Studies On Variation Pattern Of Negative Air Ions And The Influential Factors In Shanghai Zhongshan Park

With the urbanization and the alternation of living environment,more and more people go to parks for their leisure time and physical exercise regularly.Relative to the other areas in a city,there are usually higher concentrations of negative air ions(NAIs)in urban parks.NAIs have essential effects for inhibiting bacteria and cleaning atmospheric air,showing the benefits to health of residents in urban areas.Zhongshan park,located in the central area of Shanghai,is one of the earliest urban parks in Shanghai.It is rich in plant species,has a high canopy coverage,and is distributed with grassland,shrub,ponds and small squares.From April2017 to March 2018,the change of negative air ion concentrations(NAIC)in the park were measured by a long-term observation instruments in the park.And nine different sampling points were chosen to monitor the changes of NAIC in different plant communities,so as to analyze the spatial and temporal variation pattern of NAIC in the park.At the same time,the 16 meteorological factors and atmosphere pollutants,such as temperature,humidity,PM_2.5,PM₁₀and so forth,were measured to analyze the synergistic effect of plant communities and environmental factors on NAIC.In addition,on the basis of field observation research,simulation control experiments were carried out in the laboratory to verify the key influential factors and their sensitivity.The main results are as follows:Firstly,according to the observation of different community samples,the results showed that the daily variation of NAIs were low in the morning and evening,high in chalk and slightly decreased at noon.On the seasonal variation,there were a higher NAIs during the seasons of summer and autumn,lower during the winter and spring.In order to explore the influence of plant communities on NAIC,we compared the data obtained at the nine sampling points based on community structures,communities canopy density,tree species and water systems.We found that the more complex the community structures were,the lower the NAIs variability were,and the variability of NAIC in the low-depression plant communities were larger.The correlations were stronger between May and October for the growing season of plants and between 7:00 and 19:00 with photosynthesis.Secondly,according to the long-term observation instruments data,the annual average of NAIC was 410 cm^-3,the maximum monthly average of NAIC was 485 cm^-3,and the minimum monthly average of NAIC was296 cm^-3;and the NAIC were lower in the spring and winter than in the summer and autumn;there was no apparent regularity within a week;the change of NAIC in a day appeared in a single-peak model,in which the maximum reached in the noon.Through the statistical analysis results,we found that temperature,humidity,precipitation,radiation and PM_2.5were significantly correlated with NAIC.Due to the strong collinlinearity among the influential factors,the independent contributions of typical factors to the NAIC were explored according to classification screening and random forest algorithm.The order of their characteristic importance was as follows:humidity>radiation>temperature>PM_2.5.This result further reveals the differential effects of the same factor on the change of NAIC in different seasons.Thirdly,according to the sensitivity of typical influential factors were verified by laboratory simulation control experiment,and the attenuation regulars of NAIC were studied by simulation experiment,which were based on single variable control principle.The results implied that the sensitivity of temperature was greater than that of PM_2.5,that is,the change of temperature had a significant effect on the attenuation of NAIC.In this study,simultaneous long-term local monitoring method for meteorological factors and environmental pollutants were adopted,which laid data foundation for big data analysis.We innovatively introduced screening analysis and random forest algorithm,which eliminated the collinearity among environmental factors,and further ranked the contribution of four typical factors,providing a new idea for the research on the influence of environmental factors on NAIs.