Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events.

Cyclists' noise exposure in a Brazilian medium-sized city.

This study aimed to assess cyclists' exposure to noise in a medium-sized Brazilian city. Mobile sensors were used to conduct noise measurements in streets with and without dedicated cycling infrastructures. The method can be summarized in the following procedures: i) characterization of the study area; ii) data collection and validation; iii) calculation of exposure indicators; and iv) comparison and representation of the results on maps.

An international application of the city-wide mobile noise mapping methodology: Retro-active traffic attribution on a bicycle commuters health study in New York City.

The spatial resolution of third party traffic data is not adequately describing the variation of air pollution exposure along the travelled routes of bicycle commuters. In prior work, a city-wide mobile noise mapping methodology was proposed to predict Black Carbon exposure for random bicycle trips, including meteorological variability. In a proof-of-concept pilot, funded by the National Institutes of Environmental Health Sciences (NIEHS), this method is examined in the context of a commuter study in New York City.

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.