Optimization of sampling conditions to minimize sampling errors of both PM 2.5 mass and its semi-volatile inorganic ion concentrations

ABSTRACTThe accurate measurement of PM2.5 and its inorganic matters (IMs) is crucial for compliance monitoring and understanding particle formation. However, semi-volatile IMs (SVIMs) like NH4+, NO3− and Cl− tend to evaporate from particles, causing sampling artifacts. The evaporation loss occurs due to many factors making the quantitative prediction difficult. This study aimed to investigate the evaporation loss of SVIMs in PM2.5 under different sampling conditions. In the field tests, when a normal single Teflon filter sampler (STF), which is like a Federal Reference Method (FRM) sampler, was used to sample PM2.5 at ambient conditions, a significant SVIM evaporation loss was observed, resulting in negative biases for total IMs (-25.68 ± 3.25%) and PM2.5 concentrations (-9.87 ± 4.27%). But if PM2.5 was sampled by a chilled Teflon filter sampler (CTF) at 4 0C following aerosol dehumidification so that relative humidity (RH) was controlled to within the 10-20% range (RHd), evaporation loss was minimized with a bias of < ±10% for both total IMs and PM2.5 based on the reference data. When RHd is below 10%, both IMs and PM2.5 are under-measured, but only PM2.5 is over-measured when RHd is >20%. A model considering predictable saturation ratios for NH4+, NO3− and Cl− under various pressure drop, temperature and RH conditions was developed to predict accurately the actual concentrations of PM2.5 and its SVIMs for the STF. Additionally, the ISORROPIA-II model predicted SVIMs effectively for the CTF. In summary, using the CTF at optimized sampling conditions can achieve accurate measurement of both SVIMs and PM2.5 concentrations simultaneously.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgementsThis work was supported by the Ministry of Science and Technology, Taiwan (contract MOST 111-2221-E-A49-057-MY3), the Ministry of Education, the Higher Education Sprout Project of National Yang Ming Chiao Tung University, and the Academic-Industry Research Hub of People and Earth (AIR HoPE).Disclosure statementThe authors report there are no competing interests to declare.