Study On Variation Characteristics And Transmission Path Of PM_2.5 Pollution In The Atmosphere Of Harbin

This paper uses the ambient air automatic monitoring system to quantitatively analyze the monitoring data of different monitoring points in Harbin from 2011 to2020,and predict the change trend of the concentration of ambient air pollutants in Harbin from 2021 to 2025.According to the trends and characteristics analyzed and researched,the changing characteristics of ambient air pollutants and the changing laws of ambient air quality are sorted out.By comprehensively discussing the internal relationship between the concentration of major air pollutants in Harbin and various natural influencing factors,and using the obtained information to predict the urban ambient air quality in Harbin in the next five years,the changes in ambient air quality in Harbin are also obtained trend.Then,the backward trajectory model and the potential source contribution algorithm are used to analyze the transmission path of air pollution in Harbin in 2020.It provides scientific data support for the current situation of air pollution in Harbin and regional joint prevention and control,and provides a reasonable direction for the formulation and implementation of pollution control policies.The following conclusions were drawn:Analysis of time change angle and change trend of pollutant characteristics,PM_2.5in January,February and October-December are all above the 24-hour average standard line,the concentration range is 81?g/m~3-132?g/m~3,PM_2.5 The concentration contribution accounted for 68.60%of the whole year.The PM_2.5 concentration range from May to September was 22?g/m~3-29?g/m~3,and the concentration contribution rate only accounted for 17.28%of the whole year;The concentration of PM_2.5 varies greatly in different seasons,and the average concentration value in winter is significantly higher than that in other seasons.The peak concentration of PM_2.5 mainly occurs in winter,and decreases year by year in summer and autumn.Among them,the decrease is the most obvious in autumn and basically the same in spring.From the analysis of the year-by-year data of pollutant PM_2.5,the concentration value of pollutant PM_2.5 shows an annual trend.There is a decreasing trend,with an interval of 30?g/m~3-90?g/m~3.Since2014,the PM_2.5 concentration of pollutants has been lower than the annual average standard line.Obtained by quantitative analysis and test method,when n=10,the confidence level is 0.95,and when the significance level is 0.05,the critical value of?s is WP=0.648.The rank correlation coefficient?s of PM_2.5 is negative,indicating that its pollution concentration trend is decreasing year by year;and because|?s|2.5 is not obvious.And the ambient air quality in the ten years is in the range of 227 days to 310 days,accounting for 62.19%to 84.93%of the whole year.According to the analysis of grey correlation degree method and the construction of grey system model prediction analysis,the natural factors affecting the changes of PM_2.5 concentration in the air of Harbin are mainly air temperature,relative humidity,air pressure,and precipitation.0.6159>precipitation 0.6108>air temperature0.6065>sunshine duration 0.5489>wind speed 0.5476;the specific performance of each natural influencing factor and the change of PM_2.5 concentration is:there is a negative correlation between temperature and PM_2.5 concentration,the increase of average temperature,pollution The faster the diffusion of pollutants;the increase in precipitation and relative humidity,the corresponding decrease in the concentration of pollutants PM_2.5;the wind speed has a significant role in promoting the diffusion of PM_2.5;the correlation between air pressure and PM_2.5 concentration is greater,and the concentration With the decrease of air pressure,there is an upward trend;the duration of sunshine and its pollutants are negatively correlated,and the increase of sunshine duration is conducive to the diffusion of pollutants.The prediction model of PM_2.5 is x(t+1)=181.0772exp(-0.0384t),a=0.0384,b=184.5332;the gray system model test results show that the posterior difference ratio of the model is C=0.091?0.35,and the error The probability p=1.000?1.0 means that the model accuracy is first-class good;and the maximum relative error of the model is0.140,which is?0.2,so the model fitting effect meets the requirements.The changes in the concentration of pollutant PM_2.5 in the next five years will all show a downward trend.From 2021 to 2025,the PM_2.5 concentration will be lower than the national annual standard(35?g/m~3).Statistical analysis of airflow backward trajectory,cluster analysis and pollution source area analysis,the backward trajectory line at a height of 500 meters and a time of 48 hours is mainly from eastern Russia and eastern Inner Mongolia,accounting for43.95%,the largest proportion in eastern Inner Mongolia in spring,and western Russia in winter.The second is the summer and autumn airflow,and the largest proportion of the trajectories are from Heilongjiang Province;the 20-meter-high short-range airflow comes from the most air trajectories in Heilongjiang Province,accounting for 57.04%,and other provinces.The largest proportion is Jilin Province.The four-season airflow is most affected by the airflow in the province,followed by western Jilin,and finally eastern Inner Mongolia.The longest trajectory in spring and summer enters Harbin from the west,and the longest trajectory in autumn and winter enters Harbin from the southwest.The pollution of PM_2.5 in Harbin is relatively serious,and it is mainly transmitted over short distances.In addition to local pollution,the pollution source areas are Tongliao in Inner Mongolia,Songyuan in Jilin and Shuangcheng District in Harbin,which are the main routes in the southwest.This city is related,and in winter,it is related to Jilin Daan,Songyuan,Fuyu and this city.The identification of pollutants in Harbin by the PSCF model is close to the airflow trajectory obtained by the backward trajectory,and the main pollution direction is close to the direction of the airflow trajectory obtained after clustering.