Study On Distribution Characteristics And Influencing Factors Of Thermal Parameters And Particle Matters In Open Space Of Cold Cities

Urban open space is an important part of the city and also the main place of the urban residents’ activity.In recent years,the increasing urban heat island and air pollution have seriously affected residents’ activity and even threatened their health.In this paper,the typical open space in Harbin is taken as the object.The variation and correlation mechanism of the near-surface thermal environment and the concentration of particulate matter in the urban open space in the cold area are studied by experimental testing and statistical analysis,and the prediction model of the thermal environment parameters and the concentration of particulate matter is developed.Based on the characteristics of urban open space in cold regions,14 monitoring stations of the four typical open space types in Nangang District of Harbin were selected,and small weather stations were set up respectively to conduct long-term monitoring of near-surface thermal environment parameters and particulate matter concentration from November 2020 to February 2022.It provides the data basis for the study of the thermal environment and the distribution characteristics and influencing factors of particulate matter.Base on the two scales of daily distribution and annual distribution,the measured data of temperature,relative humidity,wind speed and solar radiation were analyzed in the four open space types,and compared with the monitoring data of the National Meteorological Science Data Center and the air quality online monitoring and analysis platform.The spatial and temporal distribution differences of thermal environment elements and the differences between urban macro meteorological parameters and local thermal environment in the different urban open spaces were clarified.The results showed that the thermal environment parameters of the open space in the cold area show different periodicity with 24 h as the cycle,and the thermal environment parameters of different types of open space are significantly different.Based on the long-term monitoring data of weather stations,the change rule of daily and annual distribution of particulate matter concentration in the open space of four types of cities in cold regions was compared.The analysis found that the concentration of high particulate matter near the ground changes in working days and non-working days with a cycle of 24 hours.The concentration of particulate matter in working days presents a "double peak and double valley" type curve,while that in non-working days presents an inverted "Z" type distribution.The seasonal variation trend of particulate matter concentration in the open space near the ground in the cold area was obvious,and the distribution was U-shaped throughout the year.The concentration was ranked as winter,spring,autumn,and summer.Aiming at the quantitative change rule of heat-particulate matter in urban open space in cold areas,statistical analysis software SPSS 22.0 was used to analyze various influencing factors of thermal environmental parameters and particulate matter concentration,and the correlation between open space environmental factors such as tree coverage rate,building coverage rate,sky visibility factor and traffic area coverage rate and heat-particulate matter was clarified.The main environmental factors and their influencing relationships of heat-particulate matter in different seasons were obtained.The correlation analysis between thermal environmental parameters and particulate matter concentration was carried out to determine the quantitative relationship between temperature,humidity and wind speed and PM2.5 and PM10 concentration.Based on the quantitative change rules and influencing factors of heat particulate matter in urban open space in cold regions,the BP neural network was used to predict the urban open space thermal environment parameters and particulate matter concentration,and the open space environmental factors,thermal environment factors and reference base values were taken as input variables.After data preprocessing and model training,the daily change prediction of temperature,humidity and PM2.5 was realized.The results show that the BP neural network prediction model can capture the nonlinear law between the heatparticulate matter and the influencing factors well,and the prediction accuracy of temperature,humidity and PM2.5 concentration is more than 80%.The research results of this paper can provide theoretical support for improving the thermal environment quality of urban open space and creating a comfortable outdoor environment for human activities.