Source Apportionment Of PM_2.5 In Urban Area Of A Certain City On The Loess Plateau

Fine particle(PM_2.5)is a major pollutant affecting air quality in cities all over the world in recent years.Airborne particles have adverse effects on human health,air pollution and global climate change.It plays a key role in controlling fine particles pollution and improving air quality to recognize the formation and apportion the sources of particles.The receptor model is a method that traces the source of particulate pollution through analyzing the chemical composition and physical characteristics of particulate matter collected from the receptor sites and emitted from pollution sources.In this paper,the sources of PM_2.5 were apportioned by several methods based on the data measured at sampling sites and source profiles in urban area of a certain city on the Loess Plateau.The 5 receptor points were set up in the study area with considering the environment function zone of the city.PM_2.5 was collected during the heating and non-heating periods with the medium-flow particulate matter sampler.The water-soluble ions and the elements were measured after sampling.PMF-CMB and PCA/MLR-CMB models were used to apportion the PM_2.5 sources in the area after the processing for rejecting abnormal data and then expanding all the rest data by the normal expansion method.CMB model was used to apportion the PM_2.5 sources contribution after the processing for culling abnormal data and then clustering the rest data as well.The influence of meteorological factors on the contribution of pollution sources is neglected in existing receptor model method,so there is a mismatch between the receptor and source profiles,they caused inaccuracies in results of receptor model.It is not clear to indicate the source position for without the contribution of the source from different area which can't be given by present receptor models even more.With considering the important of wind,atmospheric stability,mixed layer height and precipitation in the transmission of atmospheric pollutants,a new source apportionment method named PP-LSR-CMB method was established by combining of pollution probability,least square regression and CMB model in order to solve these problems.This method was applied to the source apportionment of PM_2.5 during the heating period at Site A.The reliability of results was verified by the analysis of measured PM_2.5.5 concentration with different wind direction and the distribution of pollution sources.The relative standard deviations?RSD?of standardized contributions apportioned by the four methods?PP-LSR-CMB,PMF-CMB,PCA/MLR-CMB,and CMB?were calculated to evaluate the accuracy of the four methods.The sequence of RSD was as follows:PP-LSR-CMB<PCA/MLR-CMB<PMF-CMB<CMB.The results show that the PP-LSR-CMB method has good diagnostic parameters in models.PP-LSR-CMB is superior to compound models?PMF-CMB and PCA/MLR-CMB?the performance of which is higher than single models like CMB in veracity.The long-range-transportation of PM_2.5 and PM₁₀ was studied by CWT methods after calculating trajectories by the HYSPLIT model in the area in 2015 and in different seasons.The concentration of pollution in monitoring sites are used for analysis without considering the impact of local emissions,and the effects of air mass height and wet deposition on air pollutants air mass carried during air mass transfer were neglected in existing CWT method.CWT method was improved to solve the above problems by the treatment of pollutant data and rejecting such trajectory nodes in this study.The potential source areas of PM_2.5 and PM₁₀0 were discovered by this improved method,and a pollution source area unrecognized by the existing method was identified.The WCWT calculated by this improved method showed a great dependence on location,which highlighted the location of the important pollutant source area.The results suggest that the potential source areas of PM_2.5 and PM₁₀ were consistent with the high-value regions of the distribution of AOD,the PM_2.5 concentration,and the anthropogenic emissions of PM_2.5 and PM₁₀.