SCC-PG Remote Sensing and Prediction of Environmental Noise to Facilitate Addressing the Social and Health Issues of Noise - Pilot Study: Schools and Hospitals

环境噪声是一种重要的环境污染物，被定义为人类活动产生的有害的室外声音。它与健康和工作相关的问题直接相关，如听力损失和儿童认知障碍。智能社区的一个主要目标是定位这些噪音事件，监控它们，并分析它们对社区居住者的有害影响，最好是实时的。在这方面，遥感噪声已被证明是一种适当的解决办法。然而，到目前为止，它们被认为是一种昂贵且难以扩展的解决方案，特别是对于大型社区。该项目旨在开发一种具有成本效益、精确且随时可用的远程噪声监测解决方案，可应用于大型社区。该解决方案是专门为适应社区对不同噪音事件的反应而设计的。这项工作是通过分析在噪音监察试验项目期间收集的噪音调查结果，以及有关烦人、工作和健康的问题，并重新设计监察方案，以更好地满足社区的需要。此外，通过识别和分析噪音场所，该项目将帮助城市规划者和政策制定者制定未来的规划，以设计道路和高速公路等基础设施，这些基础设施是现代社会噪音污染的主要来源之一。该项目将创造新的方法，利用综合遥感技术来测量、记录和分析城市大尺度的环境噪声。它旨在使用IEC和ANSI标准建议的噪声测量传感器（1类或2类电容麦克风），以保持最高水平的测量精度。该项目还将噪音对社会声学影响的调查与实时测量数据相结合，以调整噪音监测计划以满足社区需求。这种整合的结果将为制定新的噪音指令、战略噪音地图和改进现有噪音地图提供基础，以更好地满足当前和未来智能和互联社区的社会需求。此外，本项目将利用计算机模拟、先进的统计和预测模型以及机器学习对测量数据进行后处理，并结合社会声学调查结果进行分析，作为调整监测网络本身的资源，以便更有效地进行测量。具体来说，远程监控将根据传感器的数量、传感器的位置和功耗进行优化。此外，本项目将根据社区的声学不适对噪声事件进行识别和分类，量化环境噪声对社区健康的影响，并创建一个自动调节的噪声监测系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.