Environmental noise is a critical environmental pollutant defined as unwanted or harmful outdoor sound created by human activities. It is directly associated with health and work-related problems such as hearing loss and children's cognition impairment. A major goal of a smart community is to locate these noise events, monitor them, and analyze their harmful effects on the occupants of the community, preferably in real-time. Remote noise sensing has been shown to be a proper solution in this regard. However, thus far, they have been recognized as an expensive and challengingly expandable solution, especially for large communities. This project aims to develop a cost-effective, precise, and ready-to-use remote noise monitoring solution that can be applied to large communities. The solution is specifically designed to be adaptable to community responses to different noise events. This is carried out by analyzing the noise surveys of annoyance, work- and health-related issues that are collected during the course of a pilot noise monitoring project and redesigning the monitoring solution to better address the community needs. Furthermore, by identifying and analyzing the noisy sites, this project will help city planners and policy makers with their future plans for designing infrastructures such as roads and highways that are one of the major sources of noise pollution in modern societies.

This project will create novel methodologies to measure, record, and analyze the environmental noise in large scales for cities using a combined remote sensing technology. It is intended to use the suggested noise measurement sensors (Class 1 or 2 condenser microphones) by the IEC and ANSI standards to maintain the highest level of accuracy in measurement. This project also integrates surveys of socio-acoustics impacts of noise with real-time measured data to adjust the noise monitoring program to address the community needs. The results of this integration will provide a foundation for developing new noise directives, strategic noise maps, and improving the existing ones to better address the societal needs of the current and future smart and connected communities. In addition, this project will utilize computer simulations, advanced statistical and prediction models, and machine learning to post-process the measured data and analyze it in conjunction with the results of socio-acoustics surveys as a resource for adjusting the monitoring network itself in order to carry out the measurements more efficiently. Specifically, the remote monitoring will be optimized with respect to the number of sensors, location of the sensors, and their power consumption. Furthermore, this project will recognize and categorize the noise events in terms of the community's acoustic discomfort, quantify the health impacts of environmental noise on the community, and create an automatically adjusting noise monitoring system.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.