Fusion of Environmental Sensing on PM_2.5 and Deep Learning on Vehicle Detecting for Acquiring Roadside PM_2.5...

Traffic emission is one of the major contributors to urban PM_2.5, an important environmental health hazard. Estimating roadside PM_2.5 concentration increments (above background levels) due to vehicles would assist in understanding pedestrians' actual exposures. This work combines PM_2.5 sensing and vehicle detecting to acquire roadside PM_2.5 concentration increments due to vehicles. An automatic traffic analysis system (YOLOv3-tiny-3l) was applied to simultaneously detect and track vehicles with deep learning and traditional optical flow techniques, respectively, from governmental cameras that have low resolutions of only 352 × 240 pixels. Evaluation with 20% of the 2439 manually labeled images from 23 cameras showed that this system has 87% and 84% of the precision and recall rates, respectively, for five types of vehicles, namely, sedan, motorcycle, bus, truck, and trailer. By fusing the research-grade observations from PM_2.5 sensors installed at two roadside locations with vehicle counts from the nearby governmental cameras analyzed by YOLOv3-tiny-3l, roadside PM_2.5 concentration increments due to on-road sedans were estimated to be 0.0027-0.0050 µg/m³. This practical and low-cost method can be further applied in other countries to assess the impacts of vehicles on roadside PM_2.5 concentrations.