Abstract
Traffic emission is one of the major contributors to urban PM2.5, an important environmental health hazard. Estimating roadside PM2.5 concentration increments (above background levels) due to vehicles would assist in understanding pedestrians’ actual exposures. This work combines PM2.5 sensing and vehicle detecting to acquire roadside PM2.5 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 PM2.5 sensors installed at two roadside locations with vehicle counts from the nearby governmental cameras analyzed by YOLOv3-tiny-3l, roadside PM2.5 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 PM2.5 concentrations.
Highlights
IntroductionSensing technology for environmental pollutants has been rapidly developing in recent years [1,2]
Sensing technology for environmental pollutants has been rapidly developing in recent years [1,2].The application of low-cost sensors (LCSs) provides opportunities to tackle research challenges that have been difficult to address before [3,4]
The objectives of this work are to (1) evaluate an automatic traffic analysis system based on deep learning and optical flow techniques, (2) apply the automatic traffic analysis system in the field along with PM2.5 sensing, and (3) acquire roadside PM2.5 concentration increments due to vehicles with data fusion of PM2.5 sensing and vehicle counting
Summary
Sensing technology for environmental pollutants has been rapidly developing in recent years [1,2]. The targeted environmental pollutant in this work is particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5 ), which is a classified human carcinogen [5] with annual mean levels of up to 100 μg/m3 in many urban areas around the world [6,7], much higher than 10 μg/m3 , the annual recommended guideline of the World. For urban PM2.5 , traffic is the single largest contributor, accounting for 25% globally [11]. PM2.5 (μg/m3 ) Sedan_near Motocycle_near Bus_near Truck_near Trailer_near. Speed_near 1 (km/h) Sedan_far Motocycle_far Bus_far Truck_far
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.