Research On The Spatiotemporal Distribution And Prediction Of Traffic-related Particulate Matter During Commuting Around Primary Schools

In recent years,environmental problems have always been a hot issue.With the improvement of living standards,car ownership has increased year by year,and the problems caused by traffic-related pollution have become more and more prominent.Traffic-related pollution has a very adverse effect on human respiratory system,cognitive nervous system,cardiovascular and cerebrovascular system.Because of their lower height,elementary school students are exposed to higher concentrations of pollutants during commutes,especially those who commute by foot.Therefore,this study focuses on commuting time around primary schools,takes PM_2.5 and PM₁₀ as the main indicators,explores their spatial and temporal distribution characteristics and predicts their concentration by combining field tests,exposure assessment,computational fluid dynamics simulation and BP neural network.Firstly,point tests were carried out in different primary schools during commuting time.The tests were carried out in primary schools in Jinan city,and instruments were placed on the roadside of student passage.A total of 26 schools were tested.According to the point tests at the school gate,the concentration of particulate matter caused by traffic increases mainly from 7:00to 7:40 in the morning and around 15:40 in the afternoon during commuting hours.Staggered school hours were associated with the duration of increased particulate matter.The closer the distance to the city center,the higher the concentration of particulate matter at the school gate during commuting hours.Different buildings and street layouts around schools lead to different ventilation conditions and different changes in particulate matter concentration.Secondly,taking the No.2 middle(primary)school attached to the Shandong Normal University as an example,the particle concentrations of different commuting routes in commuting periods were tested.Route A(near the main road)and route B(away from the main road)with high commuters were selected for the test.The test was carried out by walking back and forth at a constant speed.In two seasons,PM_2.5 concentration and PM₁₀ concentration of route A were mostly higher than that of route B,with the average value increasing by 1.2 times and 1.1 times respectively.It can be seen from the spatial distribution cloud diagram that the low-concentration distribution area of route B is larger than that of route A.In a word,route B is better for students'commuting than route A.Then,based on the field test data,the respiratory deposition dose of different routes and the total respiratory deposition dose of different routes in 2018 and 2020 were calculated to evaluate the exposure risk of particulates on different routes during commuting time.The results show that the respiratory deposition dose of route B was always lower than that of route A.Based on the three-year average total respiratory deposition dose,route B inhaled 1,144?g(PM_2.5)and 3,117?g(PM₁₀)less than route A.The exposure risk of route B is lower.With the increase of exposure time,the advantage of route B is more obvious.Taking the primary school tested on site as the research object,a 1.38 km×1.99 km building model was built around the school.Computational fluid dynamics software was used for numerical calculation to obtain the distribution of traffic-related PM_2.5 in the commuting time around the primary school under different working conditions.The results show that the difference of particulate matter concentration between route B and route C is small in the north wind,but they are both lower than route A.In other wind directions,PM_2.5 concentration:route B2.5 concentration,reducing traffic flow or idling is still a relatively effective mitigation measure.Finally,the BP neural network is used to construct the prediction model,and the mean square error of training is 0.0015.The predicted output values of the training set and test set are highly consistent with the real output values.The average PM_2.5 concentration of the three routes is predicted:route A 109?g/m~3,route B 99?g/m~3,and route C 102?g/m~3.This result is consistent with the foregoing conclusion.Through a relatively comprehensive research,the database of traffic related particulate pollution status,risk,and simulation prediction direction is enriched,which provides data support for urban planning,traffic control,commuting route selection and other measures,and has practical application significance.At the same time,it is suggested to set up traffic pollution monitoring stations and improve the monitoring network.Combined with the prediction model,accurate understanding of traffic-related pollution can be achieved to better serve healthy travel.