Luc Dekoninck

The influence of traffic noise on appreciation of the living quality of a neighborhood.

Traffic influences the quality of life in a neighborhood in many different ways. Today, in many patsy of the world the benefits of accessibility are taken for granted and traffic is perceived as having a negative impact on satisfaction with the neighborhood. Negative health effects are observed in a number of studies and these stimulate the negative feelings in the exposed population. The noise produced by traffic is one of the most important contributors to the appreciation of the quality of life. Thus, it is useful to define a number of indicators that allow monitoring the current impact of noise on the quality of life and predicting the effect of future developments. This work investigates and compares a set of indicators related to exposure at home and exposure during trips around the house. The latter require detailed modeling of the population’s trip behavior. The validity of the indicators is checked by their ability to predict the outcome of a social survey and by outlining potential causal paths between them and the outcome variables considered: general satisfaction with the quality of life in the neighborhood, noise annoyance at home, and reported traffic density in the area.

Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures

Several studies show that a significant portion of daily air pollution exposure, in particular black carbon (BC), occurs during transport. In a previous work, a model for the in-traffic exposure of bicyclists to BC was proposed based on spectral evaluation of mobile noise measurements and validated with BC measurements in Ghent, Belgium. In this paper, applicability of this model in a different cultural context with a totally different traffic and mobility situation is presented. In addition, a similar modeling approach is tested for particle number (PN) concentration. Indirectly assessing BC and PN exposure through a model based on noise measurements is advantageous because of the availability of very affordable noise monitoring devices. Our previous work showed that a model including specific spectral components of the noise that relate to engine and rolling emission and basic meteorological data, could be quite accurate. Moreover, including a background concentration adjustment improved the model considerably. To explore whether this model could also be used in a different context, with or without tuning of the model parameters, a study was conducted in Bangalore, India. Noise measurement equipment, data storage, data processing, continent, country, measurement operators, vehicle fleet, driving behavior, biking facilities, background concentration, and meteorology are all very different from the first measurement campaign in Belgium. More than 24h of combined in-traffic noise, BC, and PN measurements were collected. It was shown that the noise-based BC exposure model gives good predictions in Bangalore and that the same approach is also successful for PN. Cross validation of the model parameters was used to compare factors that impact exposure across study sites. A pooled model (combining the measurements of the two locations) results in a correlation of 0.84 when fitting the total trip exposure in Bangalore. Estimating particulate matter exposure with traffic noise measurements was thus shown to be a valid approach across countries and cultures.

Noise measurements as proxies for traffic parameters in monitoring networks.

The present research describes how microphones could be used as proxies for traffic parameter measurements for the estimation of airborne pollutant emissions. We consider two distinct measurement campaigns of 7 and 12 days, at two different locations along the urban ring road in Antwerp, Belgium, where sound pressure levels and traffic parameters were measured simultaneously. Noise indicators are calculated and used to construct models to estimate traffic parameters. It is found that relying on different statistical levels and selecting specific sound frequencies permits an accurate estimation of traffic intensities and mean vehicle speeds, both for light and heavy vehicles. Estimations of R(2) values ranging between 0.81 and 0.92 are obtained, depending on the location and traffic parameters. Furthermore, the usefulness of these estimated traffic parameters in a monitoring strategy is assessed. Carbon monoxide, hydrocarbon and nitrogen oxide emissions are calculated with the airborne pollutant emission model Artemis. The Artemis outputs fed with directly measured and estimated traffic parameters (based on noise measurements) are very similar. Finally, a method is proposed to enable using a model calibrated at one location at another location without the need for new calibration, making it straightforward to include new measurement locations in a monitoring network.

Using city-wide mobile noise assessments to estimate bicycle trip annual exposure to Black Carbon

Several studies have shown that a significant amount of daily air pollution exposure, in particular Black Carbon (BC), is inhaled during bicycle trips. Previously, the instantaneous BC exposure of cyclists was modeled as the sum of a background concentration and a local traffic related component based on a local assessment of traffic noise. We present a fast and low cost methodology to achieve a city-wide assessment of yearly average BC exposure of cyclists along their trips, based on a city-wide mobile noise sensing campaign. The methodology requires participatory sensing measurements of noise, partially combined with BC and/or other air pollutants sensitive to local traffic variations. The combined measurements cover the spatial and meteorological variability and provide the data for an instantaneous exposure model. The mobile noise-only measurements map the full city; and yearly meteorology statistics are used to extrapolate the instantaneous exposure model to a yearly average map of in-traffic air pollution exposure. Less than four passages at each segment along the network with mobile noise equipment are necessary to reach a standard error of 500 ng/m(3) for the yearly average BC exposure. A strong seasonal effect due to the BC background concentration is detected. The background contributes only 25% to the total trip exposure during spring and summer. During winter the background component increases to 50-60%. Engine related traffic noise along the bicyclists route is a valid indicator of the BC exposure along the route, independent of the seasonal background. Low exposure route selection results in an exposure reduction of 35% in winter and 60% in summer, sensitive to the weather conditions, specific trip attributes and the available alternatives. The methodology is relevant for further research into the local effects of air pollution on health. Mobile noise mapping adds local traffic data including traffic dynamics into the air pollution exposure assessments. Local policy makers and urban planners can use the results to support the implementation of low exposure infrastructure, create awareness through route planners and achieve behavioral changes toward active travel modes.

Evolution of building façade road traffic noise levels in Flanders

The evolution of daytime façade noise levels by road traffic at 250 dwellings in Flanders is assessed. Three identical man-operated measurement campaigns have been conducted in the years 1996, 2001 and 2009, during fall. A practical methodology has been developed, based on short time noise measurements and context observations at these locations. The uncertainty introduced by short-term sampling has been quantified as a function of the noise level. Furthermore, a correction is proposed for measuring at a random moment during daytime. Analysis of the data showed that road traffic noise levels hardly changed globally over this period of 13 years. The distribution of changes in noise level at corresponding measurement locations is nevertheless rather wide-all improvements are equally compensated by increases in noise levels at other locations. The percentage of the dwelling façades exposed to daytime noise levels above 65 dBA has increased slightly between 1996 and 2001, but seems to stagnate in 2009. In spite of the increased interest and actions of policy makers during the past decades, noise exposure caused by road traffic at dwelling façades is a persistent problem.

The application of a notice-event model to improve classical exposure-annoyance estimation

Sound perception of humans is determined by a variety of factors such as intensity, frequency, temporal structure, masking and localization. Furthermore, a wide range of non-acoustical factors determine whether certain sounds are perceived as annoying. However, classical exposure-response determination for the assessment of annoyance and health effects is based on average sound levels - sometimes with applied penalties for evening and night noise (Lden). A research collaboration between Ghent University and the Medical University Innsbruck focuses on the improvement of exposure-annoyance modeling by including characteristics of the temporal structure and the attention of the involved human subjects. The basis for this work is the developed “notice-event-model” (De Coensel B et al. 2009). Intensive traffic modeling as input for extended individual noise mapping per dwelling allows to test the additional impact by the inclusion of derived acoustical indicators of the temporal pattern (Fluctuation, emergence...

Airborne sound propagation over sea during offshore wind farm piling.

Offshore piling for wind farm construction has attracted a lot of attention in recent years due to the extremely high noise emission levels associated with such operations. While underwater noise levels were shown to be harmful for the marine biology, the propagation of airborne piling noise over sea has not been studied in detail before. In this study, detailed numerical calculations have been performed with the Greens Function Parabolic Equation (GFPE) method to estimate noise levels up to a distance of 10 km. Measured noise emission levels during piling of pinpiles for a jacket-foundation wind turbine were assessed and used together with combinations of the sea surface state and idealized vertical sound speed profiles (downwind sound propagation). Effective impedances were found and used to represent non-flat sea surfaces at low-wind sea states 2, 3, and 4. Calculations show that scattering by a rough sea surface, which decreases sound pressure levels, exceeds refractive effects, which increase sound pressure levels under downwind conditions. This suggests that the presence of wind, even when blowing downwind to potential receivers, is beneficial to increase the attenuation of piling sound over the sea. A fully flat sea surface therefore represents a worst-case scenario.

Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models

Personal exposure is sensitive to the personal features and behavior of the individual, and including interpersonal variability will improve the health and quality of life evaluations. Participatory sensing assesses the spatial and temporal variability of environmental indicators and is used to quantify this interpersonal variability. Transferring the participatory sensing information to a specific study population is a basic requirement for epidemiological studies in the near future. We propose a methodology to reduce the void between participatory sensing and health research. Instantaneous microscopic land-use regression modeling (µLUR) is an innovative approach. Data science techniques extract the activity-specific and route-sensitive spatiotemporal variability from the data. A data workflow to prepare and apply µLUR models to any mobile population is presented. The µLUR technique and data workflow are illustrated with models for exposure to traffic related Black Carbon. The example µLURs are available for three micro-environments; bicycle, in-vehicle, and indoor. Instantaneous noise assessments supply instantaneous traffic information to the µLURs. The activity specific models are combined into an instantaneous personal exposure model for Black Carbon. An independent external validation reached a correlation of 0.65. The µLURs can be applied to simulated behavioral patterns of individuals in epidemiological cohorts for advanced health and policy research.

Scenarios for the acoustic environment in Flanders in 2010

The Flemish Environment Agencys report assesses the levels of noise pollution from both automobiles and planes and what percentage of the Flanders population idsaffected by it. Discoveries were made as to what caused recent increases in these percentages and predictions were made for the percentage for the year 2010. If the percent affected is to be below 20 percent by the year 2010, then a reversal of the current trends of increased air and automobile travel must be accomplished.

Noise emissions at intersections: comparing microscopic and macroscopic traffic simulation approaches

Environmental noise mapping often involves the use of traffic simulation software. In most cases, traffic flows are simulated in a macroscopic way, considering only traffic flow parameters averaged over road segments. This approach does not allow to correctly account for the typical deceleration and acceleration patterns of traffic at intersections, which influence local noise emissions. Microscopic traffic simulation models, which model each vehicle individually, incorporate these dynamic effects. However, they require much larger amounts of input data for calibration, and are therefore less suited to map large areas. In this paper, we investigate the possible influence of the choice of simulation detail on simulated traffic flow parameters and, as a consequence, on noise emissions near intersections. As a case study, several types of road intersections under various traffic demand and traffic composition conditions are considered. Simulation results are obtained using a microscopic (Paramics) as well as...