Exposure Assessment of Traffic-Related Air Pollution Based on CFD and BP Neural Network and Artificial Intelligence Prediction of Optimal Route in an Urban Area

Abstract

Due to rapid global economic development, the number of motor vehicles has increased sharply, causing significant traffic pollution and posing a threat to people’s health. People’s exposure to traffic-related particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) primarily occurs during commuting. Many studies have used exposure risk assessment models to assess the possible adverse effects of PM2.5, but few have used them to plan low-risk pathways for commuters. This study simulated the pollutant concentration distribution in an idealized urban area in different scenarios. We then used a back propagation (BP) neural network to predict the pollutant concentration. The commuter respiratory deposition dose was calculated based on the BP prediction results, and the respiratory deposition dose was converted into obstacles on the commuting map. Finally, the rapidly exploring random tree star (RRT*) algorithm was used to plan low-risk paths for commuters. The results indicate that pollutants discharged by cars and tree planting can significantly affect the pollutant concentration. A 30.25 μg/m3 increase in the pollutant concentration discharged by cars resulted in a 7~13 μg/m3 increase in the traffic-related air pollution concentration on sidewalks. Combining a computational fluid dynamics simulation, a BP neural network model, and the RRT* algorithm provides a system to plan low-risk paths for commuters. This work proposes artificial-intelligence-based models for calculating the exposure risk to traffic-related pollutants (PM2.5) and choosing a low-risk commuting path to ensure healthy travel.

Dynamic Path Planning Based on Service Level of Road Network

“…Therefore, further refinement of the statistical probability model, including driver obedience rate, may be required in future research to increase the robustness of the HD-SOD method. The HDOSD method also has some significance for future research on the sustainable development of urban transportation [27]. Therefore, the inclusion of carbon emission indicators in the evaluation system is also part of the future work of this paper.…”

Computational Fluid Dynamics Models to Estimate Pedestrian Exposure to Traffic-Related Air Pollution: A Review

“…The calibration of these sensors should be performed for each measurement and ideally compared with data from monitoring stations. [17][18][19][20][21][22][23][24][25][26] Monitoring stations…”

Spatial distribution of PM2.5 concentration around high-rise residential buildings during peak traffic hours in autumn and winter seasons

“…Multiple computational fluid dynamics (CFD) models have been used to simulate pollutant dispersion, amongst others. 28,29 Li et al 30 used CFD simulation to study the influence of traffic tidal flows on the distribution of pollutants in street canyons. They recommended enhancing ventilation to decrease the concentration of pollutants in street canyons.…”