Monitoring and modeling sound levels in National Parks

Background/Question/Methods

The National Park Service monitors and manages the quality of the sensory environment to conserve these physical resources, the integrity of ecosystem function, and the quality of visitor experience. Data collected during ten years of monitoring at more than 600 sites in National Park units have been processed to create a model that estimates sound levels throughout the United States. Machine learning methods were applied to predict measured sound levels from local summaries of environmental conditions (climate, hydrology, topography, vegetation, human factors).

Results/Conclusions

Human factors that influence sound levels are population density, transportation networks, and industrial activity. For example, noise elevates median background sound levels by more than 3 decibels in 62% of the coterminous U. S. National parks and other protected natural areas are affected, though many still present opportunities to enjoy low background sound levels and long intervals free from noise. This model also estimates the natural sound levels that would occur when human factors are minimized. This offers one potential target for management in protected natural areas, and the difference between existing and natural conditions can be used to evaluate noise impacts to wildlife. These sound level maps encourage and support  collaborative conservation efforts that extend beyond park boundaries to conserve sensory environments.