Characteristics and Prediction Model of Air Negative Oxygen Ion Concentration in Northern Chengde
Based on the Negative Oxygen Ion(NAI)concentration observation station data,meteorological data and environmental data from January 1st,2020 to May 31st,2022 in Fengning,the temporal variation characteristics of the NAI concentration and its temporal relationship with meteorological and environmental factors were analyzed in this paper,and key influencing factors that affected the NAI concentration were identified.A prediction model of NAI concentration was established.The results showed that the average annual concentrations of the NAI in the urban park and the forest region in Fengning were 1358.7 and 1995.8 ion·cm-3,respectively,with daily average maximum values of 3867 and 5845 ion·cm-3,indicating that there were natural environmental conditions for treatment and rehabilitation in the urban park and the forest region.The monthly variations of NAI concentration in the urban park and the forest region showed a single peak distribution,with peaks appearing in July(1889.5 ion·cm-3)and August(2516.3 ion·cm-3)respectively,and the minimum value in two regions appearing in January.The NAI concentration in the forest region was significantly higher than that in the urban park.The daily variation of NAI concentration showed a bimodal distribution,with peaks appearing around 06:00 and 20:00,respectively,and the minimum value appearing around 14:00.The concentrations at night in two regions were higher than that during the day.The air temperature,precipitation,relative humidity,wind speed,sunshine,PM2.5 and O3 were significantly correlated with NAI concentration in the urban park and the forest region.The meteorological and environmental factors affected urban park more than forest region.Two predictive models for the NAI concentrations in the urban park and the forest region were established using the multiple regression methods.The proportions of days with prediction accuracy greater than 70%for the two models were 80.9%and 73.7%,respectively,indicating that two predictive models had good fitting effects and could provide meteorological services for forest health and healthy life.
air negative oxygen ionscharacteristic analysisinfluencing factorsprediction model
国家科技期刊平台
NSTL
万方数据
