A combined deep learning and physical modelling method for estimating air pollutants? source location and emission profile in street canyons

Roadside air pollution monitoring stations have become frequently available for street canyons. To efficiently estimate source location and emission profile in street canyons, this study developed a combined deep learning and physical modelling method using the monitoring data as inputs. First, a deep neural network (DNN) was constructed for locating the source. The training datasets were generated from numerical simulations by the computational fluid dynamics (CFD)-Markov chain model. An inverse method based on Tikhonov regularization was then used to estimate the emission profile. Finally, the Markov chain model was used to calculate the air pollutant distribution in the whole street canyon. Case studies were conducted to demonstrate the performance of the proposed method. For the unit impulse source in the 2-D ventilated chamber of 27 m2, the source in 83% of the cases were accurately identified, and in another 13% of the cases, the identified source was within 0.4 m to the true location. For the continuous pollutant source with varying emission profile in the 3-D street canyon with an area of 25,600 m2, the source in 36% of the cases were accurately located, and in another 52% of the cases, it was within 10 m from the true location.

Automated summary

This study developed a combined deep learning and physical modelling method for efficiently estimating source location and emission profile in street canyons, demonstrating high accuracy in identifying pollution sources and locating them within close proximity to the true location in controlled or real street canyon environments.