Estimating Near-Roadway Air Pollution from Multi-Frequency Noise Measurements

Abstract

Air pollution is a major environmental problem and monitoring it is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-minute temporal resolution. The models were based on data that were collected by co-locating noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson’s R = 0.87 with a root mean square error of 7.2 ppb, about 10% of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method provides a cost-effective and efficient approach to estimating near-road air pollutant concentrations in urban areas at high temporal resolution.

Keywords: Noise, Noise frequency, Air quality, Near-roadway air quality, Traffic, machine learning