise model we found a relationship between noise levels and disturbance from road traffic and total environmental noise, with Prevalence Proportion Ratios (PPR) for highly disturbed people of 1.10 (95% CI: 1.07–1.13) and 1.04 (1.02–1.06) per 1 dB(A) L_den, respectively. Our study provides the first comprehensive information on the relationship between transportation noise levels and disturbance in a Canadian city. LUR models are still in development and further studies on transportation noise induced annoyance are consequently needed, especially for sources other than road traffic.

The temporal structure of pollution levels in developed cities

Currently, the need for mobility can cause significant pollution levels in cities, with important effects on health and quality of life. Any approach to the study of urban pollution and its effects requires an analysis of spatial distribution and temporal variability. It is a crucial dilemma to obtain proven methodologies that allow an increase in the quality of the prediction and the saving of resources in the spatial and temporal sampling. This work proposes a new analytical methodology in the study of temporal structure. As a result, a model for estimating annual levels of urban traffic noise was proposed. The average errors are less than one decibel in all acoustics indicators. A new working methodology of urban noise has begun. Additionally, a general application can be found for the study of the impacts of pollution associated with traffic, with implications for urban design and possibly in economic and sociological aspects.

Pilot study of methods and equipment for in-home noise level measurements

Knowledge of the auditory and non-auditory effects of noise has increased dramatically over the past decade, but indoor noise exposure measurement methods have not advanced appreciably, despite the introduction of applicable new technologies. This study evaluated various conventional and smart devices for exposure assessment in the National Children's Study. Three devices were tested: a sound level meter (SLM), a dosimeter, and a smart device with a noise measurement application installed. Instrument performance was evaluated in a series of semi-controlled tests in office environments over 96-hour periods, followed by measurements made continuously in two rooms (a child's bedroom and a most used room) in nine participating homes over a 7-day period with subsequent computation of a range of noise metrics. The SLMs and dosimeters yielded similar A-weighted average noise levels. Levels measured by the smart devices often differed substantially (showing both positive and negative bias, depending on the metric) from those measured via SLM and dosimeter, and demonstrated attenuation in some frequency bands in spectral analysis compared to SLM results. Virtually all measurements exceeded the Environmental Protection Agency's 45 dBA day-night limit for indoor residential exposures. The measurement protocol developed here can be employed in homes, demonstrates the possibility of measuring long-term noise exposures in homes with technologies beyond traditional SLMs, and highlights potential pitfalls associated with measurements made by smart devices.