Research On Urban Green And Health Route Planning System Based On Exposure Of Atmospheric PM2.5

With the sustained advancement of China’s industrialization and urbanization,the contradiction between the people’s growing need for a better life and the unbalanced social development has become the main contradiction in the new era.In recent years,air pollution problems such as haze have been occurred frequently in many cities,which not only bring serious effects to social production and life,but also pose risks or hazards to residents’health.PM2.5 as the major contributor to the haze problem,its main cause is the emission from fossil fuel combustion.And in order to reduce traffic exhaust emissions,the state strongly encourages residents to adopt green travel methods such as walking and cycling.Thus for responding to the green travel call of country actively and minimizing residents’green travel exposure risk to PM2.5,it is urgent to construct and develop an interactive system of urban green and health route planning based on prediction and exposure risk assessment of atmospheric PM2.5,which is expected to assist the government and relevant departments to make scientific and intelligent policies of PM2.5 control and prevention,and effectively guide residents to travel in a green environment more healthily.Beijing,the capital of China,is selected as the research object in this study.First,analyzing the correlation and temporal variation trend between the air quality monitoring concentrations and meteorological elements of Beijing from 2017 to 2019.The input features are selected by the strategy of correlation coefficients,and the data input format is processed by the method of time sequence.A forecasting model of PM2.5 concentration in the next one hour is established through random forest,and the model is trained by using fixed parameter method and cross-validation to find the optimal parameters combination.The forecasting model’s fitting results in all air quality monitoring stations is evaluated,and it shows that the prediction accuracy R~2 is above 0.87,RMSE is about 15μg/m~3,and MAE is about 8μg/m~3,which indicates that the prediction ability of the model is strong.Secondly,the geographic information software Arc GIS is used to carry out road network topological processing on the urban road map of Beijing,and the intersection nodes and sections in the road network data are persisted into nodes and relationships in the Neo4j graph database.At the same time,according to the predicted PM2.5 concentration of each air quality monitoring station and Beijing regional grid,IDW as a spatial interpolation method in GIS is used to realize the visualization of PM2.5 distribution in urban areas.Then,the relative PM2.5 exposure risk calculation model of road network sections is constructed to evaluate the relative PM2.5exposure risk value of residents’travel roads.The results show that the lowest risk route based on PM2.5 exposure risk weight has less health PM2.5 exposure risk than the shortest distance route based on distance weight,and this difference is more significant from low PM2.5 concentration area to high PM2.5 concentration area(on average of 27%).It indicates that the health route planning based on the exposure risk weight of atmospheric PM2.5 can effectively provide guidance for residents’green travel in a green environment.Finally,an urban green and health route planning system based on exposure risk of atmospheric PM2.5is established through Django framework,and the overall requirements of the system are analyzed from functional requirements and non-functional requirements for the system steadily running respectively.In addition,the system function module is divided into travel route query module,urban PM2.5 distribution query module and train PM2.5 prediction model online module.According to the system functional design flow chart,each functional module is gradually realized.Then,the system’s completeness is ensured through the system functional test and non-functional test.