A Build-in Data Inversion Method To Retrieve Ultrafine Particle Size Distributions Based On The Principle Of Electrical Mobility

Particle size is an important parameter used to characterize the properties of aerosol particles,which can reflect the source of aerosol particles,physical and chemical properties and other important information.The study of ultrafine particle size distributions is of great significance to atmospheric partic les monitoring,emission control of mobile pollution sources,research on respiratory diseases,and semiconductor industry production.In this paper,the inversion method of particle size distributions for a portable ultrafine particle sizer(PUPS)was studied by theoretical model,numerical simulation and experimental verification.The PUPS is portable and cost-effective for the measurements of the ultrafine particles in polluted environments nearsource(e.g.,cities with high traffic density,freeways,airports or stationary combustion sources).The particle sizer is mainly composed of a unipolar charger,a plate differential mobility analyzer(DMA),and a Faraday cup electrometer(FCE).The classification efficiencies of the plate DMA are strongly dependent on the charging distribution of the unipolar charger,in which multiple charging is more significant than that for a bipolar charge r.To reduce the excessive overlap in the kernel function caused by multiple charging,a guidance method for selecting the classification voltages and operating parameters of the plate DMA is proposed with the help of MATLAB.Subsequently,a combination of the nonnegative least squares(NNLS)algorithm and post facto smoothing method has been employed to derive the discretized solution of the Fredholm integral equation of the first kind.In the end,the accuracy and stability of the proposed approach are tested under a range of particle size distribution scenarios and validated with the SMPS data.The synthesized data results show that for the unimodal and bimodal aerosol distributions,almost all of the relative errors are less than 20%,and the correlation coefficients are all greater than 0.98.Taking nonideal operating conditions(e.g.,cut-size mismatch)into consideration,relative errors are within 30%,and the correlation coefficients are all greater than0.92.The inversion algorithm can b e run on Cortex-M3,an ARM embedded chip for low-cost and low-power consumption applications.A good agreement between the SMPS and PUPS data was found,the particle size and concentration in peak are consistent,which can meet the monitoring needs of ultrafine particles.